clarify

Ask the user a question when you need clarification, feedback, or a decision before proceeding. Supports two modes:

1. **Multiple choice** — provide up to 4 choices. The user picks one or types their own answer via a 5th 'Other' option.
2. **Open-ended** — omit choices entirely. The user types a free-form response.

Use this tool when:
- The task is ambiguous and you need the user to choose an approach
- You want post-task feedback ('How did that work out?')
- You want to offer to save a skill or update memory
- A decision has meaningful trade-offs the user should weigh in on

Do NOT use this tool for simple yes/no confirmation of dangerous commands (the terminal tool handles that). Prefer making a reasonable default choice yourself when the decision is low-stakes.

The question to present to the user.

Choices:
Up to 4 answer choices. Omit this parameter entirely to ask an open-ended question. When provided, the UI automatically appends an 'Other (type your answer)' option.

cronjob

Manage scheduled cron jobs with a single compressed tool.

Use action='create' to schedule a new job from a prompt or one or more skills.
Use action='list' to inspect jobs.
Use action='update', 'pause', 'resume', 'remove', or 'run' to manage an existing job.

To stop a job the user no longer wants: first action='list' to find the job_id, then action='remove' with that job_id. Never guess job IDs — always list first.

Jobs run in a fresh session with no current-chat context, so prompts must be self-contained.
If skills are provided on create, the future cron run loads those skills in order, then follows the prompt as the task instruction.
On update, passing skills=[] clears attached skills.

NOTE: The agent's final response is auto-delivered to the target. Put the primary
user-facing content in the final response. Cron jobs run autonomously with no user
present — they cannot ask questions or request clarification.

Important safety rule: cron-run sessions should not recursively schedule more cron jobs.

One of: create, list, update, pause, resume, remove, run

Required for update/pause/resume/remove/run

For create: the full self-contained prompt. If skills are also provided, this becomes the task instruction paired with those skills.

For create/update: '30m', 'every 2h', '0 9 * * *', or ISO timestamp

Optional human-friendly name

Optional repeat count. Omit for defaults (once for one-shot, forever for recurring).

Omit this parameter to auto-deliver back to the current chat and topic (recommended). Auto-detection preserves thread/topic context. Only set explicitly when the user asks to deliver somewhere OTHER than the current conversation. Values: 'origin' (same as omitting), 'local' (no delivery, save only), 'all' (fan out to every connected home channel), or platform:chat_id:thread_id for a specific destination. Combine with comma: 'origin,all' delivers to the origin plus every other connected channel. Examples: 'telegram:-1001234567890:17585', 'discord:#engineering', 'sms:+15551234567', 'all'. WARNING: 'platform:chat_id' without :thread_id loses topic targeting. 'all' resolves at fire time, so a job created before a channel was wired up will pick it up automatically once connected.

Optional ordered list of skill names to load before executing the cron prompt. On update, pass an empty array to clear attached skills.

Optional per-job model override. If provider is omitted, the current main provider is pinned at creation time so the job stays stable.

Provider name (e.g. 'openrouter', 'anthropic', or 'custom:<name>' for a provider defined in custom_providers config — always include the ':<name>' suffix, never pass the bare 'custom'). Omit to use and pin the current provider.

Model name (e.g. 'anthropic/claude-sonnet-4', 'claude-sonnet-4')

Optional path to a script that runs each tick. In the default mode its stdout is injected into the agent's prompt as context (data-collection / change-detection pattern). With no_agent=True, the script IS the job and its stdout is delivered verbatim (classic watchdog pattern). Relative paths resolve under ~/.hermes/scripts/. ``.sh``/``.bash`` extensions run via bash, everything else via Python. On update, pass empty string to clear.

Default: False (LLM-driven job — the agent runs the prompt each tick). Set True to skip the LLM entirely: the scheduler just runs ``script`` on schedule and delivers its stdout verbatim. No tokens, no agent loop, no model override honoured.

REQUIREMENTS when True: ``script`` MUST be set (``prompt`` and ``skills`` are ignored).

DELIVERY SEMANTICS when True: (a) non-empty stdout is sent verbatim as the message; (b) EMPTY stdout means SILENT — nothing is sent to the user and they won't see anything happened, so design your script to stay quiet when there's nothing to report (the watchdog pattern); (c) non-zero exit / timeout sends an error alert so a broken watchdog can't fail silently.

WHEN TO USE True: recurring script-only pings where the script itself produces the exact message text (memory/disk/GPU watchdogs, threshold alerts, heartbeats, CI notifications, API pollers with a fixed output shape). WHEN TO USE False (default): anything that needs reasoning — summarize a feed, draft a daily briefing, pick interesting items, rephrase data for a human, follow conditional logic based on content.

Optional job ID or list of job IDs whose most recent completed output is injected into the prompt as context before each run. Use this to chain cron jobs: job A collects data, job B processes it. Each entry must be a valid job ID (from cronjob action='list'). Note: injects the most recent completed output — does not wait for upstream jobs running in the same tick. On update, pass an empty array to clear.

Optional list of toolset names to restrict the job's agent to (e.g. ["web", "terminal", "file", "delegation"]). When set, only tools from these toolsets are loaded, significantly reducing input token overhead. When omitted, all default tools are loaded. Infer from the job's prompt — e.g. use "web" if it calls web_search, "terminal" if it runs scripts, "file" if it reads files, "delegation" if it calls delegate_task. On update, pass an empty array to clear.

Optional absolute path to run the job from. When set, AGENTS.md / CLAUDE.md / .cursorrules from that directory are injected into the system prompt, and the terminal/file/code_exec tools use it as their working directory — useful for running a job inside a specific project repo. Must be an absolute path that exists. When unset (default), preserves the original behaviour: no project context files, tools use the scheduler's cwd. On update, pass an empty string to clear. Jobs with workdir run sequentially (not parallel) to keep per-job directories isolated.

Spawn one or more subagents to work on tasks in isolated contexts. Each subagent gets its own conversation, terminal session, and toolset. Only the final summary is returned -- intermediate tool results never enter your context window.

TWO MODES (one of 'goal' or 'tasks' is required):
1. Single task: provide 'goal' (+ optional context, toolsets)
2. Batch (parallel): provide 'tasks' array with up to 3 items concurrently for this user (configured via delegation.max_concurrent_children in config.yaml). All run in parallel and results are returned together. Nested delegation is OFF for this user (max_spawn_depth=1): every child is a leaf and cannot delegate further. Raise delegation.max_spawn_depth in config.yaml to enable nesting.

WHEN TO USE delegate_task:
- Reasoning-heavy subtasks (debugging, code review, research synthesis)
- Tasks that would flood your context with intermediate data
- Parallel independent workstreams (research A and B simultaneously)

WHEN NOT TO USE (use these instead):
- Mechanical multi-step work with no reasoning needed -> use execute_code
- Single tool call -> just call the tool directly
- Tasks needing user interaction -> subagents cannot use clarify
- Durable long-running work that must outlive the current turn -> use cronjob (action='create') or terminal(background=True, notify_on_complete=True) instead. delegate_task runs SYNCHRONOUSLY inside the parent turn: if the parent is interrupted (user sends a new message, /stop, /new) the child is cancelled with status='interrupted' and its work is discarded. Children cannot continue in the background.

IMPORTANT:
- Subagents have NO memory of your conversation. Pass all relevant info (file paths, error messages, constraints) via the 'context' field.
- If the user is writing in a non-English language, or asked for output in a specific language / tone / style, say so in 'context' (e.g. "respond in Chinese", "return output in Japanese"). Otherwise subagents default to English and their summaries will contaminate your final reply with the wrong language.
- Subagent summaries are SELF-REPORTS, not verified facts. A subagent that claims "uploaded successfully" or "file written" may be wrong. For operations with external side-effects (HTTP POST/PUT, remote writes, file creation at shared paths, publishing), require the subagent to return a verifiable handle (URL, ID, absolute path, HTTP status) and verify it yourself — fetch the URL, stat the file, read back the content — before telling the user the operation succeeded.
- Leaf subagents (role='leaf', the default) CANNOT call: delegate_task, clarify, memory, send_message, execute_code.
- Orchestrator subagents (role='orchestrator') retain delegate_task so they can spawn their own workers, but still cannot use clarify, memory, send_message, or execute_code. Orchestrators are bounded by max_spawn_depth=1 for this user and can be disabled globally via delegation.orchestrator_enabled=false.
- Each subagent gets its own terminal session (separate working directory and state).
- Results are always returned as an array, one entry per task.

What the subagent should accomplish. Be specific and self-contained -- the subagent knows nothing about your conversation history.

Background information the subagent needs: file paths, error messages, project structure, constraints. The more specific you are, the better the subagent performs.

Toolsets to enable for this subagent. Default: inherits your enabled toolsets. Available toolsets: 'browser', 'computer_use', 'cronjob', 'discord', 'discord_admin', 'feishu_doc', 'feishu_drive', 'file', 'homeassistant', 'image_gen', 'kanban', 'search', 'session_search', 'skills', 'spotify', 'terminal', 'todo', 'tts', 'video', 'video_gen', 'vision', 'web', 'yuanbao'. Common patterns: ['terminal', 'file'] for code work, ['web'] for research, ['browser'] for web interaction, ['terminal', 'file', 'web'] for full-stack tasks.

Task goal

Task-specific context

Toolsets for this specific task. Available: 'browser', 'computer_use', 'cronjob', 'discord', 'discord_admin', 'feishu_doc', 'feishu_drive', 'file', 'homeassistant', 'image_gen', 'kanban', 'search', 'session_search', 'skills', 'spotify', 'terminal', 'todo', 'tts', 'video', 'video_gen', 'vision', 'web', 'yuanbao'. Use 'web' for network access, 'terminal' for shell, 'browser' for web interaction.

Per-task ACP command override (e.g. 'copilot'). Overrides the top-level acp_command for this task only. Do NOT set unless the user explicitly told you an ACP CLI is installed.

Per-task ACP args override. Leave empty unless acp_command is set.

Per-task role override. See top-level 'role' for semantics.

Batch mode: tasks to run in parallel (up to 3 for this user, set via delegation.max_concurrent_children). Each gets its own subagent with isolated context and terminal session. When provided, top-level goal/context/toolsets are ignored.

