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jinqiu193

long-doc-agent

by jinqiu193 · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ⚠ suspicious
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Install in OpenClaw
/install long-doc-agent
Description
Multi-Agent collaborative system for writing ultra-long feasibility study reports. Phase 0 Requirements → Phase 1 Planner Outline → Phase 2 Parallel Sub-Agen...
README (SKILL.md)

Ultra-Long Feasibility Report Multi-Agent Collaborative Writing v3.3

Changelog (v3.3)

  • ✅ Table parsing fully fixed (_flush_table not calling _parse_md_table caused character-by-character splitting)
  • ✅ Colorful chapter headings officially launched (H1 deep navy full-row band / H2 medium blue block / H3 light blue left bar)
  • ✅ Styled tables launched (deep blue header + white text + alternating row colors)
  • ✅ Key callout boxes launched (【关键】【注意】【优势】【风险】【数据】 color-coded cards)
  • ✅ Cover style enhanced (tech-digital style deep ocean blue full-screen background + white text)
  • ✅ Cover/Contents/Executive Summary title bars all colored
  • ✅ Fixed cover_style integer vs string comparison preventing cover from applying
  • ✅ Fixed RGBColor.from_string() instead of eval to avoid type errors

Core Capabilities

  • Multi-Agent Parallel: Up to 5 sub-agents writing concurrently, doubled efficiency
  • Incremental Updates: Chapters with unchanged content are skipped, faster processing
  • Beautiful Formatting: Auto-generated cover, table-style TOC, colorful chapter headings, key callout boxes, styled tables
  • Feishu RAG: Auto-search Feishu knowledge base to supplement reference materials
  • 6 Cover Styles: Switch freely to suit different scenarios

File Structure

skill_dir/
├── SKILL.md                    # This file
├── integrate_report.py          # Core engine: parse/ integrate/CLI
├── parallel_tracker.py          # Parallel progress tracking
└── references/                 # Sub-process reference documents
    ├── phase0_guide.md         # Phase 0 requirements confirmation flow
    ├── phase1_guide.md         # Planner prompt template
    ├── phase2_guide.md         # Sub-Agent prompt template
    ├── table_format_guide.md   # Markdown table format specification
    └── bug_fix_guide.md        # Bug troubleshooting & forced rebuild

First-time setup: Ensure the F:/agent/chapters/ directory exists.


Pipeline Routing

User Task
├─ First writing request ("I want to write xxx"/"help me write a feasibility report")
│   → Phase 0 Requirements → Phase 1 Planner
│
├─ Outline exists, request to start writing
│   → Phase 2 Parallel Sub-Agents
│
├─ A chapter needs modification
│   → Small change: directly edit F:/agent/chapters/0X-xxx.txt
│   → Large change: regenerate that chapter
│
├─ All chapters done, request docx generation
│   → Phase 2.5 Review → Phase 3 Integrator
│
├─ Independent small proposal (2~5 chapters, no existing chapters dependency)
│   → Write Markdown directly → make_docx.py to generate styled docx
│   → See: references/bug_fix_guide.md "make_docx.py Mode"
│
└─ Just need to check progress/glossary/reference materials
    → Direct CLI commands

Phase 0: Requirements Confirmation

Confirm 4 items in order; all confirmed → Phase 1:

  1. Writing Topic: document type / audience / style / special constraints
  2. Background Information: project background / construction goals / industry context
  3. Reference Materials (most important):
    • A. Local file path or paste directly
    • B. Feishu document (RAG search)
    • C. Paste content directly
    • D. Not provided for now
  4. Outline Confirmation: After planner outputs outline, user chooses A.Start / B.Adjust / C.Cancel

More reference materials → more business-aligned content. See references/phase0_guide.md for details.


Phase 1: Planner

Input: Phase 0 topic / background / reference materials

Execute:

python integrate_report.py glossary

Auto-generates plan.json + plan_outline_snapshot.md

Full prompt template in references/phase1_guide.md

After completion, send WeChat notification (using message tool, channel=openclaw-weixin):

📋 Report Outline Generated

📌 《[Report Topic]》
📊 Chapters: [X] chapters
🔍 Industry: [Industry Field]

✅ Reply "start writing" once the outline is confirmed — the system will launch parallel creation!

