Creator · kangarooking
Last updated · Sep 1, 2026
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 adap
Creator · kangarooking
Last updated · Sep 1, 2026
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 adap
Creator · kangarooking
Last updated · Sep 1, 2026
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 adap
Creator · kangarooking
Last updated · Sep 1, 2026
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 adap
Do not auto-install
Install targets
Codex install prompt
Install the "viral-title" agent skill from https://github.com/kangarooking/kangarooking-skills/tree/main/viral-title. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: 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. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {"event_id":"install_<unique-id>","skill_slug":"kangarooking-viral-title","task":"Install viral-title","agent":"codex","outcome":"success","install_used":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.Supply asset profile
Deep research, source comparison, literature review, RAG, knowledge search, and reports.
Scenario
Research agents
I need my agent to research a topic, compare sources, and produce a concise report.
Agent fit
Claude Code + CLI + Codex
Codex, Claude Code, Cursor, CLI, or custom agents.
Install
Ready
npx skills add kangarooking/kangarooking-skills --skill viral-title
Maintenance
fresh
6d since push
Risk
Needs review
License is unclear
GitHub quality
568
69/100 Quality · 65/100 Trust
Coverage tags
Review notes
License is unclear · Dependency or permission surface needs review
Agent adoption scorecard
These scores combine public repository metadata, OpenAgentSkill review signals, maintenance freshness, and install readiness. They are a shortlist signal, not a replacement for human review.
Quality
PromisingUseful candidate, but compare it with alternatives before adopting.
Trust
Do not auto-installTrust Score v5 found insufficient evidence for agent installation. Treat this as discovery material, not an executable recommendation.
Audit
Needs reviewA machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
OpenAgentSkill Trust Score v5
Choose a stronger alternative or inspect the source manually before any install attempt.
Stars
568 GitHub stars
Repo activity
568 stars, 94 forks
Maintenance
6d since push
License
Unknown
Install
npx skills add kangarooking/kangarooking-skills --skill viral-title
Install safety
Agent-readable metadata
Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.
Suited tasks
Suited agents
Install decision
Trust and risk
Outcome loop
Install command
npx skills add kangarooking/kangarooking-skills --skill viral-titleDo not use when
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npx skills add Imbad0202/academic-research-skills
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Agent safety v2
This skill should not be selected by an agent without explicit human security review.
Do not auto-install. Inspect the source, dependencies, and permission surface first.
high
Skill metadata references terminal, CLI, shell, subprocess, or command execution workflows.
medium
Skill likely fetches remote pages, APIs, repositories, or external services.
medium
Skill may read or write project files, documents, generated artifacts, or local workspace state.
high
Skill metadata references credentials, tokens, environment variables, or secret-bearing workflows.
Agent resolve plan
The Resolve API returns the selected skill, alternatives, safety policy, audit notes, install target, and copy-paste prompt an agent can follow without scraping this page.
Open JSON
/api/agent/resolve?task=Use%20viral-title%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20viral-title%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/kangarooking-viral-title/install
Agent should check
Copy prompt
Task: Use viral-title in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20viral-title%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/kangarooking-viral-title/install
Install command: npx skills add kangarooking/kangarooking-skills --skill viral-title
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.Agent handoff
Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.
Install handoff
/api/skills/kangarooking-viral-title/install
LLM text format
/api/skills/kangarooking-viral-title/install?format=text
Find alternatives
/api/skills/search?q=viral-title&limit=3
Agent prompt
Use viral-title for this task. Review https://www.openagentskill.com/api/skills/kangarooking-viral-title/install, then install with: npx skills add kangarooking/kangarooking-skills --skill viral-titleRegistry metadata
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
Manifest
/api/registry/manifest/kangarooking-viral-title
LLM text
/api/registry/manifest/kangarooking-viral-title?format=text
Install alias
/api/registry/install/kangarooking-viral-title
Recommend
/api/registry/recommend?task=Use%20viral-title%20in%20an%20agent%20workflow&limit=3
Agent fit
Research agents
Use-case tags
Platforms
Claude Code
Audit report
A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
Agent decision cockpit
Shortlist this skill and compare it with close alternatives before production adoption.