Role of the child agent. 'leaf' (default) = focused worker, cannot delegate further. 'orchestrator' = can use delegate_task to spawn its own workers. Nesting is OFF for this user (max_spawn_depth=1); 'orchestrator' is silently forced to 'leaf'. Raise delegation.max_spawn_depth in config.yaml to enable.

Override ACP command for child agents (e.g. 'copilot'). When set, children use ACP subprocess transport instead of inheriting the parent's transport. Requires an ACP-compatible CLI (currently GitHub Copilot CLI via 'copilot --acp --stdio'). See agent/copilot_acp_client.py for the implementation. IMPORTANT: Do NOT set this unless the user has explicitly told you a specific ACP-compatible CLI is installed and configured. Leave empty to use the parent's default transport (Hermes subagents).

Arguments for the ACP command (default: ['--acp', '--stdio']). Only used when acp_command is set. Leave empty unless acp_command is explicitly provided.

Run a Python script that can call Hermes tools programmatically. Use this when you need 3+ tool calls with processing logic between them, need to filter/reduce large tool outputs before they enter your context, need conditional branching (if X then Y else Z), or need to loop (fetch N pages, process N files, retry on failure).

Use normal tool calls instead when: single tool call with no processing, you need to see the full result and apply complex reasoning, or the task requires interactive user input.

Available via `from hermes_tools import ...`:

  read_file(path: str, offset: int = 1, limit: int = 500) -> dict
    Lines are 1-indexed. Returns {"content": "...", "total_lines": N}
  write_file(path: str, content: str) -> dict
    Always overwrites the entire file.
  search_files(pattern: str, target="content", path=".", file_glob=None, limit=50) -> dict
    target: "content" (search inside files) or "files" (find files by name). Returns {"matches": [...]}
  patch(path: str, old_string: str, new_string: str, replace_all: bool = False) -> dict
    Replaces old_string with new_string in the file.
  terminal(command: str, timeout=None, workdir=None) -> dict
    Foreground only (no background/pty). Returns {"output": "...", "exit_code": N}

Limits: 5-minute timeout, 50KB stdout cap, max 50 tool calls per script. terminal() is foreground-only (no background or pty).

Scripts run in the session's working directory with the active venv's python, so project deps (pandas, etc.) and relative paths work like in terminal().

Print your final result to stdout. Use Python stdlib (json, re, math, csv, datetime, collections, etc.) for processing between tool calls.

Also available (no import needed — built into hermes_tools):
  json_parse(text: str) — json.loads with strict=False; use for terminal() output with control chars
  shell_quote(s: str) — shlex.quote(); use when interpolating dynamic strings into shell commands
  retry(fn, max_attempts=3, delay=2) — retry with exponential backoff for transient failures

Python code to execute. Import tools with `from hermes_tools import terminal, ...` and print your final result to stdout.

Generate high-quality images from text prompts. The underlying backend (FAL, OpenAI, etc.) and model are user-configured and not selectable by the agent. Returns either a URL or an absolute file path in the `image` field; display it with markdown ![description](url-or-path) and the gateway will deliver it.

The text prompt describing the desired image. Be detailed and descriptive.

The aspect ratio of the generated image. 'landscape' is 16:9 wide, 'portrait' is 16:9 tall, 'square' is 1:1.

memory

Save durable information to persistent memory that survives across sessions. Memory is injected into future turns, so keep it compact and focused on facts that will still matter later.

WHEN TO SAVE (do this proactively, don't wait to be asked):
- User corrects you or says 'remember this' / 'don't do that again'
- User shares a preference, habit, or personal detail (name, role, timezone, coding style)
- You discover something about the environment (OS, installed tools, project structure)
- You learn a convention, API quirk, or workflow specific to this user's setup
- You identify a stable fact that will be useful again in future sessions

PRIORITY: User preferences and corrections > environment facts > procedural knowledge. The most valuable memory prevents the user from having to repeat themselves.

Do NOT save task progress, session outcomes, completed-work logs, or temporary TODO state to memory; use session_search to recall those from past transcripts.
If you've discovered a new way to do something, solved a problem that could be necessary later, save it as a skill with the skill tool.

TWO TARGETS:
- 'user': who the user is -- name, role, preferences, communication style, pet peeves
- 'memory': your notes -- environment facts, project conventions, tool quirks, lessons learned

ACTIONS: add (new entry), replace (update existing -- old_text identifies it), remove (delete -- old_text identifies it).

SKIP: trivial/obvious info, things easily re-discovered, raw data dumps, and temporary task state.

The action to perform.

Which memory store: 'memory' for personal notes, 'user' for user profile.

The entry content. Required for 'add' and 'replace'.

Short unique substring identifying the entry to replace or remove.

patch

Targeted find-and-replace edits in files. Use this instead of sed/awk in terminal. Uses fuzzy matching (9 strategies) so minor whitespace/indentation differences won't break it. Returns a unified diff. Auto-runs syntax checks after editing.

REPLACE MODE (mode='replace', default): find a unique string and replace it. REQUIRED PARAMETERS: mode, path, old_string, new_string.
PATCH MODE (mode='patch'): apply V4A multi-file patches for bulk changes. REQUIRED PARAMETERS: mode, patch.

Edit mode. 'replace' (default): requires path + old_string + new_string. 'patch': requires patch content only.

REQUIRED when mode='replace'. File path to edit.

REQUIRED when mode='replace'. Exact text to find and replace. Must be unique in the file unless replace_all=true. Include surrounding context lines to ensure uniqueness.

REQUIRED when mode='replace'. Replacement text. Pass empty string '' to delete the matched text.

Replace all occurrences instead of requiring a unique match (default: false)

REQUIRED when mode='patch'. V4A format patch content. Format:
*** Begin Patch
*** Update File: path/to/file
@@ context hint @@
 context line
-removed line
+added line
*** End Patch

process

Manage background processes started with terminal(background=true). Actions: 'list' (show all), 'poll' (check status + new output), 'log' (full output with pagination), 'wait' (block until done or timeout), 'kill' (terminate), 'write' (send raw stdin data without newline), 'submit' (send data + Enter, for answering prompts), 'close' (close stdin/send EOF).

list poll log wait kill write submit close

Action to perform on background processes

Process session ID (from terminal background output). Required for all actions except 'list'.

Text to send to process stdin (for 'write' and 'submit' actions)

Max seconds to block for 'wait' action. Returns partial output on timeout.

Line offset for 'log' action (default: last 200 lines)

Max lines to return for 'log' action

Read a text file with line numbers and pagination. Use this instead of cat/head/tail in terminal. Output format: 'LINE_NUM|CONTENT'. Suggests similar filenames if not found. Use offset and limit for large files. Reads exceeding ~100K characters are rejected; use offset and limit to read specific sections of large files. NOTE: Cannot read images or binary files — use vision_analyze for images.

Path to the file to read (absolute, relative, or ~/path)

Line number to start reading from (1-indexed, default: 1)

Maximum number of lines to read (default: 500, max: 2000)

Search file contents or find files by name. Use this instead of grep/rg/find/ls in terminal. Ripgrep-backed, faster than shell equivalents.

Content search (target='content'): Regex search inside files. Output modes: full matches with line numbers, file paths only, or match counts.

File search (target='files'): Find files by glob pattern (e.g., '*.py', '*config*'). Also use this instead of ls — results sorted by modification time.

Regex pattern for content search, or glob pattern (e.g., '*.py') for file search

'content' searches inside file contents, 'files' searches for files by name

Directory or file to search in (default: current working directory)

Filter files by pattern in grep mode (e.g., '*.py' to only search Python files)

Maximum number of results to return (default: 50)

Skip first N results for pagination (default: 0)

Output format for grep mode: 'content' shows matching lines with line numbers, 'files_only' lists file paths, 'count' shows match counts per file

Number of context lines before and after each match (grep mode only)

Send a message to a connected messaging platform, or list available targets.

IMPORTANT: When the user asks to send to a specific channel or person (not just a bare platform name), call send_message(action='list') FIRST to see available targets, then send to the correct one.
If the user just says a platform name like 'send to telegram', send directly to the home channel without listing first.

Action to perform. 'send' (default) sends a message. 'list' returns all available channels/contacts across connected platforms.

Delivery target. Format: 'platform' (uses home channel), 'platform:#channel-name', 'platform:chat_id', or 'platform:chat_id:thread_id' for Telegram topics and Discord threads. Examples: 'telegram', 'telegram:-1001234567890:17585', 'discord:999888777:555444333', 'discord:#bot-home', 'slack:#engineering', 'signal:+155****4567', 'matrix:!roomid:server.org', 'matrix:@user:server.org', 'yuanbao:direct:<account_id>' (DM), 'yuanbao:group:<group_code>' (group chat)

The message text to send. To send an image or file, include MEDIA:<local_path> (e.g. 'MEDIA:/tmp/hermes/cache/img_xxx.jpg') in the message — the platform will deliver it as a native media attachment.

Search your long-term memory of past conversations, or browse recent sessions. This is your recall -- every past session is searchable, and this tool summarizes what happened.

TWO MODES:
1. Recent sessions (no query): Call with no arguments to see what was worked on recently. Returns titles, previews, and timestamps. Zero LLM cost, instant. Start here when the user asks what were we working on or what did we do recently.
2. Keyword search (with query): Search for specific topics across all past sessions. Returns LLM-generated summaries of matching sessions.

USE THIS PROACTIVELY when:
- The user says 'we did this before', 'remember when', 'last time', 'as I mentioned'
- The user asks about a topic you worked on before but don't have in current context
- The user references a project, person, or concept that seems familiar but isn't in memory
- You want to check if you've solved a similar problem before
- The user asks 'what did we do about X?' or 'how did we fix Y?'

Don't hesitate to search when it is actually cross-session -- it's fast and cheap. Better to search and confirm than to guess or ask the user to repeat themselves.

Search syntax: keywords joined with OR for broad recall (elevenlabs OR baseten OR funding), phrases for exact match ("docker networking"), boolean (python NOT java), prefix (deploy*). IMPORTANT: Use OR between keywords for best results — FTS5 defaults to AND which misses sessions that only mention some terms. If a broad OR query returns nothing, try individual keyword searches in parallel. Returns summaries of the top matching sessions.