Phase 2: Parallel Sub-Agents

Execution flow (fully automatic, no manual confirmation):

  1. Display outline / current batch status (display only, no waiting)
  2. python parallel_tracker.py clear to clear previous batch state
  3. Start up to 5 concurrent sub-agents (sessions_spawn), automatically execute all batches
  4. python parallel_tracker.py wait to monitor in background until this batch is complete
  5. After completion, automatically run python integrate_report.py convert-batch

Sub-Agent prompt template: see references/phase2_guide.md

After each batch completes, send WeChat notification (using message tool, channel=openclaw-weixin):

✅ Batch [X] Chapter Writing Complete!

📖 Completed: [Done]/[Total] chapters
📝 This batch:
   • [Chapter 1 Title]
   • [Chapter 2 Title]
   • [Chapter 3 Title] (if any)

⏳ Next batch: [Next batch chapter list]
(Automatically proceeds to next batch, no manual confirmation needed)
  • Small change: directly edit F:/agent/chapters/0X-xxx.txt, save and regenerate
  • Large change: re-trigger sub-agent to rewrite, replacing the original file

Phase 2.5: Cross-Chapter Consistency Review

python integrate_report.py check

Review numerical indicator consistency and terminology uniformity (对照 glossary.json)

After review completes, send WeChat notification (using message tool, channel=openclaw-weixin):

🔍 Consistency Review Complete

✅ Terminology uniformity: OK
✅ Numerical indicators: consistent
✅ Cross-chapter references: no conflicts

📄 Proceeding to final integration phase...

Phase 3: Integrator Summary

python integrate_report.py

Auto-completes: parse chapters (error isolation) → update glossary → consistency review → generate styled docx

After final completion, send WeChat notification (using message tool, channel=openclaw-weixin):

🎉🎉🎉 Report Writing Complete! 🎉🎉🎉

📄 《[Report Topic]》
📊 Scale: [X] chapters / ~[Y] thousand characters
🎨 Cover Style: [Style Name]

✅ Styled report generated!
📁 File location: F:/agent/chapters/output/

Wenxin, full text ready for your review~

Document Beautification Features (Auto-Applied)

Generated reports automatically include the following formatting effects (selected via cover_style field in plan.json):

  1. 6 Cover Styles — Edit plan.jsoncover_style field (integer 1~6)
  2. Executive Summary — Deep blue title bar (#1F4E79) background + white text + body indent
  3. Table-Style TOC — Deep blue title bar + three-column entries (number/chapter/page)
  4. Colorful Chapter Headings:
    • H1: Full-row deep navy background #1F4E79 + white text Microsoft YaHei
    • H2: Medium blue background #2E75B6 + white text
    • H3: Light blue background #D6E4F0 + dark blue text + left bar
  5. Key Callout Boxes — Auto-detect 【关键】【注意】【优势】【风险】【数据】 tags, render as color cards (background/white text/border)
  6. Styled Tables — Header deep navy background #1F4E79 + white text + alternating row colors (#DEEAF6 / #FFFFFF)

Cover Styles (6 Types)

Cover style specified via cover_style field in plan.json (integer, 1~6):

# Style Name Features Recommended For
1 Classic Government Deep navy top bar + gold accents Government/state enterprise approval
2 Modern Minimalist Left blue heavy block + right info Tech/business reports
3 Business Elegant Burgundy + centered progression Consulting/investment bank reports
4 Tech Digital Deep ocean blue fill + large white title Internet/digital projects
5 Chinese Traditional Forbidden City red + rice paper cream background Traditional culture/state enterprise
6 Full Immersive Deep ocean blue fill + large white title Digital/tech projects

Note: cover_style value is integer (e.g., 4), code automatically converts to string for comparison.