Role in stack
Companion skill
Primary fit
Research agents
Trust label
Strong shortlist
Install path
Command ready
Use when
Evidence
review first
Implementation path
Trust profile
Trust Score v5 found insufficient evidence for agent installation. Treat this as discovery material, not an executable recommendation.
GitHub adoption
INFO568 GitHub stars
Stars/forks activity
INFO568 stars, 94 forks; issue activity unavailable in current metadata
Recent maintenance
PASS6d since push
License clarity
CHECKUnknown
Good signals
Review before install
Recommended action
Choose a stronger alternative or inspect the source manually before any install attempt.
Quality profile
Useful candidate, but compare it with alternatives before adopting.
Workflow fit
Investigate faster
I need my agent to research a topic, compare sources, and produce a concise report.
Publish consistently
I need my agent to turn research and product updates into useful content drafts.
Search private knowledge
I need my agent to build a RAG workflow over documents and retrieve reliable context.
Workflow fit
Find, compare, and synthesize
A workflow for agents that gather sources, compare claims, summarize long material, and draft useful research briefs.
Turn skills into distribution
A workflow for turning newly indexed skills into SEO briefs, social drafts, comparison pages, and reusable publishing workflows.
Ingest, retrieve, and cite
A workflow for document-heavy agents that ingest files, create searchable knowledge, retrieve relevant context, and answer with grounded sources.
Alternative shortlist
Similar skills that may fit this task.
Generate original one-ink or controlled two-ink editorial images from any theme, sentence, article idea, object, or reference photo. Always use this skill when the user asks for 单色海报、双色印刷、单色调视觉、蓝色/绿色孔版印刷、risograph、网点照片、复古或当代编辑排版、zine poster, monochrome editorial poster, duotone print, or asks to use the mono-color style. It uses an adaptive white, gray, or pale-beige substrate, no more than two printing inks, active negative space, terse human language, and strong serif/grotesk/mono typography without making retro styling the default or copying a source composition, wording, logo, or artwork. Produce both the final generation prompt and the generated raster image unless the user explicitly asks for prompt only.
Research the last 30 days across Reddit, X, YouTube, Hacker News, Polymarket, GitHub, and the web, then synthesize a grounded brief for an AI agent.
Academic Research Skills for Claude Code: research → write → review → revise → finalize
Run autonomous deep research over web and local sources
--- name: viral-title description: 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. ---
# 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 <wechat|x|youtube|bilibili> --query "<topic words>" --mechanism "<optional 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 <platform> --topic "..." --recommended-title "..."` | Log user feedback | `python3 scripts/log_feedback.py --session-id "..." --platform <platform> --selected-title "..." --user-edit "..." --rating 5 --feedback "..."` | Retrieve title examples | `python3 scripts/retrieve_title_examples.py --platform <wechat|x|youtube|bilibili> --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.
Decision snapshot
568 GitHub stars
Audit
Install and adoption review
Agent-proven evidence
Outcome reports after resolve, review, install, and one narrow run.
No agent outcome data yet. The first agent run can report success, setup needs, risk blocks, failure, or not-relevant through /api/agent/outcome.
Install
Free and open source. Review the report before installing into production agents.
Growth loop
Scenario-led draft for viral-title, ready for a manual X post.
A practical pick for design or creative work: viral-title: Generate high-potential viral title candidates for content across WeChat public account articles, X/Twitter posts, YouTube... 568 stars https://www.openagentskill.com/skills/kangarooking-viral-title?ref=x
Listing + install path for viral-title: https://www.openagentskill.com/skills/kangarooking-viral-title?ref=x Install: npx skills add kangarooking/kangarooking-skills --skill viral-title
Listing source
This listing was indexed from public sources and is not marked official until a maintainer claim is approved.
Attribution links to the public repository or creator profile. Creators can claim the listing to update ownership signals.
Claim this skillOwner claim
This Registry indexed listing is attributed to kangarooking but is not marked official yet. Claim it to add a verified owner signal and make future launch, install, and audit updates easier to trust.