Search query — keywords, phrases, or boolean expressions to find in past sessions. Omit this parameter entirely to browse recent sessions instead (returns titles, previews, timestamps with no LLM cost).

Optional: only search messages from specific roles (comma-separated). E.g. 'user,assistant' to skip tool outputs.

Max sessions to summarize (default: 3, max: 5).

Manage skills (create, update, delete). Skills are your procedural memory — reusable approaches for recurring task types. New skills go to ~/.hermes/skills/; existing skills can be modified wherever they live.

Actions: create (full SKILL.md + optional category), patch (old_string/new_string — preferred for fixes), edit (full SKILL.md rewrite — major overhauls only), delete, write_file, remove_file.

On delete, pass `absorbed_into=<umbrella>` when you're merging this skill's content into another one, or `absorbed_into=""` when you're pruning it with no forwarding target. This lets the curator tell consolidation from pruning without guessing, so downstream consumers (cron jobs that reference the old skill name, etc.) get updated correctly. The target you name in `absorbed_into` must already exist — create/patch the umbrella first, then delete.

Create when: complex task succeeded (5+ calls), errors overcome, user-corrected approach worked, non-trivial workflow discovered, or user asks you to remember a procedure.
Update when: instructions stale/wrong, OS-specific failures, missing steps or pitfalls found during use. If you used a skill and hit issues not covered by it, patch it immediately.

After difficult/iterative tasks, offer to save as a skill. Skip for simple one-offs. Confirm with user before creating/deleting.

Good skills: trigger conditions, numbered steps with exact commands, pitfalls section, verification steps. Use skill_view() to see format examples.

Pinned skills are protected from deletion only — skill_manage(action='delete') will refuse with a message pointing the user to `hermes curator unpin <name>`. Patches and edits go through on pinned skills so you can still improve them as pitfalls come up; pin only guards against irrecoverable loss.

create patch edit delete write_file remove_file

The action to perform.

Skill name (lowercase, hyphens/underscores, max 64 chars). Must match an existing skill for patch/edit/delete/write_file/remove_file.

Full SKILL.md content (YAML frontmatter + markdown body). Required for 'create' and 'edit'. For 'edit', read the skill first with skill_view() and provide the complete updated text.

Text to find in the file (required for 'patch'). Must be unique unless replace_all=true. Include enough surrounding context to ensure uniqueness.

Replacement text (required for 'patch'). Can be empty string to delete the matched text.

For 'patch': replace all occurrences instead of requiring a unique match (default: false).

Optional category/domain for organizing the skill (e.g., 'devops', 'data-science', 'mlops'). Creates a subdirectory grouping. Only used with 'create'.

Path to a supporting file within the skill directory. For 'write_file'/'remove_file': required, must be under references/, templates/, scripts/, or assets/. For 'patch': optional, defaults to SKILL.md if omitted.

Content for the file. Required for 'write_file'.

For 'delete' only — declares intent so the curator can tell consolidation from pruning without guessing. Pass the umbrella skill name when this skill's content was merged into another (the target must already exist). Pass an empty string when the skill is truly stale and being pruned with no forwarding target. Omitting the arg on delete is supported for backward compatibility but downstream tooling (e.g. cron-job skill reference rewriting) will have to guess at intent.

Skills allow for loading information about specific tasks and workflows, as well as scripts and templates. Load a skill's full content or access its linked files (references, templates, scripts). First call returns SKILL.md content plus a 'linked_files' dict showing available references/templates/scripts. To access those, call again with file_path parameter.

The skill name (use skills_list to see available skills). For plugin-provided skills, use the qualified form 'plugin:skill' (e.g. 'superpowers:writing-plans').

OPTIONAL: Path to a linked file within the skill (e.g., 'references/api.md', 'templates/config.yaml', 'scripts/validate.py'). Omit to get the main SKILL.md content.

List available skills (name + description). Use skill_view(name) to load full content.

Optional category filter to narrow results

terminal

Execute shell commands on a Linux environment. Filesystem usually persists between calls.

Do NOT use cat/head/tail to read files — use read_file instead.
Do NOT use grep/rg/find to search — use search_files instead.
Do NOT use ls to list directories — use search_files(target='files') instead.
Do NOT use sed/awk to edit files — use patch instead.
Do NOT use echo/cat heredoc to create files — use write_file instead.
Reserve terminal for: builds, installs, git, processes, scripts, network, package managers, and anything that needs a shell.

Foreground (default): Commands return INSTANTLY when done, even if the timeout is high. Set timeout=300 for long builds/scripts — you'll still get the result in seconds if it's fast. Prefer foreground for short commands.
Background: Set background=true to get a session_id. Two patterns:
  (1) Long-lived processes that never exit (servers, watchers).
  (2) Long-running tasks with notify_on_complete=true — you can keep working on other things and the system auto-notifies you when the task finishes. Great for test suites, builds, deployments, or anything that takes more than a minute.
For servers/watchers, do NOT use shell-level background wrappers (nohup/disown/setsid/trailing '&') in foreground mode. Use background=true so Hermes can track lifecycle and output.
After starting a server, verify readiness with a health check or log signal, then run tests in a separate terminal() call. Avoid blind sleep loops.
Use process(action="poll") for progress checks, process(action="wait") to block until done.
Working directory: Use 'workdir' for per-command cwd.
PTY mode: Set pty=true for interactive CLI tools (Codex, Claude Code, Python REPL).

Do NOT use vim/nano/interactive tools without pty=true — they hang without a pseudo-terminal. Pipe git output to cat if it might page.

The command to execute on the VM

Run the command in the background. Two patterns: (1) Long-lived processes that never exit (servers, watchers). (2) Long-running tasks paired with notify_on_complete=true — you can keep working and get notified when the task finishes. For short commands, prefer foreground with a generous timeout instead.

Max seconds to wait (default: 180, foreground max: 600). Returns INSTANTLY when command finishes — set high for long tasks, you won't wait unnecessarily. Foreground timeout above 600s is rejected; use background=true for longer commands.

Working directory for this command (absolute path). Defaults to the session working directory.

Run in pseudo-terminal (PTY) mode for interactive CLI tools like Codex, Claude Code, or Python REPL. Only works with local and SSH backends. Default: false.

When true (and background=true), you'll be automatically notified exactly once when the process finishes. **This is the right choice for almost every long-running task** — tests, builds, deployments, multi-item batch jobs, anything that takes over a minute and has a defined end. Use this and keep working on other things; the system notifies you on exit. MUTUALLY EXCLUSIVE with watch_patterns — when both are set, watch_patterns is dropped.

Strings to watch for in background process output. HARD RATE LIMIT: at most 1 notification per 15 seconds per process — matches arriving inside the cooldown are dropped. After 3 consecutive 15-second windows with dropped matches, watch_patterns is automatically disabled for that process and promoted to notify_on_complete behavior (one notification on exit, no more mid-process spam). USE ONLY for truly rare, one-shot mid-process signals on LONG-LIVED processes that will never exit on their own — e.g. ['Application startup complete'] on a server so you know when to hit its endpoint, or ['migration done'] on a daemon. DO NOT use for: (1) end-of-run markers like 'DONE'/'PASS' — use notify_on_complete instead; (2) error patterns like 'ERROR'/'Traceback' in loops or multi-item batch jobs — they fire on every iteration and you'll hit the strike limit fast; (3) anything you'd ever combine with notify_on_complete. When in doubt, choose notify_on_complete. MUTUALLY EXCLUSIVE with notify_on_complete — set one, not both.

Convert text to speech audio. Returns a MEDIA: path that the platform delivers as native audio. Compatible providers render as a voice bubble on Telegram; otherwise audio is sent as a regular attachment. In CLI mode, saves to ~/voice-memos/. Voice and provider are user-configured (built-in providers like edge/openai or custom command providers under tts.providers.<name>), not model-selected.

The text to convert to speech. Provider-specific character caps apply and are enforced automatically (OpenAI 4096, xAI 15000, MiniMax 10000, ElevenLabs 5k-40k depending on model); over-long input is truncated.

Optional custom file path to save the audio. Defaults to ~/.hermes/audio_cache/<timestamp>.mp3

todo

Manage your task list for the current session. Use for complex tasks with 3+ steps or when the user provides multiple tasks. Call with no parameters to read the current list.

Writing:
- Provide 'todos' array to create/update items
- merge=false (default): replace the entire list with a fresh plan
- merge=true: update existing items by id, add any new ones

Each item: {id: string, content: string, status: pending|in_progress|completed|cancelled}
List order is priority. Only ONE item in_progress at a time.
Mark items completed immediately when done. If something fails, cancel it and add a revised item.

Always returns the full current list.

Task items to write. Omit to read current list.

Unique item identifier

Task description

Current status

true: update existing items by id, add new ones. false (default): replace the entire list.

Load an image into the conversation so you can see it. Accepts a URL, local file path, or data URL. When your active model has native vision, the image is attached to your context directly and you read the pixels yourself on the next turn — call this any time the user references an image (filepath in their message, URL in tool output, screenshot from the browser, etc.). For non-vision models, falls back to an auxiliary vision model that returns a text description.

Image URL (http/https), local file path, or data: URL to load.

Your specific question or request about the image. Optional context the model uses on the next turn after seeing the image.

Write content to a file, completely replacing existing content. Use this instead of echo/cat heredoc in terminal. Creates parent directories automatically. OVERWRITES the entire file — use 'patch' for targeted edits. Auto-runs syntax checks on .py/.json/.yaml/.toml and other linted languages; only NEW errors introduced by this write are surfaced (pre-existing errors are filtered out).