CLI Command Reference

Command Description
python integrate_report.py Generate integrated report (full)
python integrate_report.py convert-batch Batch convert to docx
python integrate_report.py convert-one \x3Cin> \x3Cout> Single chapter to docx
python integrate_report.py check Consistency review
python integrate_report.py glossary Glossary generation/update
python integrate_report.py ref show View reference materials
python integrate_report.py ref clear Clear reference materials
python integrate_report.py preview [chapter prefix] Preview chapter summary
python integrate_report.py feishu-search \x3Cquery> Search Feishu knowledge base
python parallel_tracker.py show View writing progress
python parallel_tracker.py wait Block & monitor (Ctrl+C to stop)
python parallel_tracker.py clear Clear tracking state

Switching cover style: Edit cover_style field (integer 1~6) in F:/agent/chapters/plan.json, then regenerate. After modifying code: delete .pyc files under __pycache__ + content_hashes.json to force rebuild.


State Files

File Description
F:/agent/chapters/plan.json Chapter metadata
F:/agent/chapters/glossary.json Terminology table
F:/agent/chapters/reference_material.txt Raw reference materials
F:/agent/chapters/plan_outline_snapshot.md Outline snapshot
F:/agent/chapters/content_hashes.json Incremental cache (delete to force rebuild)
F:/agent/chapters/writing_tracker.json Parallel progress tracking
F:/agent/chapters/config.json Cover style and other config

Critical Rules

Markdown Table Format (Sub-Agents Must Follow)

See references/table_format_guide.md for full spec

Key points:

  • Separator row must be |---|---|---| (leading/trailing | required)
  • All rows must have same column count as header
  • Cell content should avoid containing | (use or - for ranges)

Force Rebuild (Must Do Both Steps After Code Changes)

After modifying integrate_report.py, must delete both files for new code to take effect:

# 1. Delete .pyc cache (required after code changes)
del "C:\Users\Administrator\AppData\Roaming\LobsterAI\SKILLs\long-doc-agent\__pycache__\integrate_report.cpython-311.pyc"

# 2. Delete incremental hash (or incremental mode skips everything)
del F:\agent\chapters\content_hashes.json

# 3. Regenerate
python integrate_report.py

Known Bugs Fixed (For Reference)

See references/bug_fix_guide.md, including:

  • _flush_table not calling _parse_md_table causing character-by-character table splitting
  • cover_style integer vs string comparison preventing cover from applying
  • eval RGB color assignment type error
  • .pyc cache causing new code to not take effect
  • RGBColor using index access rgb[0]/rgb[1]/rgb[2] instead of .red/.green/.blue
  • add_cover() setting section.margin=0 causing body text to have no margins
  • PermissionError when docx file is open in WPS → auto-add _v2 suffix
  • write tool has 50KB line limit → large scripts must be written in chunks

