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BytesAgain › Skills › WeChat Article Writer
🦀 ClawHub

WeChat Article Writer

by @chunhualiao

End-to-end 微信公众号 article writing and publishing — from topic ideation to published article, with quality gates.

Versionv2.4.1
Downloads1,110
Installs3
Stars⭐ 1
#clawhub
TERMINAL
clawhub install wechat-article-forge

📖 About This Skill

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 /scripts/setup.sh

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 | Publish an existing draft to WeChat | | forge preview | 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-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://: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 | Orchestrator | Three paths — check in order: (C) WeChat Official Account API via appid+appsecret (credentials at wechat_secrets_path in config.json) — preferred, most reliable; (A) OpenClaw browser tool with base64 chunking for macOS/Titan; or (B) direct CDP WebSocket for Linux/remote. Paths A+B use two-phase injection (text first, then images via clipboard blob paste). See references/browser-automation.md |

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 |

    ⚙️ 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.