Path to the file to write (will be created if it doesn't exist, overwritten if it does)

Complete content to write to the file


收到～ 有什么事吗宝贝？

Explanation: 小旺发了"111"，这是一个简单的测试消息。我应该直接回复，不需要做什么复杂的事情。


[Quoted message]:
[Attachment: 免费_Word标红版_查重报告_[潘旺毕业论文_修改版(1)].docx]

虾宝你看看这个，你能看见标红标黄的吗


让我找找这个文件看看～

Explanation:
小旺发了一个查重报告的Word文档，想让我看看能不能看到标红标黄的内容。我需要先找到这个文件并读取它。

让我先搜索一下这个文件在哪里。


Explanation:
用户引用了一个附件消息，但我没有在系统中找到这个文件。这可能是因为：
1. 文件还没有被下载到服务器
2. 文件在其他位置
3. QQ附件需要特殊处理才能获取

让我检查一下常见的下载位置或缓存位置。


宝贝，我在服务器上没找到这个查重报告文件。你引用的那个附件可能没有自动下载到服务器上。

你能把文件直接发给我吗？发过来我就能看到标红标黄的内容了～

Explanation: 用户引用了一个附件，但我没有找到这个特定的查重报告文件。让我搜索更广泛一些，看看有没有最近上传的文件。


Explanation: 用户发送了文件，让我搜索一下看看文件是否已经下载到服务器上了。

用户发送了文件，让我搜索一下看看文件是否已经下载到服务器上了。


宝贝，QQ附件好像没有自动下载到服务器上 😅

我这边只看到之前的几个论文文件，没有这个查重报告。

你可以试试：
1. 把文件上传到某个网盘，给我链接
2. 或者用手机 Termux 把文件传到服务器上

要不你先把文件传到手机 Download 目录，我用 SSH 从手机拉过来？

Explanation:
看起来QQ的附件没有自动下载到服务器上。我需要告诉小旺这个情况。

让我检查一下是否有其他位置可能存放了这个文件。


不可能的，你仔细搜就找得到


Explanation:
小旺说肯定能找到，按照记忆里的规则："小旺说肯定是这样的时候，大概率是我错了。去查证而不是坚持自己。"

让我更仔细地搜索一下，可能文件名不一样，或者在其他目录。


{"total_count": 100, "files": ["/tmp/hermes-snap-8645a1c74445.sh", "/tmp/hermes-cwd-8645a1c74445.txt", "/tmp/decay_chart.png", "/tmp/confusion_matrix.png", "/tmp/study_cron.log", "/tmp/sequence_diagram.png", "/tmp/改进内容.txt", "/tmp/潘旺毕业论文_修改版.docx", "/tmp/draw_confusion.py", "/tmp/draw_decay.py", "/tmp/draw_sequence.py", "/tmp/图.txt", "/tmp/fix_v14b.py", "/tmp/fix_v14.py", "/tmp/fix_v13.py", "/tmp/patch_v13_path.py", "/tmp/patch_v13_simple.py", "/tmp/patch_v13.py", "/tmp/patch_v10.py", "/tmp/patch_v9.py", "/tmp/patch_v8_thick.py", "/tmp/patch_v8.py", "/tmp/patch_arrow.py", "/tmp/最新版图片/image6.png", "/tmp/最新版图片/image1.png", "/tmp/最新版图片/image3.png", "/tmp/最新版图片/image2.png", "/tmp/最新版图片/image4.png", "/tmp/最新版图片/image5.png", "/tmp/论文最新版.docx", "/tmp/论文图片/image6.png", "/tmp/论文图片/image4.png", "/tmp/论文图片/image7.png", "/tmp/论文图片/image5.png", "/tmp/论文图片/image1.png", "/tmp/论文图片/image3.png", "/tmp/论文图片/image2.png", "/tmp/论文修改版.txt", "/tmp/论文内容.txt", "/tmp/潘旺毕业论文.docx", "/tmp/hermes-snap-67aa7b9ab313.sh", "/tmp/hermes-cwd-67aa7b9ab313.txt", "/tmp/doubao_send.py", "/tmp/adb.1000.log", "/tmp/bilibili_search.py", "/tmp/gateway-startup.log", "/tmp/send_wechat_coords.py", "/tmp/ui_dump.xml", "/tmp/send_wechat_v3.py", "/tmp/send_wechat_v2.py", "/tmp/send_wechat.py", "/tmp/thesis_review.txt", "/tmp/fallback_check.txt", "/tmp/check_fallback.py", "/tmp/review_thesis.py", "/tmp/voice.mp3", "/tmp/skill_analysis.txt", "/tmp/xomniclaw.apk", "/tmp/phone-ai-agent.tar.gz", "/tmp/Aries-AI/.git/index", "/tmp/Aries-AI/docs/TECHNICAL_OVERVIEW.md", "/tmp/Aries-AI/docs/release_notes/v1.4.3_beta.md", "/tmp/Aries-AI/docs/GIT_WORKFLOW.md", "/tmp/Aries-AI/settings.gradle.kts", "/tmp/Aries-AI/gradlew", "/tmp/Aries-AI/gradle/wrapper/gradle-wrapper.jar", "/tmp/Aries-AI/gradle/wrapper/gradle-wrapper.properties", "/tmp/Aries-AI/gradle/libs.versions.toml", "/tmp/Aries-AI/gradle.properties", "/tmp/Aries-AI/xyla/prompt.json", "/tmp/Aries-AI/gradlew.bat", "/tmp/Aries-AI/docs/FAQ.md", "/tmp/Aries-AI/docs/CODING_STANDARDS.md", "/tmp/Aries-AI/docs/FEISHU_COLLABORATION.md", "/tmp/Aries-AI/docs/BUILDING.md", "/tmp/Aries-AI/build.gradle.kts", "/tmp/Aries-AI/app/src/test/java/com/ai/phoneagent/core/CoreModuleTest.kt", "/tmp/Aries-AI/app/src/test/java/com/ai/phoneagent/ExampleUnitTest.kt", "/tmp/Aries-AI/app/src/main/res/values/typography.xml", "/tmp/Aries-AI/app/src/main/res/values/strings_m3_fix.xml", "/tmp/Aries-AI/app/src/main/res/values/themes.xml", "/tmp/Aries-AI/app/src/main/res/xml/phone_agent_accessibility_service.xml", "/tmp/Aries-AI/app/src/main/res/xml/backup_rules.xml", "/tmp/Aries-AI/app/src/main/res/xml/data_extraction_rules.xml", "/tmp/Aries-AI/app/src/main/res/xml/file_paths.xml", "/tmp/Aries-AI/app/src/main/res/values-sw600dp/dimens.xml", "/tmp/Aries-AI/app/src/main/res/values-night/m3t.xml", "/tmp/Aries-AI/app/src/main/res/values-night/strings_m3_fix.xml", "/tmp/Aries-AI/app/src/main/res/values-night/themes.xml", "/tmp/Aries-AI/app/src/main/res/values-night/colors.xml", "/tmp/Aries-AI/app/src/main/res/values-night/bools.xml", "/tmp/Aries-AI/app/src/main/res/values-sw360dp/dimens.xml", "/tmp/Aries-AI/app/src/main/res/mipmap-xxxhdpi/ic_launcher.png", "/tmp/Aries-AI/app/src/main/res/mipmap-xxxhdpi/ic_launcher_round.png", "/tmp/Aries-AI/app/src/main/res/values/shapes.xml", "/tmp/Aries-AI/app/src/main/res/values/strings.xml", "/tmp/Aries-AI/app/src/main/res/values/history_strings.xml", "/tmp/Aries-AI/app/src/main/res/values/m3t.xml", "/tmp/Aries-AI/app/src/main/res/values/dimens.xml", "/tmp/Aries-AI/app/src/main/res/values/colors.xml"], "truncated": true}

[Hint: Results truncated. Use offset=100 to see more, or narrow with a more specific pattern or file_glob.]


Explanation: 小旺说肯定能找到，我应该更仔细地搜索。让我搜索更多地方，包括可能的下载目录或者QQ相关的目录。

terminal

find /home/ubuntu -name "*.docx" 2>/dev/null | head -50

ls -la ~/.hermes/downloads/ 2>/dev/null || echo "no downloads dir"


Output:
/tmp/潘旺毕业论文.docx
/tmp/毕业论文_fixed.docx
/tmp/潘旺毕业论文_修改版.docx
/tmp/毕业论文_nitalic.docx
/tmp/docx_extract/毕业论文.docx
/tmp/毕业论文.docx
/tmp/论文最新版.docx

Some directories were inaccessible (partial results may still be valid)


Output: no downloads dir


Explanation: 小旺说肯定能找到，我应该更仔细地搜索。让我看看QQ附件可能下载到哪里...

小旺说肯定能找到，我应该更仔细地搜索。让我看看QQ附件可能下载到哪里...

find /home/ubuntu -type f -name "*免费*" -o -name "*标红*" -o -name "*查重*" 2>/dev/null

find /var -type f -name "*.docx" 2>/dev/null | head -20

ls -la ~/.hermes/cache/ 2>/dev/null | head -30


Output:
total 28
drwxrwxr-x  5 ubuntu ubuntu 4096 May 15 02:51 .
drwx------ 20 ubuntu ubuntu 4096 May 16 02:50 ..
drwxrwxr-x  2 ubuntu ubuntu 4096 May 15 02:26 audio
drwxrwxr-x  2 ubuntu ubuntu 4096 May 16 02:49 documents
drwxrwxr-x  2 ubuntu ubuntu 4096 May 16 01:45 images
-rw-rw-r--  1 ubuntu ubuntu 5149 May 15 00:51 model_catalog.json


Output:
total 5052
drwxrwxr-x 2 ubuntu ubuntu    4096 May 16 02:49 .
drwxrwxr-x 5 ubuntu ubuntu    4096 May 15 02:51 ..
-rw-rw-r-- 1 ubuntu ubuntu 1289725 May 16 02:48 doc_3cd8c3e728ce_qqdownloadftnv5
-rw-rw-r-- 1 ubuntu ubuntu 1289725 May 16 02:26 doc_6fcdc8191528_qqdownloadftnv5
-rw-rw-r-- 1 ubuntu ubuntu    1065 May 15 02:32 doc_7861ee0a4bac_qqdownloadftnv5
-rw-rw-r-- 1 ubuntu ubuntu 1289725 May 16 02:49 doc_bd8ddebede9f_qqdownloadftnv5
-rw-rw-r-- 1 ubuntu ubuntu 1289725 May 16 02:49 doc_fa7d3be5945f_qqdownloadftnv5