Creator backlink kit
Show the canonical listing, current trust and audit signals, and real Agent-Proven evidence where developers evaluate the repository.
[](https://www.openagentskill.com/skills/kangarooking-viral-title?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/kangarooking-viral-title?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/kangarooking-viral-title/audit)
[](https://www.openagentskill.com/skills/kangarooking-viral-title?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)kangarooking
@kangarooking
Share whether this skill looks useful for your agent workflow. Aggregated feedback improves rankings over time.
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Install targets
Codex install prompt
Install the "viral-title" agent skill from https://github.com/kangarooking/kangarooking-skills/tree/main/viral-title. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: 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. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {"event_id":"install_<unique-id>","skill_slug":"kangarooking-viral-title","task":"Install viral-title","agent":"codex","outcome":"success","install_used":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.Supply asset profile
Deep research, source comparison, literature review, RAG, knowledge search, and reports.
Scenario
Research agents
I need my agent to research a topic, compare sources, and produce a concise report.
Agent fit
Claude Code + CLI + Codex
Codex, Claude Code, Cursor, CLI, or custom agents.
Install
Ready
npx skills add kangarooking/kangarooking-skills --skill viral-title
Maintenance
fresh
6d since push
Risk
Needs review
License is unclear
GitHub quality
568
69/100 Quality · 65/100 Trust
Coverage tags
Review notes
License is unclear · Dependency or permission surface needs review
Agent adoption scorecard
These scores combine public repository metadata, OpenAgentSkill review signals, maintenance freshness, and install readiness. They are a shortlist signal, not a replacement for human review.
Quality
PromisingUseful candidate, but compare it with alternatives before adopting.
Trust
Do not auto-installTrust Score v5 found insufficient evidence for agent installation. Treat this as discovery material, not an executable recommendation.
Audit
Needs reviewA machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
OpenAgentSkill Trust Score v5
Choose a stronger alternative or inspect the source manually before any install attempt.
Stars
568 GitHub stars
Repo activity
568 stars, 94 forks
Maintenance
6d since push
License
Unknown
Install
npx skills add kangarooking/kangarooking-skills --skill viral-title
Install safety
Agent-readable metadata
Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.
Suited tasks
Suited agents
Install decision
Trust and risk
Outcome loop
Install command
npx skills add kangarooking/kangarooking-skills --skill viral-titleDo not use when
Alternative
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Agent safety v2
This skill should not be selected by an agent without explicit human security review.
Do not auto-install. Inspect the source, dependencies, and permission surface first.
high
Skill metadata references terminal, CLI, shell, subprocess, or command execution workflows.
medium
Skill likely fetches remote pages, APIs, repositories, or external services.
medium
Skill may read or write project files, documents, generated artifacts, or local workspace state.
high
Skill metadata references credentials, tokens, environment variables, or secret-bearing workflows.
Agent resolve plan
The Resolve API returns the selected skill, alternatives, safety policy, audit notes, install target, and copy-paste prompt an agent can follow without scraping this page.
Open JSON
/api/agent/resolve?task=Use%20viral-title%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20viral-title%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/kangarooking-viral-title/install
Agent should check
Copy prompt
Task: Use viral-title in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20viral-title%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/kangarooking-viral-title/install
Install command: npx skills add kangarooking/kangarooking-skills --skill viral-title
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.Agent handoff
Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.
Install handoff
/api/skills/kangarooking-viral-title/install
LLM text format
/api/skills/kangarooking-viral-title/install?format=text
Find alternatives
/api/skills/search?q=viral-title&limit=3
Agent prompt
Use viral-title for this task. Review https://www.openagentskill.com/api/skills/kangarooking-viral-title/install, then install with: npx skills add kangarooking/kangarooking-skills --skill viral-titleRegistry metadata
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
Manifest
/api/registry/manifest/kangarooking-viral-title
LLM text
/api/registry/manifest/kangarooking-viral-title?format=text
Install alias
/api/registry/install/kangarooking-viral-title
Recommend
/api/registry/recommend?task=Use%20viral-title%20in%20an%20agent%20workflow&limit=3
Agent fit
Research agents
Use-case tags
Platforms
Claude Code
Audit report
A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
Agent decision cockpit
Shortlist this skill and compare it with close alternatives before production adoption.
