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Self Media Compliance Review

Community
JuneYaooo
self-media-compliance-review

Use when auditing self-media videos, scripts, covers, subtitles, voiceover, product links, account copy, comments, articles, or publishing packages for platform violation risk; especially before final delivery or publishing after video production or clipping. Covers e-commerce product consistency, Qianchuan material quality, regulated qualifications, efficacy claims, prices, gifts, activities, and pre-selection risk across WeChat, Douyin, Kuaishou, Bilibili, Xiaohongshu, TikTok, and related platforms.

Overview

PublisherJuneYaooo
Repositoryself-media-compliance-review
Skill nameself-media-compliance-review
Stars
115
Forks
9
Bundled files
49
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 49 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by JuneYaooo on GitHub. Read the source before you install it.

Installation

Install the Self Media Compliance Review AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/JuneYaooo/self-media-compliance-review/tree/main
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/JuneYaooo/self-media-compliance-review.git \
  .claude/skills/self-media-compliance-review
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Self Media Compliance Review in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Self Media Compliance Review on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Self Media Compliance Review is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Self-Media Compliance Review

Core Rule

Run a risk-control review before public content is treated as final. This Skill does not provide legal advice, decide for a platform, or guarantee approval. Default to Chinese unless the user asks otherwise.

Missing evidence is 待核验. Never invent rules, facts, qualifications, authorizations, prices, provenance, or platform behavior. Do not return Pass when any required audible or visible region remains unreviewed.

Collect Inputs

Collect or infer:

  • target platforms and publishing context;
  • final video, script, subtitles, cover, title, caption, tags, comments, product link, CTA, and account copy;
  • account identity, audience, content intent, source ownership, promotion status, and qualifications for regulated topics;
  • reviewable evidence such as files, timecodes, frames, transcripts, screenshots, manifests, links, and authorization records.

Continue with available evidence when inputs are missing, but list every gap.

Load Only Relevant References

Always apply the universal workflow below. Then read the matching platform file:

  • 微信视频号: references/wechat-channels.md
  • 微信公众号: references/wechat-official-account.md
  • 抖音: references/douyin.md
  • 快手: references/kuaishou.md
  • B站: references/bilibili.md
  • 小红书: references/xiaohongshu.md
  • TikTok or unsupported platforms: universal workflow only; state that no dedicated local reference exists.

Read additional workflow references only when their conditions apply:

  • A local or acquired video is being reviewed: read references/video-review-workflow.md and follow its evidence coverage gate.
  • E-commerce, 带货、挂车、商品、SKU、千川、选品 or commercial promotion is involved: read references/ecommerce-workflow.md and its linked commerce rules, including douyin-ecommerce.md, qianchuan-low-quality.md, ecommerce-claims.md, and when relevant cases/ecommerce-cases.md.
  • The user explicitly requests current cases or live search: read references/live-evidence-workflow.md. Live search is off by default. Channel tiers, first working tier wins: ① the agent's own computer use and ② the agent's built-in browser tools (browser use / MCP browser) — self-check the host and start directly, without asking again; ③ opencli, first option of the ask-the-user step, and ④ TikHub (paid REST API; explicit consent before spending); ⑤ MediaCrawler (tools/mediacrawler_search.py) only when ①-④ are all unavailable — last resort, own logged-in account, flagged by platform anti-crawl in practice, bound by the frequency hard rules. The presence of a live-search channel is not authorization; search only when the user explicitly asks.
  • A serious review or JSON handoff is needed: read references/report-schema.md and use the published schemas.
  • Recent enforcement, creator discussion, account-status, or appeal context is relevant: read references/recent-cases-2025-2026.md, then the matching file under references/cases/.
  • A remote URL, OCR/ASR, TikHub, Gemini, or another multimodal model is involved: read references/video-evidence-integrity.md.

For a new platform, add a focused references/<platform>.md with scope and source date, severity and blockers, official category names, risky elements, and remediation patterns. Keep catalogs out of this file.

Local Evidence Search

Local static search may run automatically because it makes no network request and does not require live-search permission. Use tools/search_local_evidence.py for focused lookup across published references/**/*.md and docs/sources.md. Never search developer-only local/ as installation evidence.

Cite repository path, line, heading, and source type. Call results 本地静态证据. No match means only that the shipped corpus has no lexical match; it does not prove compliance or absence of platform cases.

Review Workflow

  1. Inventory every public and audible surface: video, first frame, cover, title, subtitles, voiceover, BGM, caption, stickers, comments, private-message prompts, product card, links, QR codes, and profile.
  2. Identify intent and regulated domains early, including health, medical, finance, legal services, minors, news, fundraising, gambling, drugs, devices, health food, and special medical formula food.
  3. Prepare traceable evidence. For video, use the dedicated workflow before assigning severity. For structured commerce data, run optional prechecks but confirm their 商品一致性审核 results against source evidence.
  4. Run every universal risk area below, then the selected platform and conditional references.
  5. Separate official rules, regulatory sources, media reporting, creator discussion, automated signals, and reviewer observations.
  6. Give each finding an exact evidence pointer, the matching rule or risk area, and a concrete fix. Record all unreviewed surfaces and missing proof.
  7. Apply the final coverage gate before assigning the report-level result.