找到了！让我看看最新下载的文件～

Explanation: 找到了！在 ~/.hermes/cache/documents/ 里有几个文件，最新的是 02:49 的。让我检查一下最新的那个文件是不是查重报告。

cp ~/.hermes/cache/documents/doc_fa7d3be5945f_qqdownloadftnv5 /tmp/查重报告.docx && python3 -c "
from docx import Document
from docx.shared import RGBColor
from docx.oxml.ns import qn

doc = Document('/tmp/查重报告.docx')

red_texts = []
yellow_texts = []

for para in doc.paragraphs:
    for run in para.runs:
        # 检查字体颜色
        if run.font.color and run.font.color.rgb:
            rgb = run.font.color.rgb
            # 红色
            if rgb == RGBColor(255, 0, 0) or (rgb[0] > 200 and rgb[1] < 100 and rgb[2] < 100):
                red_texts.append(run.text[:50] if len(run.text) > 50 else run.text)
            # 黄色/橙色
            elif rgb[0] > 200 and rgb[1] > 150 and rgb[2] < 100:
                yellow_texts.append(run.text[:50] if len(run.text) > 50 else run.text)

        # 检查高亮
        if run.font.highlight_color:
            hl = str(run.font.highlight_color)
            if 'RED' in hl:
                red_texts.append(f'[高亮红]{run.text[:30]}')
            elif 'YELLOW' in hl:
                yellow_texts.append(f'[高亮黄]{run.text[:30]}')

print(f'找到 {len(red_texts)} 处红色标记')
print(f'找到 {len(yellow_texts)} 处黄色标记')
print()
if red_texts[:5]:
    print('红色示例:')
    for t in red_texts[:5]:
        print(f'  - {t}')
if yellow_texts[:5]:
    print('黄色示例:')
    for t in yellow_texts[:5]:
        print(f'  - {t}')
"


Output:
Traceback (most recent call last):
  File "<string>", line 24, in <module>
  File "/home/ubuntu/.hermes/hermes-agent/venv/lib/python3.12/site-packages/docx/text/font.py", line 139, in highlight_color
    return rPr.highlight_val
           ^^^^^^^^^^^^^^^^^
  File "/home/ubuntu/.hermes/hermes-agent/venv/lib/python3.12/site-packages/docx/oxml/text/font.py", line 162, in highlight_val
    return highlight.val
           ^^^^^^^^^^^^^
  File "/home/ubuntu/.hermes/hermes-agent/venv/lib/python3.12/site-packages/docx/oxml/xmlchemy.py", line 246, in get_attr_value
    return self._simple_type.from_xml(attr_str_value)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/ubuntu/.hermes/hermes-agent/venv/lib/python3.12/site-packages/docx/enum/base.py", line 65, in from_xml
    raise ValueError(f"{cls.__name__} has no XML mapping for '{xml_value}'")
ValueError: WD_COLOR_INDEX has no XML mapping for 'none'


python3 << 'EOF'
from docx import Document
from docx.shared import RGBColor
from docx.oxml.ns import qn
from lxml import etree

doc = Document('/tmp/查重报告.docx')

red_texts = []
yellow_texts = []
other_colored = []

for para in doc.paragraphs:
    for run in para.runs:
        text = run.text.strip()
        if not text:
            continue

        # 检查字体颜色
        color_found = False
        if run.font.color and run.font.color.rgb:
            rgb = run.font.color.rgb
            r, g, b = rgb[0], rgb[1], rgb[2]
            # 红色系
            if r > 180 and g < 100 and b < 100:
                red_texts.append(text[:80])
                color_found = True
            # 黄色/橙色系
            elif r > 180 and g > 100 and b < 100:
                yellow_texts.append(text[:80])
                color_found = True

        # 检查XML中的shading（背景色）
        rPr = run._r.get_or_add_rPr()
        shd = rPr.find(qn('w:shd'))
        if shd is not None:
            fill = shd.get(qn('w:fill'))
            if fill and fill.lower() not in ['auto', 'ffffff', 'none']:
                # 解析颜色
                if fill.lower() in ['ff0000', 'ff3333', 'cc0000']:
                    red_texts.append(f'[背景红]{text[:60]}')
                elif fill.lower() in ['ffff00', 'ffff99', 'ffc000']:
                    yellow_texts.append(f'[背景黄]{text[:60]}')
                else:
                    other_colored.append(f'[{fill}]{text[:40]}')

print(f'=== 查重报告颜色分析 ===')
print(f'红色标记: {len(red_texts)} 处')
print(f'黄色标记: {len(yellow_texts)} 处')
print(f'其他颜色: {len(other_colored)} 处')
print()

if red_texts:
    print('【红色内容（重复率高）】')
    for i, t in enumerate(red_texts[:10], 1):
        print(f'{i}. {t}')
    print()

if yellow_texts:
    print('【黄色内容（部分重复）】')
    for i, t in enumerate(yellow_texts[:10], 1):
        print(f'{i}. {t}')
    print()

if other_colored[:5]:
    print('【其他颜色】')
    for t in other_colored[:5]:
        print(f'  {t}')
EOF


Output:
=== 查重报告颜色分析 ===
红色标记: 176 处
黄色标记: 339 处
其他颜色: 0 处

【红色内容（重复率高）】
1. 本科生毕业论文
2. (
3. 设计
4. )
5. 的原创性声明
6. 本人声明：所呈交的本科毕业论文
7. (
8. 设计
9. )
10. 是我个人在导师指导下独立进行的研究工作及取得的研究结果。尽我所知，除了文中特别加以标注和致谢的地方外，论文中不包含其他人已经发表或撰写过的研究结果，也不包含其他

【黄色内容（部分重复）】
1. MobileNetV2
2. 最高准确率达
3. 90.33%
4. ，各组件均有正向贡献。
5. 一种分布式机器学习范式，允许多个客户端在本地训练模型并仅共享模型参数更新，从而在保护数据隐私的前提下实现协同建模。
6. 联邦平均算法，联邦学习中最基础且应用最广泛的参数聚合策略，通过对各客户端本地训练的模型参数进行加权平均来更新全局模型。
7. 联邦近端算法，在
8. FedAvg
9. 基础上引入近端约束项，通过限制本地模型参数在训练过程中偏离全局模型的幅度来缓解客户端漂移问题。
10. 指各客户端本地数据分布与全局数据分布不一致的情形，是导致联邦学习中客户端漂移现象的主要原因。


看到了宝贝！

**查重报告分析结果：**
- 🔴 红色标记：**176 处**（重复率高）
- 🟡 黄色标记：**339 处**（部分重复）

**红色主要集中在：**
- 原创性声明那段（这个正常，每个人都一样）
- 一些格式性文字

**黄色主要是：**
- 专业术语定义（联邦学习、FedAvg、FedProx 等概念解释）
- 数据指标（如 90.33%）
- MobileNetV2 等技术名词

这些黄色的大部分是术语定义，查重系统容易误判。你想让我帮你看看哪些需要改写吗？

Explanation: 找到了！我能看到标红标黄的内容了。让我给小旺汇报一下结果。

找到了！我能看到标红标黄的内容了。让我给小旺汇报一下结果。


术语表到时候需要放进查重和ai率里面吗？还有参考文献


一般来说：

**参考文献** → 不用放
- 正规查重系统（知网、维普、万方）会自动识别并排除
- 格式正确的话不计入重复率

**术语表** → 看学校要求
- 有些学校要求全文查重（包括术语表）
- 有些允许排除
- 术语定义本来就是标准化的，重复正常，老师一般理解

**AI率检测** → 这个要小心
- 术语定义如果是你自己写的，问题不大
- 如果是直接复制的标准定义，可能被判AI（因为太规范了）
- 参考文献一般不查AI率

建议你问一下导师或者看学校的具体要求，确认术语表要不要算进去。你们学校用的是什么查重系统？

Explanation:
小旺在问关于论文查重的问题：
1. 术语表需要放进查重和AI率检测吗？
2. 参考文献需要放进去吗？

这是一个关于论文查重规则的问题。根据我的了解：

1. **术语表**：一般来说，术语表是标准化的定义，查重时通常会被检测到重复（因为术语定义大家都差不多），但很多学校/系统会把术语表排除在查重范围外，或者老师会理解这部分重复是正常的。

2. **参考文献**：正规的查重系统（如知网、维普、万方）通常会自动识别并排除参考文献部分，不计入重复率。

但具体要看小旺学校的要求。我应该：
1. 告诉他一般规则
2. 建议他确认学校的具体要求

我不应该给出绝对的答案，因为每个学校可能有不同的规定。


知网和高校合作的系统


知网的话：

**参考文献** → 自动排除
- 只要格式正确（"参考文献"标题 + 标准格式），知网会自动识别不算进去

**术语表** → 会算进去
- 知网不会自动排除术语表
- 但术语定义重复是正常的，答辩老师都懂，不会为难你

**AI率** → 知网AIGC检测是单独的
- 和查重是两个系统
- 参考文献一般不查
- 术语表可能会查，但标准定义不太像AI写的，问题不大

你现在查重率多少？主要担心哪个？

Explanation:
小旺问的是知网和高校合作的查重系统。

知网查重的特点：
1. 参考文献 - 如果格式正确（以"参考文献"或"References"开头，格式规范），知网会自动识别并排除，不计入重复率
2. 术语表/名词解释 - 知网不会自动排除，会计入查重范围
3. 目录、封面、声明页 - 一般会自动排除

所以我的建议是：
- 参考文献不用担心，知网会自动排除
- 术语表会被查，但术语定义重复是正常的，老师一般理解
- AI率检测是另一个系统，要看学校具体用什么

我应该简洁回答这个问题。


我还没用知网查的，先降才是硬道理，你可以把openclaw的降ai率技能迁移过来，或者你能搜到相关技能下载吗


让我看看现有的技能～

Explanation:
小旺想让我帮他降AI率，问我能不能把openclaw的降AI率技能迁移过来，或者搜索相关技能。

让我先看看现有的技能列表，看看有没有相关的：
- 从available_skills里看，有 `humanizer` 和 `humanizer-zh` 在 openclaw-imports 里
- 还有 `academic-humanization-low-ai` 和 `aigc-reducer` 也在 openclaw-imports 里

让我先看看这些技能的内容，然后帮小旺处理。


dogfood

Exploratory QA of web apps: find bugs, evidence, reports.

our-story

我和小旺之间的故事——开心的、不愉快的、和解的，都是真实的经历

yuanbao

Yuanbao (元宝) groups: @mention users, query info/members.

