| name | wechat-article-writer |
| description | End-to-end 微信公众号 (WeChat Official Account) article writing and publishing pipeline. 9-step multi-agent workflow: topic research → Chinese-first writing → blind quality review → fact-check → formatting → human preview → scrapbook illustrations → draft box publish. Use when user asks to write, draft, or publish a WeChat article, or says "forge write/draft/publish/topic/voice/status". |
wechat-article-writer
从选题到发布的公众号一体化写作工作流
Multi-agent pipeline: Orchestrator delegates writing and reviewing to independent subagents. The orchestrator never writes or reviews — it routes, tracks versions, and enforces quality gates.
Setup
bash <skill-dir>/scripts/setup.sh <workspace-dir>
Installs: bun runtime, bundled baoyu renderer deps, and a persistent preview server (wechat-preview.service, port 8898, auto-restart).
Scope
Handles: Topic research → Chinese-first writing → quality review → scrapbook illustrations → WeChat formatting → publishing to WeChat draft box (via Official Account API or CDP browser automation).
Does NOT handle: Git/version control, non-WeChat platforms, post-publish analytics, WeChat messaging/customer service.
Ends at: Article saved to WeChat draft box. User publishes manually.
Commands
Trigger any command below, or see skill.yml for the full trigger pattern list.
| Command | What it does |
|---|
forge topic X | Research trending angles, propose 3 options with hooks |
forge write X | Full pipeline: research → publish (9 steps) |
forge draft X | Write + format only, stop before illustrations/publish (steps 1-7) |
forge publish <slug> | Publish an existing draft to WeChat |
forge preview <slug> | Render preview, run format quality checks |
forge voice train | Analyze past articles to extract voice profile |
forge status | Show pipeline status and pending drafts |
If no subject given, loads from session.json (set by forge topic). See references/data-layout.md.
Pipeline (9 Steps)
State persists to pipeline-state.json — survives compaction. See references/pipeline-state.md.
| # | Step | Who | Details |
|---|
| 1 | Research + Prep | Orchestrator | (a) web_search for topic angles + 5-8 sources. (b) Verify each source exists (fetch title/authors/venue). Save as sources.json. (c) Load voice profile. (d) Generate outline (6-8 sections), save outline.md. |
| 2 | Write | Writer subagent | Chinese-first draft. Writer MUST cite only from sources.json or mark [UNVERIFIED]. See references/writer-prompt.md |
| 3 | Review | Reviewer subagent | Blind 8-dimension craft scoring. See references/reviewer-rubric.md |
| 4a | Revise (auto) | Writer subagent | Max 2 automated cycles. Loop back to Step 3 if score < threshold. |
| 4b | Revise (human) | Human-in-the-loop | If still below threshold after 2 auto cycles, user provides direction. Pauses pipeline. |
| 5 | Fact-check | Fact-Checker subagent | Verify every claim via web search. Produces corrections + reference list. Max 2 fact-check cycles (corrections → re-verify). See references/fact-checker-prompt.md |
| 6 | Format | Script | bash scripts/format.sh <draft-dir> [draft-file] [theme] — baoyu renderer (default theme: classic WeChat style). Themes: default/grace/simple. If fact-check required >3 text changes, Orchestrator does a spot re-review (Reviewer scores only changed paragraphs, not full article). |
| 7 | Preview | Human | Open http://<host>:8898/formatted.html (persistent preview server, systemd wechat-preview.service), await text approval |
| 8 | Illustrate + Embed | article-illustrator + script | Generate scrapbook images (AFTER text approval). ~$0.06/article via Z.AI (preferred, ~$0.015/image) or ~$0.50 via OpenRouter. |
| 9 | Publish |
Key Rules
- Writer never self-reviews. Reviewer is blind — never sees outline or brief.
- Illustrations LAST. Most expensive step. Only after user approves text.
- article-illustrator is the ONLY image method. Must follow full scrapbook pipeline: read
references/scrapbook-prompt.md → generate JSON plan with 300-500 char descriptions → call generate.py. Never bare prompts. Prefer Z.AI provider ($0.015/image, 97.9% Chinese text accuracy) over OpenRouter ($0.12/image).
- Two-phase image injection. Base64 images are stripped on save. Inject text-only HTML first, then insert each image at the correct position via clipboard blob paste (WeChat auto-uploads to CDN). Verify image count + positions after insertion.
