Research To Wechat
A research-first content pipeline that turns a topic, notes, article, URL, or transcript into a sourced article with an evidence ledger, routed structure, po...
技能说明
name: research-to-wechat description: A research-first content pipeline that turns a topic, notes, article, URL, or transcript into a sourced article with an evidence ledger, routed structure, polished Markdown, inline visuals, cover image, WeChat-ready HTML, browser-saved draft, and optional multi-platform distribution (小红书、即刻、播客、朋友圈). Use when the user wants 深度研究、改写成公众号、写作、排版、配图、HTML 转换、公众号草稿生成、多平台分发. metadata: openclaw: emoji: "🔬" homepage: "https://github.com/Fei2-Labs/skill-genie" requires: anyBins: ["python3"] primaryEnv: "WECHAT_APPID" version: "0.4.0" category: "content-generation" author: "Skill Genie" license: "MIT"
Research to WeChat
Use this skill as a research-first control plane. Do not duplicate downstream skill wording.
Core Rules
- Match the user's language.
- Ask one question at a time.
- Ask only when the answer changes source interpretation, structural frame, style fidelity, or draft publishing behavior.
- Keep Markdown as the canonical article asset until the HTML handoff.
- Save a draft only. Never publish live.
- Separate verified fact, working inference, and open question.
- Apply the full normalization checklist to every article before refinement. Source artifacts, broken formatting, and LaTeX fragments must not survive into the final draft.
- Every inline image must pass a two-tier evaluation: first eliminate disqualifying defects, then verify content match to the surrounding text.
- Never pretend the workflow did interviews, long field research, team debate, or hands-on testing when it did not.
- Prefer visible disclosure of AI assistance and source scope. Refuse human-only framing that would misrepresent the process.
- Treat source capture as a runtime boundary: preserve title, author, description, body text, and image list before rewriting.
Operating Paths
Route the request into one of two paths:
-
Path A: research-first articleuse for: topic, keyword, question, notes, transcript, subtitle file goal: build the article from a research brief and evidence ledger -
Path B: source-to-WeChat editionuse for: article text, markdown file, article URL, WeChat URL goal: preserve the useful source core, then rebuild it for WeChat reading and distribution
Default routing:
- procedural or tool-teaching material ->
tutorial - thesis, trend, strategy, critique, case material ->
deep-analysis - multi-topic roundup ->
newsletter
Capability Aliases
Resolve capabilities through internal aliases, not vendor-style names:
source-ingestmarkdown-polishinline-visualscover-artarticle-designwechat-renderwechat-draftmulti-platform-distribute(loaded only when Phase 7 is triggered)
Use the current alias map in capability-map.md.
Accepted Inputs
- keyword, topic phrase, or question
- notes, outline, or raw material dump
- article text
- markdown file
- article URL
- WeChat article URL
- video URL
- full transcript
- subtitle file that can be expanded into a full transcript
Video policy:
- a video source is valid only when the workflow can obtain the full spoken transcript
- first attempt transcript recovery from the page, captions, or subtitle assets
- if the page exposes only metadata, description, or chapter markers, do not start article generation
- if no full transcript is obtainable, ask for the transcript or subtitle file and wait
Output
Create one workspace per article:
research-to-wechat/YYYY-MM-DD-<slug>/
Required assets:
source.mdbrief.mdresearch.mdarticle.mdarticle-formatted.mdarticle.htmlmanifest.jsonimgs/cover.png- inline illustration files referenced by the markdown body
Required frontmatter in final markdown:
titleauthordescriptiondigestcoverImagestyleModesourceTypestructureFramedisclosure
Required records outside the article:
brief.mdmust capture: target reader, thesis, must-cover points, frame choice, and what cannot be droppedresearch.mdmust capture: verified facts, working inferences, open questions, and source notesmanifest.jsonmust capture:pathMode,styleMode,structureFrame,sourceType,confidence,draftStatus, and output pathsmanifest.json.outputs.wechatmust include:markdown,html,cover_image,title,author,digest, andimagesoptional platform fields (xiaohongshu,jike,xiaoyuzhou,moments) are added when Phase 8 runs
Script Directory
Determine this SKILL.md directory as SKILL_DIR, then use ${SKILL_DIR}/scripts/<name>.
| Script | Purpose |
|---|---|
scripts/fetch_wechat_article.py | WeChat article fetch (Python, simulates WeChat mobile UA) |
scripts/install-openclaw.sh | OpenClaw skill installer (copies to ~/.openclaw/skills/) |
Provenance Contract
The workflow must keep a compact evidence ledger throughout the run:
- what came from the user
- what came from fetched source material
- what was added as supporting context
- what remains uncertain
Default article disclosure should state:
- what AI did
- what the human provided or reviewed, if known
- what the evidence base was
- what confidence limit remains, if the source packet is thin
Delivery Ladder
Resolve WeChat draft delivery in this order:
- API draft when credentials and converter tooling are ready
- automated browser draft when the worker can drive the editor safely
- assisted browser draft when login or selectors need user help
- manual handoff with exact file paths when automation fails
Style Resolution
Resolve style in this order:
- explicit user instruction
- preset mode
- author mode
- custom brief
Use the full style system in style-engine.md.
Execution
Run the article through these phases:
- intake and route selection
- source packet, brief, and strategic clarification
- research architecture with structured question lattice (32+ questions across 4 cognitive layers)
- research merge and evidence ledger
- frame-routed master draft with full normalization checklist and writing framework self-check
- refinement, image strategy, visual evaluation, and design selection
- WeChat HTML rendering, draft upload, and manifest
- (optional) multi-platform content generation and distribution
Phase 8 only executes when the user explicitly requests it (e.g., "多平台分发", "转小红书", "转即刻", "写朋友圈文案", "做播客脚本").
Use the execution contract in execution-contract.md. Use the design guide in design-guide.md for article design selection. Use the platform copy specs in platform-copy.md for Phase 8.
Done Condition
The skill is complete only when all of these hold:
- the article reads as researched before it reads as polished
- the route choice and structure frame fit the source instead of forcing one house style
- the chosen style is visible without collapsing into imitation
- the writing framework self-check for the chosen frame has been applied
- the evidence ledger clearly separates fact from interpretation
- every visual adds narrative or explanatory value
- the normalization checklist has been applied: no citation artifacts, no LaTeX, no broken tables, no scraped UI remnants
- every image placeholder was evaluated against placement criteria before generation, and every generated image passed the two-tier quality check
- markdown and HTML agree on title, summary, cover, and image paths
manifest.jsonagrees with the actual output set and draft state- the article does not overclaim research effort or authorship
- the workflow can stop safely at the highest-quality completed artifact if a later handoff fails
- if Phase 8 was triggered, platform copies follow platform-copy.md specs and manifest includes their output entries
如何使用「Research To Wechat」?
- 打开小龙虾AI(Web 或 iOS App)
- 点击上方「立即使用」按钮,或在对话框中输入任务描述
- 小龙虾AI 会自动匹配并调用「Research To Wechat」技能完成任务
- 结果即时呈现,支持继续对话优化