Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add jhwreal/wechat-article-pipeline-skill --skill wechat-article-pipelinegit clone --depth 1 https://github.com/jhwreal/wechat-article-pipeline-skillWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/jhwreal/wechat-article-pipeline-skill/wechat-article-pipeline)<a href="https://agentmods.dev/skills/jhwreal/wechat-article-pipeline-skill/wechat-article-pipeline"><img src="https://agentmods.dev/badge/skills/jhwreal/wechat-article-pipeline-skill/wechat-article-pipeline/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/jhwreal/wechat-article-pipeline-skill/wechat-article-pipeline"><img src="https://agentmods.dev/badge/skills/jhwreal/wechat-article-pipeline-skill/wechat-article-pipeline.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00054 | $0.02275 |
| Opus 5 | $0.00027 | $0.01137 |
| Sonnet 5 | $0.00011 | $0.00455 |
| Haiku 4.5 | $0.00005 | $0.00228 |
Grade A, and why
wechat-article-pipeline scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured today.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 155 lines — stays where its author put it; the contents beside it link to each section on GitHub.
WeChat Article Pipeline
Produce a complete local article package; run delivery only when requested.
Core Decisions
- When the user asks to annotate a term, follow annotations.md: use “（注1）” in the text and numbered explanations in a final appendix.
- Before drafting or revising, read writing-donts.md. Before delivery, repackaging, or publishing, check the current article against the prohibited expressions and writing behaviors and follow its contextual scope and user-text preservation rules.
- Use this skill for writing, packaging, formatting, or polishing a WeChat/公众号 article.
- The first Markdown H1 is the canonical title; rename it there and require it.
- If the user says "打开秘书模式", enable it for this request only and read its section in style-guide.md. Do not infer or mention it unless asked.
- Treat rough ideas and follow-ups as briefs under workflow.md; ask only when ambiguity changes delivery.
- If the user asks for "不配图", "只排版", "直接格式化", or similar — or the runtime cannot generate images (say so) — use the no-image path.
- If the user asks to补图, continue, or fix missing assets, use the missing-image path and do not rebuild finished images.
- If the user asks to导入草稿箱, create a WeChat draft through official APIs only. Never use browser automation or private
mp.weixin.qq.comendpoints for delivery. - Toutiao: use Computer Use to operate the user's real Chrome end to end and follow publishing-toutiao.md. Do not use Browser/Chrome browser automation, Playwright, CDP, DOM evaluation, or background tab objects for any Toutiao UI step.
- Toutiao publish authorization: a user-authored instruction to “发头条”, “发布头条”, or schedule a Toutiao post is already the confirmation to submit that same content to Toutiao at the stated time. Do not ask for a second publish confirmation in the same workflow; pause only when a material choice is missing or changed, or for CAPTCHA, authentication, or a platform hard blocker.
- Xiaohongshu: use Chrome + Computer Use and publishing-xiaohongshu.md.
- Three-platform sync: read publishing-three-platform.md, initialize its state, then create WeChat → Toutiao → Xiaohongshu drafts.
What ships with it
40 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- .env.example 513 B
- agents/openai.yaml 344 B
- assets/templates/wechat-md-workbench.template.v3.html 103 KB
- assets/workbench-save-controller.js 2.5 KB runs code
- references/annotations.md 1.8 KB
- references/forbidden-expressions.md 224 B
- references/image-production.md 3.4 KB
- references/image-rules.json 8.5 KB
- references/job-schema.md 8.4 KB
- references/platform-adapters.json 1.6 KB
- references/publishing-three-platform.md 2.7 KB
- references/publishing-toutiao.md 22 KB
- references/publishing-xiaohongshu.md 10 KB
- references/publishing.md 14 KB
- references/style-guide.md 3.6 KB
- references/workflow.md 5.5 KB
- references/writing-donts.md 3.9 KB
- scripts/article_core.py 1.2 KB runs code
- scripts/atomic_files.py 2.4 KB runs code
- scripts/build_wechat_article_workbench.py 19 KB runs code
- scripts/doctor_wechat_article_skill.py 5.7 KB runs code
- scripts/image_jobs_contract.py 14 KB runs code
- scripts/image_planning_catalog.py 11 KB runs code
- scripts/make_wechat_article_image_jobs.py 53 KB runs code
- scripts/make_wechat_publish_manifest.py 28 KB runs code
- scripts/mark_wechat_article_focus.py 9.7 KB runs code
- scripts/package_wechat_article_bundle.py 31 KB runs code
- scripts/platform_assets.py 2.3 KB runs code
- scripts/platform_delivery_state.py 7.0 KB runs code
- scripts/postprocess_wechat_article.py 13 KB runs code
- scripts/publish_run_state.py 2.0 KB runs code
- scripts/publish_wechat_api.py 63 KB runs code
- scripts/release_info.py 3.1 KB runs code
- scripts/serve_wechat_workbench.py 51 KB runs code
- scripts/verify_wechat_article_package.py 9.5 KB runs code
- scripts/wechat_account_config.py 10 KB runs code
- scripts/workbench_document.py 161 B runs code
- tests/test_publish_wechat_api.py 1.7 KB runs code
- tests/test_serve_wechat_workbench.py 40 KB runs code
- VERSION 6 B
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- today Changed · +2 lines 4dc56b877628
- 7d ago Changed · +1 lines 02290980d9fe
- 11d ago First seen · 152 lines · 54 tokens per session scan A 5a766fbfbb9d
wechat-article-pipeline is a skill published in the GitHub repository jhwreal/wechat-article-pipeline-skill (21 stars, last pushed today), licensed MIT. It adds 54 tokens to every session and 2,275 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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