References

File Content
references/phase0_guide.md Phase 0 requirements confirmation full flow & scripts
references/phase1_guide.md Planner full prompt template & plan.json format
references/phase2_guide.md Sub-Agent full prompt template (incl. table format warnings)
references/table_format_guide.md Markdown table format spec, common errors & examples
references/bug_fix_guide.md Bug troubleshooting & forced rebuild procedures
Usage Guidance
What to check before installing: - Confirm the file paths: This skill reads/writes at F:/agent/chapters and references E:\lonb\... — it appears Windows-specific and will create and modify files there. If you don't want files written to those locations, do not install or edit the code/paths first. - External integrations: The SKILL.md instructs Feishu RAG searches and sending WeChat notifications via a 'message' tool (openclaw-weixin). The skill metadata declares no credentials — ask the author which connectors/credentials are required, or assume the platform's messaging/Feishu tools will be used. If you do not trust automatic external posting of content, do not enable or bind the messaging/Feishu connectors. - Automatic behavior: The skill explicitly runs Phase 2 'fully automatic, no manual confirmation' and will spawn up to 5 sub-agents and automatically proceed between batches and to docx generation. If you want human approval before publishing or sending data externally, do not enable this skill or modify the workflow to require confirmation. - Rendering tool dependency: The code will try to call mermaid mmdc/node if present. If you lack these tools, rendering will fall back, but the code spawns subprocesses — review them if you run in a restricted/sensitive environment. - Review and sandbox first: Run the skill in an isolated environment (or inspect/modify the scripts) to change paths, disable automatic notifications, and confirm no undesired deletions (the bug_fix_guide suggests deleting files under AppData roaming and F:/ — be cautious). - Ask for clarification: Request the author to declare required connectors/credentials (Feishu, WeChat/message tool) and to make automatic notifications and automatic progression opt-in instead of default. Reason for 'suspicious': overall behavior matches the stated purpose, but the mismatch between declared requirements and the runtime instructions that read/write local files and transmit progress via external messaging is an unexplained inconsistency that could leak content or perform unexpected file operations.
Capability Analysis
Type: OpenClaw Skill Name: long-doc-agent Version: 1.0.0 The skill bundle implements a complex multi-agent document generation system that exhibits several high-risk behaviors. Specifically, 'integrate_report.py' uses 'subprocess.run' to execute external commands (Node.js/Mermaid CLI) with hardcoded paths, and 'SKILL.md' (along with 'bug_fix_guide.md') instructs the AI agent to delete files within the 'AppData' directory to clear Python caches. While these actions appear intended for legitimate document styling and environment management, the use of shell execution on agent-generated content and the manipulation of system-level directories represent a significant security risk and potential for unintended code execution or file system interference.
Capability Assessment
Purpose & Capability
The code (integrate_report.py, parallel_tracker.py) and SKILL.md align with the declared purpose (multi-agent writing and docx integration). However the skill references Feishu RAG and automatic WeChat notifications while declaring no credentials or external service requirements; it also hard-codes Windows-specific paths (e.g., F:/agent/chapters, E:\lonb\...) that are not documented as required configuration. These are plausible for a local Windows deployment but are not proportionately declared.
Instruction Scope
Runtime instructions ask the agent to read/write files under F:/agent/chapters, to spawn sub-agents (sessions_spawn) and to send WeChat messages via a 'message' tool (channel=openclaw-weixin). The skill explicitly states several phases run 'fully automatic, no manual confirmation' and will auto-send notifications after batches complete. That grants the skill broad discretion to read user-provided reference materials and to transmit progress/content via an external messaging tool — this is not surfaced as a required permission/credential in the metadata.
Install Mechanism
No install spec (instruction-only plus included Python scripts). No external archive downloads or package installs in the manifest. The code does call subprocess to detect/run mermaid CLI or node/mmdc if present — rendering is optional and depends on local environment.
Credentials
The skill requests no environment variables or credentials but expects integration with Feishu (RAG search) and WeChat messaging (message tool). Those integrations typically require credentials/config; their absence in requires.env is an inconsistency. The code also refers to user-local paths (AppData roaming __pycache__ path) and suggests deleting files — filesystem access is broad relative to the declared metadata and could be surprising to users.
Persistence & Privilege
always:false (normal). The skill relies on autonomous agent features (sessions_spawn) and explicitly instructs fully automatic batch progression and notifications without manual confirmation — this is a functional design choice but increases the chance of unintended automated actions or data transmission. It does write/read files under F:/agent/chapters and suggests deleting cached files under the user's roaming SKILLs path, but it does not request system-wide privileges or modify other skills' configs.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install long-doc-agent
  3. After installation, invoke the skill by name or use /long-doc-agent
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
long-doc-agent v3.3.0 – Major document formatting, workflow enhancements, and stability fixes - Table parsing fully fixed to prevent character-by-character splits. - Launched colorful chapter headings and styled tables for improved document aesthetics. - Introduced key callout boxes (【关键】【注意】【优势】【风险】【数据】) as color-coded cards. - Enhanced cover style with tech-themed options and colored bars for cover, contents, and executive summary. - Improved system robustness: fixed cover-style comparison issue and type errors in color parsing. - Streamlined multi-agent workflow with incremental updates, parallel writing, consistency checking, and Feishu RAG support.
Metadata
Slug long-doc-agent
Version 1.0.0
License MIT-0
All-time Installs 0
Active Installs 0
Total Versions 1
Frequently Asked Questions

What is long-doc-agent?

Multi-Agent collaborative system for writing ultra-long feasibility study reports. Phase 0 Requirements → Phase 1 Planner Outline → Phase 2 Parallel Sub-Agen... It is an AI Agent Skill for Claude Code / OpenClaw, with 54 downloads so far.

How do I install long-doc-agent?

Run "/install long-doc-agent" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is long-doc-agent free?

Yes, long-doc-agent is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does long-doc-agent support?

long-doc-agent is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created long-doc-agent?

It is built and maintained by jinqiu193 (@jinqiu193); the current version is v1.0.0.

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