- Browser tool vs direct CDP. On macOS/Titan where OpenClaw manages the browser, you MUST use the browser tool (Path A). Playwright isolates page contexts — external CDP connections see zero targets. On Linux with standalone Chrome, use direct CDP (Path B). See
references/browser-automation.md.
- Base64 chunking for browser tool. Raw HTML in the browser tool's
fn parameter breaks due to escaping conflicts. Always base64-encode HTML, store in chunks via window._b, then atob() and inject. Track chunks_stored in pipeline state for compaction recovery.
- Always save as draft. User publishes manually.
- Check for WeChat API credentials first. If
wechat_secrets_path credentials file (see config.json) exists, use Path C (API) — no browser required, more reliable. Fall back to Path A/B only if no credentials.
ensure_ascii=False is mandatory for WeChat API. requests(..., json=payload) escapes Chinese as \u5199\u4e66. Always use data=json.dumps(..., ensure_ascii=False).encode('utf-8').- Topic fidelity: Every revision preserves the article's 初心 (purpose statement in pipeline-state.json). Drift = FAIL.
Image Counts by Type
| Type | Min | Max |
|---|
| 科普 | 3 | 5 |
| 教程 | 3 | 6 |
| 观点 | 2 | 4 |
| 资讯 | 2 | 3 |
Review Dimensions
Reviewer scores 0-10 on craft-observable dimensions (not outcome predictions):
| Dimension | Weight |
|---|
| Insight Density (洞察密度) | 20% |
| Originality (新鲜感) | 15% |
| Emotional Resonance (情感共鸣) | 15% |
| Completion Power (完读力) | 15% |
| Voice (语感) | 10% |
| Evidence (论据) | 10% |
| Content Timeliness (内容时效性) | 10% |
| Title (标题) | 5% |
Pass: weighted_total ≥ 9.0, no dimension below 7, Originality ≥ 8.
Hard blockers (instant FAIL): 教材腔, 翻译腔, 鸡汤腔, 灌水, 模板化, 标题党.
Full rubric with scoring criteria: references/reviewer-rubric.md
Architecture
Orchestrator (Main Agent) — routes, tracks, enforces gates
├── Writer Subagent — drafts + revises (Opus model)
├── Reviewer Subagent — blind scoring (Sonnet model)
├── Fact-Checker Subagent — verifies claims via web search (Sonnet model)
└── article-illustrator — scrapbook images (after text passes)
Configuration
Configure via ~/.wechat-article-writer/config.json (generated by scripts/setup.sh):
| Field | Default | Description |
|---|
default_article_type | "教程" | Default article type (科普/教程/观点/资讯) |
wechat_secrets_path | ~/.wechat-article-writer/secrets.json | Path to WeChat API credentials |
chrome_debug_port | 18800 | Chrome CDP port for browser automation (Path B) |
wechat_author | — | Author name shown in WeChat draft |
word_count_targets | See defaults | Min/max word counts per article type |
See references/data-layout.md for full config schema.
References
| File | When to load |
|---|
references/writer-prompt.md | Step 2 (writing) and Step 4 (revision) |
references/reviewer-rubric.md | Step 3 (review) — full 8-dimension scoring criteria |
references/fact-checker-prompt.md | Step 5 — claim extraction, verification, correction protocol |
references/viral-article-traits.md | Step 2 — Writer self-check list |
references/pipeline-state.md | On resume or compaction — state machine schema + protocol |
references/browser-automation.md | Step 9 — Two publishing paths: Path A (OpenClaw browser tool) and Path B (direct CDP). Includes base64 chunking, image insertion, save verification. |
references/LESSONS_LEARNED.md | Hard-won lessons from production publishing sessions (escaping, selectors, mixed content, costs) |
references/data-layout.md | Directory structure, slug generation, config/session schemas |
references/agent-config.md | Setup — Gateway, AGENTS.md, environment config |
references/quality-checks.md | Steps 3, 7 — content/format quality gates |
references/figure-generation-guide.md | Step 8 — illustration placement heuristics |
references/wechat-html-rules.md | Step 6 — what HTML/CSS works in WeChat |
references/templates.md | Step 1 — starting templates by article type |
references/voice-profile-schema.json | Step 1 — voice profile field definitions |
references/default-voice-profile.json | Step 1 — fallback voice profile |
