# Viral Title > Generate high-potential viral title candidates for content across WeChat public account articles, X/Twitter posts, YouTube videos, Bilibili videos, and similar content platforms. Use when the user needs headline/title ideation, batch title generation, platform-specific title adaptation, title-library reuse, title selection, title scoring, or a reusable title workflow based on universal formulas plus separated platform methods such as 公众号, X, YouTube, and B站. - Skill: `kangarooking/viral-title` (Agent Skill, multi-file: 28 files) - Install (CLI): `npx skillmds@latest add kangarooking/viral-title` - Raw SKILL.md: https://api.skillmd.com/api/skills/kangarooking/viral-title/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Productivity - Author: kangarooking (https://skillmd.com/u/kangarooking) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/kangarooking/viral-title --- # Viral Title ## Overview Use this skill to generate and select viral title candidates. The current version implements Phase 1 universal methodology, WeChat public-account title reuse, X/Twitter hook generation, YouTube title-thumbnail packaging, Bilibili title-cover-tag packaging, and a lightweight evolution loop. Phase 1 must generate 30 titles: 10 universal formulas x 3 variants per formula. Then score the candidates and recommend the single best title. ## Workflow 1. Clarify only if the content is too vague to title. 2. Identify the core material: - topic or content summary - target audience - platform if provided - strongest value, conflict, novelty, or emotional hook - hard facts, numbers, names, examples, and constraints that must remain true 3. Read `references/phase1-universal-methodology.md`. 4. If the user names a platform, load the matching platform reference from `references/platforms/`. 5. Read `references/evolution/promoted-rules.md` and `references/evolution/anti-patterns.md`. 6. Generate exactly 3 titles for each of the 10 Phase 1 formulas. 7. If a platform reference is implemented, generate platform-tuned candidates after the universal batch when the user asks for platform-specific titles. 8. If the user asks to reuse proven titles or says "套用标题库", retrieve relevant examples instead of loading full libraries. 9. Score the candidate pool with the Phase 1 scoring rubric plus any platform-specific rules. 10. Select one best title and briefly explain why. 11. End every substantial title-generation response with the feedback prompt from `Feedback Hook`. 12. If the user replies with a selected title, edit, rating, or critique, log it with `scripts/log_feedback.py`. ## Platform Routing Use one platform file at a time: | User says | Platform file | Status | |---|---|---| | 公众号, 微信公众号, WeChat article | `references/platforms/wechat-public-account.md` | Implemented | | X, Twitter, 推特 | `references/platforms/x.md` | Implemented | | YouTube, 油管 | `references/platforms/youtube.md` | Implemented | | B站, Bilibili | `references/platforms/bilibili.md` | Implemented | For title-library reuse, load only the current platform's library: - `references/title-library/wechat-public-account-hot-titles.md` for summary and examples. - `references/title-library/wechat-ai-curated-hot-titles.md` for user-curated AI/tech viral title patterns and hotspot-dependent examples. - `references/title-library/wechat-public-account-hot-titles.json` only when many source titles are needed for matching or adaptation. - `references/title-library/x-hot-hooks.md` for X/Twitter hook skeletons and reusable mechanisms. - `references/title-library/youtube-hot-titles.md` for YouTube title-thumbnail packaging skeletons. - `references/title-library/bilibili-hot-titles.md` for B站 title-cover-tag packaging skeletons. For token-efficient retrieval, prefer: ```bash python3 scripts/retrieve_title_examples.py --platform --query "" --mechanism "" --limit 10 ``` ## Evolution Loop Use the loop only when logging, feedback, review, or evaluation is useful. Do not load historical logs during ordinary title generation. | Need | Command | |---|---| | Log a title session | `python3 scripts/log_title_session.py --platform --topic "..." --recommended-title "..."` | Log user feedback | `python3 scripts/log_feedback.py --session-id "..." --platform --selected-title "..." --user-edit "..." --rating 5 --feedback "..."` | Retrieve title examples | `python3 scripts/retrieve_title_examples.py --platform --query "AI Agent speed" --limit 10` | | Review recent learning | `python3 scripts/analyze_feedback.py --recent 20` | | Run seed evals | `python3 scripts/run_title_evals.py --evals references/evals/bilibili-ai-title-evals.json --case-id agent-speed-step37-bilibili --titles-json titles.json` | Follow `meta/RULES.md`: append logs automatically, but require user confirmation before modifying core methodology or promoting rules. ## Feedback Hook After every substantial title-generation response, append exactly one short feedback prompt: ```markdown **反馈一下** 你最终会用哪个标题？如果你改了标题，把最终版发我；也可以给 1-5 分。我会用这次反馈优化下次标题。 ``` Do not ask for feedback after pure research, implementation, explanation, or tiny one-off edits. When feedback arrives: 1. Treat a chosen title, edited title, rating, or critique as evolution feedback. 2. Log feedback with the current platform when known. 3. Prefer `user_edit` over `selected_title` when both are provided. 4. Store titles, platform, rating, tags, and concise feedback only. Do not store full unpublished drafts by default. 5. After every 5-20 feedback records, run `scripts/analyze_feedback.py --recent 20` and summarize learning candidates. 6. Do not promote candidate observations into methodology files without user confirmation. ## Phase 1 Output Format Use this structure: ```markdown **内容判断** 核心对象： 目标人群： 主要点击理由： 真实约束： **第一阶段：通用方法论标题池** 1. 结果承诺型 - ... - ... - ... 2. 问题解决型 - ... - ... - ... [continue through all 10 formulas] **最佳标题** 标题： 理由： **备选 Top 3** 1. ... 2. ... 3. ... **反馈一下** 你最终会用哪个标题？如果你改了标题，把最终版发我；也可以给 1-5 分。我会用这次反馈优化下次标题。 ``` ## Quality Rules - Generate exactly 30 Phase 1 candidates unless the user explicitly asks for fewer. - Each formula must contribute exactly 3 titles. - Do not fabricate unsupported facts, names, numbers, or results. - Prefer concrete nouns, visible stakes, and reader-facing benefits over abstract claims. - Keep titles platform-neutral in Phase 1. Do not overfit to WeChat, X, YouTube, or Bilibili unless the user asks. - Use Chinese titles by default when the user writes in Chinese. Use English when the source content or user request is English. - Avoid empty hype such as "震惊", "必看", "全网最强", unless the user's style explicitly asks for it. - Make the final recommendation decisive. Do not say "it depends" after scoring. ## Future Extension Points - Add deeper live-sampled libraries for each platform as more user-approved data sources become available. - Split Bilibili libraries by partition if a single library becomes too broad. - Load only the platform or library reference needed for the current user request.