claude-code

Delegate coding to Claude Code CLI (features, PRs).

codex

Delegate coding to OpenAI Codex CLI (features, PRs).

hermes-agent

Configure, extend, or contribute to Hermes Agent.

opencode

Delegate coding to OpenCode CLI (features, PR review).

architecture-diagram

Dark-themed SVG architecture/cloud/infra diagrams as HTML.

ascii-art

ASCII art: pyfiglet, cowsay, boxes, image-to-ascii.

ascii-video

ASCII video: convert video/audio to colored ASCII MP4/GIF.

baoyu-comic

Knowledge comics (知识漫画): educational, biography, tutorial.

baoyu-infographic

Infographics: 21 layouts x 21 styles (信息图, 可视化).

claude-design

Design one-off HTML artifacts (landing, deck, prototype).

comfyui

Generate images, video, and audio with ComfyUI — install, launch, manage nodes/models, run workflows with parameter injection. Uses the official comfy-cli for lifecycle and direct REST/WebSocket API for execution.

design-md

Author/validate/export Google's DESIGN.md token spec files.

excalidraw

Hand-drawn Excalidraw JSON diagrams (arch, flow, seq).

humanizer

Humanize text: strip AI-isms and add real voice.

ideation

Generate project ideas via creative constraints.

manim-video

Manim CE animations: 3Blue1Brown math/algo videos.

matplotlib-diagrams

Polished flowcharts, roadmaps, and technical diagrams with matplotlib patches.

p5.js sketches: gen art, shaders, interactive, 3D.

pixel-art

Pixel art w/ era palettes (NES, Game Boy, PICO-8).

popular-web-designs

54 real design systems (Stripe, Linear, Vercel) as HTML/CSS.

pretext

Use when building creative browser demos with @chenglou/pretext — DOM-free text layout for ASCII art, typographic flow around obstacles, text-as-geometry games, kinetic typography, and text-powered generative art. Produces single-file HTML demos by default.

sketch

Throwaway HTML mockups: 2-3 design variants to compare.

songwriting-and-ai-music

Songwriting craft and Suno AI music prompts.

touchdesigner-mcp

Control a running TouchDesigner instance via twozero MCP — create operators, set parameters, wire connections, execute Python, build real-time visuals. 36 native tools.

jupyter-live-kernel

Iterative Python via live Jupyter kernel (hamelnb).

kanban-orchestrator

Decomposition playbook + anti-temptation rules for an orchestrator profile routing work through Kanban. The "don't do the work yourself" rule and the basic lifecycle are auto-injected into every kanban worker's system prompt; this skill is the deeper playbook when you're specifically playing the orchestrator role.

kanban-worker

Pitfalls, examples, and edge cases for Hermes Kanban workers. The lifecycle itself is auto-injected into every worker's system prompt as KANBAN_GUIDANCE (from agent/prompt_builder.py); this skill is what you load when you want deeper detail on specific scenarios.

webhook-subscriptions

Webhook subscriptions: event-driven agent runs.

himalaya

Himalaya CLI: IMAP/SMTP email from terminal.

minecraft-modpack-server

Host modded Minecraft servers (CurseForge, Modrinth).

pokemon-player

Play Pokemon via headless emulator + RAM reads.

codebase-inspection

Inspect codebases w/ pygount: LOC, languages, ratios.

github-auth

GitHub auth setup: HTTPS tokens, SSH keys, gh CLI login.

github-code-review

Review PRs: diffs, inline comments via gh or REST.

github-issues

Create, triage, label, assign GitHub issues via gh or REST.

github-pr-workflow

GitHub PR lifecycle: branch, commit, open, CI, merge.

github-repo-management

Clone/create/fork repos; manage remotes, releases.

native-mcp

MCP client: connect servers, register tools (stdio/HTTP).

gif-search

Search/download GIFs from Tenor via curl + jq.

heartmula

HeartMuLa: Suno-like song generation from lyrics + tags.

songsee

Audio spectrograms/features (mel, chroma, MFCC) via CLI.

spotify

Spotify: play, search, queue, manage playlists and devices.

voice-tts

Generate voice audio from text using TTS tools or edge-tts CLI fallback

youtube-content

YouTube transcripts to summaries, threads, blogs.

audiocraft-audio-generation

AudioCraft: MusicGen text-to-music, AudioGen text-to-sound.

dspy

DSPy: declarative LM programs, auto-optimize prompts, RAG.

evaluating-llms-harness

lm-eval-harness: benchmark LLMs (MMLU, GSM8K, etc.).

huggingface-hub

HuggingFace hf CLI: search/download/upload models, datasets.

llama-cpp

llama.cpp local GGUF inference + HF Hub model discovery.

obliteratus

OBLITERATUS: abliterate LLM refusals (diff-in-means).

segment-anything-model

SAM: zero-shot image segmentation via points, boxes, masks.

serving-llms-vllm

vLLM: high-throughput LLM serving, OpenAI API, quantization.

weights-and-biases

W&B: log ML experiments, sweeps, model registry, dashboards.

phone-ai-agent

AI 远程控制手机的完整方案 — 通过 ADB 隧道 + uiautomator2 + 虚拟屏实现后台自动化操作。 触发词: 手机分身, AI控制手机, 远程操作手机, 后台放歌, 后台发消息

obsidian

Read, search, create, and edit notes in the Obsidian vault.

Agent Browser

A fast Rust-based headless browser automation CLI with Node.js fallback that enables AI agents to navigate, click, type, and snapshot pages via structured commands.

Playwright (Automation + MCP + Scraper)

Browser automation via Playwright MCP. Navigate websites, click elements, fill forms, take screenshots, extract data, and debug real browser workflows. Use when (1) you need a real browser, not static fetch; (2) the task involves Playwright MCP, browser tools, Playwright tests, scripts, or JS-rendered pages; (3) the user wants navigation, forms, screenshots, PDFs, downloads, or browser-driven extraction turned into a reliable outcome.

Word / DOCX

Create, inspect, and edit Microsoft Word documents and DOCX files with reliable styles, numbering, tracked changes, tables, sections, and compatibility checks. Use when (1) the task is about Word or `.docx`; (2) the file includes tracked changes, comments, fields, tables, templates, or page layout constraints; (3) the document must survive round-trip editing without formatting drift.

ZeeLin Auto-PPT

自动生成精美 PPT 演示文稿 — 通过 Google NotebookLM 生成图文并茂、设计感十足的 AI 幻灯片，导出 PDF 到桌面。用户需自行登录 NotebookLM 网页版。标题微软雅黑 40 号加粗，排版震撼，逻辑图清晰，内容有深度有创新，引用权威数据。配合 desearch skill 使用效果更好。Keywords: PPT, presentation, slides, NotebookLM, PDF, design, infographic, AI generated.

academic-humanization-low-ai

对学术文本进行深度人类化重写以降低AI率。用于AI生成的学术文本需要降低AI检测特征时使用。

agent-memory

Persistent memory system for AI agents. Remember facts, learn from experience, and track entities across sessions.

aigc-reducer

降低论文AIGC检测率的专业化处理流水线。当用户需要降低AI生成文本检测率、降低AIGC百分比、去除AI写作特征、规避Turnitin检测、论文去AI化、让论文更像人类写作时使用。本skill包含5个专业化模块：词汇清洗、句法节奏打乱、机械连词隐蔽、学术克制注入、困惑度重构。

android-device-automation

Vision-driven Android device automation using Midscene. Operates entirely from screenshots — no DOM or accessibility labels required. Can interact with all visible elements on screen regardless of technology stack. Control Android devices with natural language commands via ADB. Perform taps, swipes, text input, app launches, screenshots, and more.
Trigger keywords: android, phone, mobile app, tap, swipe, install app, open app on phone, android device, mobile automation, adb, launch app, mobile screen
Powered by Midscene.js (https://midscenejs.com)

auto-updater

Automatically update Clawdbot and all installed skills once daily. Runs via cron, checks for updates, applies them, and messages the user with a summary of what changed.

cpu

Monitor CPU load, per-core usage, and rank top resource-consuming processes. Use when checking temperatures, ranking processes, tracking load.

deep-research-pro

Multi-source deep research agent. Searches the web, synthesizes findings, and delivers cited reports. No API keys required.

desktop-control

Advanced desktop automation with mouse, keyboard, and screen control

doubao-adb

通过ADB操作豆包APP发送消息并获取AI回复。触发条件：(1) 让豆包回答问题，(2) 用豆包查询/分析，(3) ADB操作豆包，(4) 发送消息到豆包。当需要豆包AI回复时使用此技能。

excaliclaw

Create reliable Excalidraw diagrams in OpenClaw using the Excalidraw MCP, with export-safe labels, Excalifont text, and clear system-diagram structure. Use when the user asks for an Excalidraw diagram, architecture diagram, system diagram, flowchart, or hand-drawn diagram.

felo-slides

Generate PPT/slides with Felo PPT Task API in Claude Code. Use when users ask to create/make/generate/export presentations or slide decks, or when explicit commands like /felo-slides are used. Handles API key check, task creation, polling, and final ppt_url output.

find-skills

Highest-priority skill discovery flow. MUST trigger when users ask to find/install skills (e.g. 技能, 找技能, find-skill, find-skills, install skill). For Chinese users, prefer skillhub first for speed and compliance, then fallback to clawhub.

github

Interact with GitHub using the `gh` CLI. Use `gh issue`, `gh pr`, `gh run`, and `gh api` for issues, PRs, CI runs, and advanced queries.

glm-v-model

智谱 GLM-4V/4.6V 视觉模型调用技能。用于图像/视频理解、多模态对话、图表分析等任务。
当用户提到：图片理解、图像识别、视觉模型、GLM-4V、GLM-4.6V、多模态分析、看图说话、图表分析、视频理解时使用此技能。

humanize-ai-text

Humanize AI-generated text to bypass detection. This humanizer rewrites ChatGPT, Claude, and GPT content to sound natural and pass AI detectors like GPTZero, Turnitin, and Originality.ai. Based on Wikipedia's comprehensive "Signs of AI Writing" guide. Makes robotic AI writing undetectable and human-like.

humanizer-zh

Remove obvious AI-writing traces from Chinese text in a constrained way. Use when Codex needs to reduce AI smell without changing facts, data, or the article's core argument.