Universal Risk Areas

  • Rights and identity: copyright, low-effort reuse, third-party watermarks, portrait, name, reputation, privacy, trademark, patent, and authorization.
  • Sexual or lowbrow material: nudity, body focus, sexual implication, sexual sound or text, sex jokes, and animal mating.
  • Violence or discomfort: gore, injury, death, surgery, abuse, horror, excretions, dense holes or insects, and disturbing food or animals.
  • Illegal or harmful conduct: gambling, pyramid schemes, controlled goods, illegal finance, fraud, fake cheating tools, dangerous stunts, and unsafe behavior involving minors.
  • Marketing and commerce: absolutes, unverifiable data, fake authority, inconsistent products, prices, gifts, activities or links, nonofficial sales channels, excessive insertion, missing qualifications, and unsupported claims.
  • Misinformation: old events presented as current, fabricated interviews, unknown-source stories, rumors, unlabeled synthetic incidents, and pseudoscience.
  • Inducement and diversion: coercive engagement, fake benefits, incomplete episodes, off-platform traffic, contacts, QR codes, and private funnels.
  • Public order and morals: discrimination, insults, sensationalized abnormal relationships, family abuse, and conduct that disrupts public order.
  • Production quality: unreadable or wrong subtitles, bad aspect ratio, black screens, distortion, audio gaps, audio-video mismatch, and invalid links.

Sensitivity alone is not a finding. Identify the exact visible, audible, or written element that creates the risk.

Evidence Standard

Every finding needs at least one pointer: timecode or frame, transcript line, cover/title/caption/comment/link text, screenshot description, or explicitly missing proof. Cite official category IDs or policy names when available.

Maintain a source ledger for user material, platform metadata, downloaded media, local observations, OCR/ASR, and model output. When sources disagree, explain the mismatch and possible technical causes as 待核验; do not choose a winner or accuse anyone of manipulation without independent evidence.

Creator cases and comments may reveal enforcement symptoms, but they are not binding rules. Optional live evidence belongs in its own section with provider, search terms, content IDs, dates, and limitations.

Severity

  • Blocker: clear illegal or severe platform red line, major user safety or property risk, unqualified regulated marketing, porn, gambling, fraud, unmasked severe harm, obvious unauthorized reuse, or risky diversion.
  • High: likely violation or strong enforcement risk; edit or add proof before publishing.
  • Medium: ambiguous or context-dependent risk; revise, disclose, mask, or retain stronger evidence.
  • Low: minor wording, UX, or production-quality risk.
  • Pass: no material risk found in the evidence that was fully reviewed.

An unresolved Blocker or unaccepted High means the package is not ready. If required audio, visuals, product details, qualifications, or authorization were not reviewed, the conclusion cannot be Pass.

Concrete Fixes

  • Audio: mute or replace exact ranges and update matching subtitles or cards.
  • Visual: cut, replace, crop, blur, or mask; keep risky frames off the cover and opening.
  • Claims: remove absolutes and guarantees, add a verifiable source and context, disclose marketing, and align products, prices, gifts, specifications, and activities.
  • Regulated topics: verify qualifications or remove prescriptive marketing and convert it to general, supportable information.
  • Rights and privacy: replace unlicensed material; attribution alone does not cure unauthorized use.
  • Diversion: remove coercive CTA, fake benefits, off-platform contacts, risky private-message funnels, and QR codes.

Common Failure Modes

  • Rewriting subtitles while risky speech remains audible.
  • Reviewing only a script and missing cover, opening, visual, or BGM risks.
  • Treating cross-platform public material as authorized or safe.
  • Treating detail-page claims, OCR, ASR, or model output as verified facts.
  • Calling sampled frames a full-frame review or assigning Pass from samples while audio or visual regions remain unreviewed.
  • Hiding uncertain provenance, qualifications, product consistency, or failed media acquisition instead of marking it 待核验.
  • Running commerce review without the product-consistency and Qianchuan checks.

The Skill supports 发布前合规, 选品前风险, and 文案生成前风险. Use references/ecommerce-workflow.md for their distinct scopes and references/report-schema.md for Markdown or machine-readable JSON output.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Self Media Compliance Review AI skill do?

Use when auditing self-media videos, scripts, covers, subtitles, voiceover, product links, account copy, comments, articles, or publishing packages for platform violation risk; especially before final delivery or publishing after video production or clipping. Covers e-commerce product consistency, Qianchuan material quality, regulated qualifications, efficacy claims, prices, gifts, activities, and pre-selection risk across WeChat, Douyin, Kuaishou, Bilibili, Xiaohongshu, TikTok, and related platforms.

Why use Self Media Compliance Review on TypingMind?

Because you install it once and use it with any model. Self Media Compliance Review is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Self Media Compliance Review in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/JuneYaooo/self-media-compliance-review/tree/main. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Self Media Compliance Review?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Self Media Compliance Review?

As many as you like. As long as a model supports skills, you can use Self Media Compliance Review with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Self Media Compliance Review AI skill free?

Yes. It is published on GitHub by JuneYaooo under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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