Role in stack
Companion skill
Primary fit
Research agents
Trust label
Strong shortlist
Install path
Command ready
Use when
Evidence
review first
Implementation path
Trust profile
Trust Score v5 found insufficient evidence for agent installation. Treat this as discovery material, not an executable recommendation.
GitHub adoption
INFO568 GitHub stars
Stars/forks activity
INFO568 stars, 94 forks; issue activity unavailable in current metadata
Recent maintenance
PASS6d since push
License clarity
CHECKUnknown
Good signals
Review before install
Recommended action
Choose a stronger alternative or inspect the source manually before any install attempt.
Quality profile
Useful candidate, but compare it with alternatives before adopting.
Workflow fit
Investigate faster
I need my agent to research a topic, compare sources, and produce a concise report.
Publish consistently
I need my agent to turn research and product updates into useful content drafts.
Search private knowledge
I need my agent to build a RAG workflow over documents and retrieve reliable context.
Workflow fit
Find, compare, and synthesize
A workflow for agents that gather sources, compare claims, summarize long material, and draft useful research briefs.
Turn skills into distribution
A workflow for turning newly indexed skills into SEO briefs, social drafts, comparison pages, and reusable publishing workflows.
Ingest, retrieve, and cite
A workflow for document-heavy agents that ingest files, create searchable knowledge, retrieve relevant context, and answer with grounded sources.
Alternative shortlist
Similar skills that may fit this task.
Generate original one-ink or controlled two-ink editorial images from any theme, sentence, article idea, object, or reference photo. Always use this skill when the user asks for 单色海报、双色印刷、单色调视觉、蓝色/绿色孔版印刷、risograph、网点照片、复古或当代编辑排版、zine poster, monochrome editorial poster, duotone print, or asks to use the mono-color style. It uses an adaptive white, gray, or pale-beige substrate, no more than two printing inks, active negative space, terse human language, and strong serif/grotesk/mono typography without making retro styling the default or copying a source composition, wording, logo, or artwork. Produce both the final generation prompt and the generated raster image unless the user explicitly asks for prompt only.
Research the last 30 days across Reddit, X, YouTube, Hacker News, Polymarket, GitHub, and the web, then synthesize a grounded brief for an AI agent.
Academic Research Skills for Claude Code: research → write → review → revise → finalize
Run autonomous deep research over web and local sources
--- name: viral-title description: 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. ---
# 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 <wechat|x|youtube|bilibili> --query "<topic words>" --mechanism "<optional 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 <platform> --topic "..." --recommended-title "..."` | Log user feedback | `python3 scripts/log_feedback.py --session-id "..." --platform <platform> --selected-title "..." --user-edit "..." --rating 5 --feedback "..."` | Retrieve title examples | `python3 scripts/retrieve_title_examples.py --platform <wechat|x|youtube|bilibili> --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.
Decision snapshot
568 GitHub stars
Audit
Install and adoption review
Agent-proven evidence
Outcome reports after resolve, review, install, and one narrow run.
No agent outcome data yet. The first agent run can report success, setup needs, risk blocks, failure, or not-relevant through /api/agent/outcome.
Install
Free and open source. Review the report before installing into production agents.
Growth loop
Scenario-led draft for viral-title, ready for a manual X post.
A practical pick for design or creative work: viral-title: Generate high-potential viral title candidates for content across WeChat public account articles, X/Twitter posts, YouTube... 568 stars https://www.openagentskill.com/skills/kangarooking-viral-title?ref=x
Listing + install path for viral-title: https://www.openagentskill.com/skills/kangarooking-viral-title?ref=x Install: npx skills add kangarooking/kangarooking-skills --skill viral-title
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[](https://www.openagentskill.com/skills/kangarooking-viral-title?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
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[](https://www.openagentskill.com/skills/kangarooking-viral-title?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)kangarooking
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mono-color
Generate original one-ink or controlled two-ink editorial images from any theme, sentence, article idea, object, or reference photo. Always use this skill when the user asks for 单色海报、双色印刷、单色调视觉、蓝色/绿色孔版印刷、risograph、网点照片、复古或当代编辑排版、zine poster, monochrome editorial poster, duotone print, or asks to use the mono-color style. It uses an adaptive white, gray, or pale-beige substrate, no more than two printing inks, active negative space, terse human language, and strong serif/grotesk/mono typography without making retro styling the default or copying a source composition, wording, logo, or artwork. Produce both the final generation prompt and the generated raster image unless the user explicitly asks for prompt only.