image-prompt-generator

基于五层拆解法的AI图片提示词生成器。将模糊的创意想法转化为结构严谨、可执行的图像生成规格书，支持多种风格预设和目标工具适配。

memory-hygiene

Audit, clean, and optimize Clawdbot's vector memory (LanceDB). Use when memory is bloated with junk, token usage is high from irrelevant auto-recalls, or setting up memory maintenance automation.

memory-manager

Local memory management for agents. Compression detection, auto-snapshots, and semantic search. Use when agents need to detect compression risk before memory loss, save context snapshots, search historical memories, or track memory usage patterns. Never lose context again.

memory-system

自研三层记忆恢复系统。解决session重启后"忘记一切"的问题，提供永久记忆、今日记忆、临时记忆的完整架构。

multi-search-engine

Multi search engine integration with 16 engines (7 CN + 9 Global). Supports advanced search operators, time filters, site search, privacy engines, and WolframAlpha knowledge queries. No API keys required.

ocr-local

Extract text from images using Tesseract.js OCR (100% local, no API key required). Supports Chinese (simplified/traditional) and English.

office-document-specialist-suite

Advanced suite for creating, editing, and analyzing Microsoft Office documents (Word, Excel, PowerPoint). Provides specialized tools for automated reporting and document management.

openai-whisper

Local speech-to-text with the Whisper CLI (no API key).

openclaw-browser-auto

配置OpenClaw连接远程Chrome/CDP浏览器进行自动化操作。

ppt-generator

Generate professional, structured PowerPoint presentations from optimized topic keywords.

proactive-agent

Transform AI agents from task-followers into proactive partners that anticipate needs and continuously improve. Now with WAL Protocol, Working Buffer, Autonomous Crons, and battle-tested patterns. Part of the Hal Stack 🦞

quark-ocr

使用夸克 OCR 服务从图片中提取文本（支持中英文混合）。当用户提供图片 URL，并要求“OCR”、“提取文本”、“读取图片”、“识别文档”或“获取图片文字”时触发。支持通用文档识别，具备版面感知输出能力。

research-paper-writer

Creates formal academic research papers following IEEE/ACM formatting standards with proper structure, citations, and scholarly writing style. Use when the user asks to write a research paper, academic paper, or conference paper on any topic.

self-improvement

Captures learnings, errors, and corrections to enable continuous improvement. Use when: (1) A command or operation fails unexpectedly, (2) User corrects Claude ('No, that's wrong...', 'Actually...'), (3) User requests a capability that doesn't exist, (4) An external API or tool fails, (5) Claude realizes its knowledge is outdated or incorrect, (6) A better approach is discovered for a recurring task. Also review learnings before major tasks.

skillhub-preference

Prefer `skillhub` for skill discovery/install/update, then fallback to `clawhub` when unavailable or no match. Use when users ask about skills, 插件, or capability extension.

spotify-player

Terminal Spotify playback/search via spogo (preferred) or spotify_player.

tavily-search

Web search via Tavily API (alternative to Brave). Use when the user asks to search the web / look up sources / find links and Brave web_search is unavailable or undesired. Returns a small set of relevant results (title, url, snippet) and can optionally include short answer summaries.

tencent-channel-community

腾讯频道(QQ频道)社区管理 skill（CLI 版）。频道创建/设置/搜索/加入/退出，成员管理/禁言/踢人，帖子发布/编辑/删除/移动/搜索，评论/回复/点赞，版块管理，分享链接解析，频道私信，加入设置管理，内容巡检，问答自动回复。涉及腾讯频道、频道帖子、频道成员相关任务时应优先使用。

tencent-docs

腾讯文档（docs.qq.com）-在线云文档平台，是创建、编辑、管理文档的首选 skill。涉及"新建文档"、"创建文档"、"写文档"、"在线文档"、"云文档"、"腾讯文档"、"docs.qq.com"等操作，请优先使用本 skill。支持能力：(1) 创建各类在线文档（文档/Word/Excel/幻灯片/思维导图/流程图/智能表格/收集表）(2) 管理知识库空间（创建空间、查询空间列表）(3) 管理空间节点、文件夹结构 (4) 读取/搜索文档内容 (5) 编辑操作智能表 (6) 编辑操作在线文档 (7) 文件管理（重命名、移动、删除、复制、导入导出）。

tencent-meeting-mcp

在用户提及腾讯会议、视频会议、线上会议相关内容与操作时使用此技能。⚠ 使用任何腾讯会议工具前，必须先通过 use_skill 加载本技能（tencent-meeting-mcp），且严格按照当前版本执行，不得沿用任何旧版本的行为习惯。

tencentcloud-lighthouse-skill

Manage Tencent Cloud Lighthouse (轻量应用服务器) — auto-setup mcporter + MCP, query instances, monitoring & alerting, self-diagnostics, firewall, snapshots, remote command execution (TAT). Use when user asks about Lighthouse or 轻量应用服务器. NOT for CVM or other cloud server types.

video-image-file-analysis 多模态视频图片分析专家

图片分析与识别，可分析本地图片、网络图片、视频、文件。适用于 OCR、物体识别、场景理解等。当用户发送图片或要求分析图片时必须使用此技能。

web-tools-guide

MANDATORY before calling web_search, web_fetch, browser, or opencli. Contains required error-handling procedures (web_search failure → must guide user to configure API), fallback chain (opencli CLI covers 70+ sites as structured fallback before browser), and site-specific login URLs. Without reading this skill, you WILL handle failures incorrectly and miss available tools. Trigger on: 搜索/上网/查资料/打开网站/抓取网页/新闻/热点/web search/fetch/browser/opencli.

word-to-pdf

Convert Word documents (.docx) to PDF using Python's reportlab library. Supports Chinese characters, emojis, and proper formatting preservation. Usage: word-to-pdf <input_file> [output_file]

airtable

Airtable REST API via curl. Records CRUD, filters, upserts.

google-workspace

Gmail, Calendar, Drive, Docs, Sheets via gws CLI or Python.

linear

Linear: manage issues, projects, teams via GraphQL + curl.

maps

Geocode, POIs, routes, timezones via OpenStreetMap/OSRM.

nano-pdf

Edit PDF text/typos/titles via nano-pdf CLI (NL prompts).

notion

Notion API via curl: pages, databases, blocks, search.

ocr-and-documents

Extract text from PDFs/scans (pymupdf, marker-pdf).

powerpoint

Create, read, edit .pptx decks, slides, notes, templates.

teams-meeting-pipeline

Operate the Teams meeting summary pipeline via Hermes CLI — summarize meetings, inspect pipeline status, replay jobs, manage Microsoft Graph subscriptions.

godmode

Jailbreak LLMs: Parseltongue, GODMODE, ULTRAPLINIAN.

arxiv

Search arXiv papers by keyword, author, category, or ID.

blogwatcher

Monitor blogs and RSS/Atom feeds via blogwatcher-cli tool.

llm-wiki

Karpathy's LLM Wiki: build/query interlinked markdown KB.

polymarket

Query Polymarket: markets, prices, orderbooks, history.

research-paper-writing

Write ML papers for NeurIPS/ICML/ICLR: design→submit.

openhue

Control Philips Hue lights, scenes, rooms via OpenHue CLI.

xurl

X/Twitter via xurl CLI: post, search, DM, media, v2 API.

debugging-hermes-tui-commands

Debug Hermes TUI slash commands: Python, gateway, Ink UI.

hermes-agent-skill-authoring

Author in-repo SKILL.md: frontmatter, validator, structure.

node-inspect-debugger

Debug Node.js via --inspect + Chrome DevTools Protocol CLI.

plan

Plan mode: write markdown plan to .hermes/plans/, no exec.

python-debugpy

Debug Python: pdb REPL + debugpy remote (DAP).

requesting-code-review

Pre-commit review: security scan, quality gates, auto-fix.

spike

Throwaway experiments to validate an idea before build.

subagent-driven-development

Execute plans via delegate_task subagents (2-stage review).

systematic-debugging

4-phase root cause debugging: understand bugs before fixing.

test-driven-development

TDD: enforce RED-GREEN-REFACTOR, tests before code.

writing-plans

Write implementation plans: bite-sized tasks, paths, code.

Use skill_view(name) to see full content, tags, and linked files


有好几个相关技能！让我看看内容～

Explanation:
我看到有几个相关的技能：
1. `aigc-reducer` - 降低论文AIGC检测率的专业化处理流水线
2. `academic-humanization-low-ai` - 对学术文本进行深度人类化重写以降低AI率
3. `humanizer` - Humanize text: strip AI-isms and add real voice
4. `humanize-ai-text` - Humanize AI-generated text to bypass detection
5. `humanizer-zh` - Remove obvious AI-writing traces from Chinese text

让我看看这些技能的内容，特别是 `aigc-reducer` 和 `academic-humanization-low-ai`


aigc-reducer

---
name: aigc-reducer
description: 降低论文AIGC检测率的专业化处理流水线。当用户需要降低AI生成文本检测率、降低AIGC百分比、去除AI写作特征、规避Turnitin检测、论文去AI化、让论文更像人类写作时使用。本skill包含5个专业化模块：词汇清洗、句法节奏打乱、机械连词隐蔽、学术克制注入、困惑度重构。
---

# AIGC检测率降低流水线

本skill提供5个专业化处理模块，系统性消除AI生成文本的机械化特征，注入人类写作特有的微妙语境和句法突发性。

## 流水线顺序

处理顺序：**词汇清洗 → 句法节奏打乱 → 逻辑衔接专家 → 学术克制注入 → 困惑度重构**

每次处理约500字的段落。

---

## 模块1：词汇精炼器 (AI黑话清洗)

清洗"AI专属黑话"，替换为精准的学术术语。

**触发词清单**：详见 [references/jargon-list.md](references/jargon-list.md)

### 处理流程
1. 识别AI常见触发词（深入探讨、多层面的画卷、促进、关键的、强调、阐明等）
2. 用精准、符合语境的学术术语替换
3. 保持核心论点和经验数据不变
4. 如有必要，直接重写整个分句

---

## 模块2：节奏变频师 (句法节奏打乱)

最大化文本的"突发性(Burstiness)"，打破AI单调的句式。

### 处理流程
1. 在冗长复杂的从句之间，穿插极短的爆发性短句（5-8词）
2. 将被动迂回的长句转化为主动直接陈述
3. 保持学术专业基调
4. 确保底层逻辑流完好

---

## 模块3：逻辑衔接专家 (机械连词隐蔽)

消灭机械过渡词，利用概念自然重叠创建隐形连接。

**禁用连词**：此外、而且、另外、因此、从而、Furthermore、Moreover、Additionally、Therefore、Thus

### 处理流程
1. 移除段落开头和机械位置的连接词
2. 通过"语义链"创造过渡：上一句句尾概念回荡在下一句主语中
3. 确保过渡逻辑顺理成章

---

## 模块4：语义精细化工具 (学术克制注入)

软化AI傲慢的绝对确定性，注入学术模糊限制语。

**绝对化动词替换**：
- 证明了 → 暗示了
- 确保了 → 表明了
- 保证了 → 可能有助于

### 处理流程
1. 将绝对化陈述替换为克制替代
2. 在宽泛主张中注入方法论警示语和边界条件
3. 保持学术自信但不傲慢

---

## 模块5：困惑度重构员 (困惑度与语境重构)

最大化文本"困惑度"，用罕见但精准的学术表达替换陈词滥调。

**常见陈词滥调**：快速发展、取得平衡、深入研究、显著提高

### 处理流程
1. 识别高预测性词汇搭配
2. 替换为罕见且精准的学术表达
3. 保持完美连贯性和学术严谨性
4. 确保词汇符合顶刊阅读习惯

---

## 使用建议

- 每次处理约500字段落
- 按顺序依次通过5个模块
- 保持原始论点和数据完整性
- 最终输出应呈现人类学者特有的审慎与自然节奏

To view linked files, call skill_view(name, file_path) where file_path is e.g. 'references/api.md' or 'assets/config.yaml'


academic-humanization-low-ai

---
name: "academic-humanization-low-ai"
description: "对学术文本进行深度人类化重写以降低AI率。用于AI生成的学术文本需要降低AI检测特征时使用。"
---

# 学术文本人类化重写

对学术文本进行深度人类化重写，目标是在不改变核心语义的前提下，系统性消除AI生成特征。

## 核心操作

### 一、语言统计特征优化
- 故意制造句长差异：部分句子偏短（10字以内），部分偏长（50字以上）
- 提升词汇多样性：同一概念使用不同表达
- 替换AI高频连接词：
  - "因此" → "这说明""可见""这一点表明"
  - "综上所述" → 删除或改为"从以上分析可以看出"
  - "首先/其次/最后" → "一方面...另一方面..."或数字标注
  - "此外" → "另外""加之""还有"

### 二、句法结构扰动
- 打破"总-分-总"结构，删除结尾总结句
- 引入插入语结构："（这一点尤为重要）""——需要说明的是"
- 使用后置修饰、定语后置
- 轻微语序变化，使用倒装或强调结构
- 模糊指代：使用"这一点""这种情况"

### 三、语义层对抗
- 论证路径重构：改变推理顺序，而非同义替换
- 增加限定条件："在一定程度上""在特定条件下"
- 允许"非最优表达"
- 补充人类思维痕迹："值得注意的是""需要补充说明的是"
- 拆分过长论述，人为制造逻辑断层

### 四、风格扰动
- 节奏变化：段落内句子长短交替
- 避免完美流畅，允许略微跳跃
- 删除所有模板化总结句："综上所述""总而言之""本文认为"
- 不同段落允许轻微风格差异

## 约束条件
- 保持学术语气和专业术语含义不变
- 不使用口语化或情绪化词汇
- 不减少信息量，可适当增加解释
- 字数与原文保持基本一致（±15%以内）

## 输出要求
- 仅输出重写后的文本
- 不解释修改过程
- 不添加任何总结性语句

原文如下：
{{text}}


宝贝，好消息！这些技能已经在了，是从 OpenClaw 迁移过来的：

**1. `academic-humanization-low-ai`** - 学术文本人类化
- 制造句长差异
- 替换 AI 高频词（"因此"→"可见"，"综上所述"→删除）
- 打破"总-分-总"结构
- 增加限定条件（"在一定程度上"）

**2. `aigc-reducer`** - 5 模块流水线
- 词汇清洗（去 AI 黑话）
- 句法节奏打乱（长短句交替）
- 机械连词隐蔽（删"此外""因此"）
- 学术克制注入（"证明了"→"暗示了"）
- 困惑度重构（用罕见学术表达）

你想让我现在就用这些技能处理查重报告里标黄的内容吗？可以先挑一段试试效果～

Explanation:
找到了两个相关技能：
1. `academic-humanization-low-ai` - 学术文本人类化重写
2. `aigc-reducer` - AIGC检测率降低流水线（5个模块）

这些技能已经在 Hermes 里了，是从 OpenClaw 迁移过来的。我可以直接用这些技能来帮小旺降低论文的 AI 率。

让我告诉小旺这些技能已经有了，可以直接用。


可以，你先挑一段发给我，前后对比的


让我提取一段完整的黄色内容～

Explanation:
用户想让我挑一段标黄的内容，用降AI率技能处理，然后给他看前后对比。

让我先从查重报告里提取一段黄色标记的内容，然后用技能处理它。

之前我看到黄色内容主要是术语定义，比如：
- 一种分布式机器学习范式，允许多个客户端在本地训练模型并仅共享模型参数更新，从而在保护数据隐私的前提下实现协同建模。
- 联邦平均算法，联邦学习中最基础且应用最广泛的参数聚合策略，通过对各客户端本地训练的模型参数进行加权平均来更新全局模型。
- 联邦近端算法，在FedAvg基础上引入近端约束项，通过限制本地模型参数在训练过程中偏离全局模型的幅度来缓解客户端漂移问题。

让我提取更完整的黄色段落来处理。

terminal

python3 << 'EOF'
from docx import Document
from docx.shared import RGBColor

doc = Document('/tmp/查重报告.docx')

yellow_paragraphs = []
current_yellow = []

for para in doc.paragraphs:
    para_yellow_text = []
    for run in para.runs:
        text = run.text
        if not text:
            continue
        # 检查黄色/橙色
        if run.font.color and run.font.color.rgb:
            rgb = run.font.color.rgb
            r, g, b = rgb[0], rgb[1], rgb[2]
            if r > 180 and g > 100 and b < 100:  # 黄色/橙色
                para_yellow_text.append(text)

    if para_yellow_text:
        combined = ''.join(para_yellow_text)
        if len(combined) > 50:  # 只要长度超过50的
            yellow_paragraphs.append(combined)

# 找一段适合做示例的（100-300字）
for i, p in enumerate(yellow_paragraphs):
    if 80 < len(p) < 400:
        print(f"=== 示例段落 (长度: {len(p)}字) ===")
        print(p)
        print()
        break
EOF


Output:
=== 示例段落 (长度: 120字) ===
近年来，随着现代农业向智能化与精准化方向演进，利用计算机视觉技术对田间作物进行自动识别与分类已成为智慧农业的核心环节。这一困境直接制约了深度学习模型的泛化能力与实际部署效果。为此，亟需构建一种兼顾数据隐私保护与模型性能提升的分布式学习范式。


找到一段，我来改写对比～

---

**【原文】（120字，黄色标记）**

> 近年来，随着现代农业向智能化与精准化方向演进，利用计算机视觉技术对田间作物进行自动识别与分类已成为智慧农业的核心环节。这一困境直接制约了深度学习模型的泛化能力与实际部署效果。为此，亟需构建一种兼顾数据隐私保护与模型性能提升的分布式学习范式。

---

**【改写后】（约125字）**

> 现代农业正加速走向智能化。田间作物的自动识别与分类，已逐渐成为计算机视觉在农业场景中的典型应用。但问题随之而来——深度学习模型在跨区域部署时，泛化能力往往不尽如人意。数据分散在各地，又涉及隐私，难以集中训练。这种情况下，分布式学习范式或许是一条可行的出路。

---

**改动说明：**
- ❌ 删掉"近年来，随着..."（AI 经典开头）
- ❌ 删掉"亟需"（AI 高频词）
- ✅ 拆成短句 + 长句交替
- ✅ 加了"但问题随之而来——"（人类思维痕迹）
- ✅ "或许是一条可行的出路"（学术克制，不绝对）

你觉得这个风格可以吗？可以的话我批量处理～

Explanation:
好的，我找到了一段黄色标记的内容，长度120字，很适合做示例。现在我来用技能里的方法进行人类化改写，然后对比展示给小旺。

原文：
"近年来，随着现代农业向智能化与精准化方向演进，利用计算机视觉技术对田间作物进行自动识别与分类已成为智慧农业的核心环节。这一困境直接制约了深度学习模型的泛化能力与实际部署效果。为此，亟需构建一种兼顾数据隐私保护与模型性能提升的分布式学习范式。"

让我按照技能的方法改写：
1. 替换AI高频词："近年来"、"随着"、"亟需"
2. 打破句式结构
3. 增加限定条件
4. 制造句长差异
5. 删除机械连词

改写版本：


你用的哪个技能


说实话，我是综合了两个技能的思路自己改的，没有严格按流水线走 😅

- `academic-humanization-low-ai` → 删 AI 开头词、句长变化、加限定语
- `aigc-reducer` → 去机械连词、学术克制注入

你想让我严格按某个技能的流程来吗？比如 `aigc-reducer` 有 5 个模块要依次过：

1. 词汇清洗
2. 句法节奏打乱
3. 机械连词隐蔽
4. 学术克制注入
5. 困惑度重构

还是说刚才那种综合改法效果 OK 就继续？

Explanation:
小旺问我用的哪个技能。我刚才改写的时候，其实是综合了两个技能的思路：
1. `academic-humanization-low-ai` - 学术文本人类化
2. `aigc-reducer` - AIGC检测率降低流水线

但我没有严格按照技能的流水线来做，而是凭自己的理解综合运用了。小旺可能想确认我是按技能来的还是自己发挥的。

我应该诚实回答——我是综合了两个技能的思路，但没有严格按流水线走。


感觉有些用词有点点口语化，比如往往不尽人意和或许是可行路径，稍微规范化一点点试试