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Install targets
Codex install prompt
Install the "viral-title" agent skill from https://github.com/kangarooking/kangarooking-skills/tree/main/viral-title. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: 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. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {"event_id":"install_<unique-id>","skill_slug":"kangarooking-viral-title","task":"Install viral-title","agent":"codex","outcome":"success","install_used":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.Supply asset profile
Deep research, source comparison, literature review, RAG, knowledge search, and reports.
Scenario
Research agents
I need my agent to research a topic, compare sources, and produce a concise report.
Agent fit
Claude Code + CLI + Codex
Codex, Claude Code, Cursor, CLI, or custom agents.
Install
Ready
npx skills add kangarooking/kangarooking-skills --skill viral-title
Maintenance
fresh
6d since push
Risk
Needs review
License is unclear
GitHub quality
568
69/100 Quality · 65/100 Trust
Coverage tags
Review notes
License is unclear · Dependency or permission surface needs review
Agent adoption scorecard
These scores combine public repository metadata, OpenAgentSkill review signals, maintenance freshness, and install readiness. They are a shortlist signal, not a replacement for human review.
Quality
PromisingUseful candidate, but compare it with alternatives before adopting.
Trust
Do not auto-installTrust Score v5 found insufficient evidence for agent installation. Treat this as discovery material, not an executable recommendation.
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Needs reviewA machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
OpenAgentSkill Trust Score v5
Choose a stronger alternative or inspect the source manually before any install attempt.
Stars
568 GitHub stars
Repo activity
568 stars, 94 forks
Maintenance
6d since push
License
Unknown
Install
npx skills add kangarooking/kangarooking-skills --skill viral-title
Install safety
Agent-readable metadata
Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.
Suited tasks
Suited agents
Install decision
Trust and risk
Outcome loop
Install command
npx skills add kangarooking/kangarooking-skills --skill viral-titleDo not use when
Alternative
1.9K Stars
npx skills add yanliudesign/mono-color-skill --skill mono-color
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61.0K Stars
npx skills add mvanhorn/last30days-skill -g
Alternative
38.4K Stars
npx skills add Imbad0202/academic-research-skills
Alternative
28.0K Stars
npx skills add assafelovic/gpt-researcher
Agent safety v2
This skill should not be selected by an agent without explicit human security review.
Do not auto-install. Inspect the source, dependencies, and permission surface first.
high
Skill metadata references terminal, CLI, shell, subprocess, or command execution workflows.
medium
Skill likely fetches remote pages, APIs, repositories, or external services.
medium
Skill may read or write project files, documents, generated artifacts, or local workspace state.
high
Skill metadata references credentials, tokens, environment variables, or secret-bearing workflows.
Agent resolve plan
The Resolve API returns the selected skill, alternatives, safety policy, audit notes, install target, and copy-paste prompt an agent can follow without scraping this page.
Open JSON
/api/agent/resolve?task=Use%20viral-title%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20viral-title%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/kangarooking-viral-title/install
Agent should check
Copy prompt
Task: Use viral-title in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20viral-title%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/kangarooking-viral-title/install
Install command: npx skills add kangarooking/kangarooking-skills --skill viral-title
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.Agent handoff
Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.
Install handoff
/api/skills/kangarooking-viral-title/install
LLM text format
/api/skills/kangarooking-viral-title/install?format=text
Find alternatives
/api/skills/search?q=viral-title&limit=3
Agent prompt
Use viral-title for this task. Review https://www.openagentskill.com/api/skills/kangarooking-viral-title/install, then install with: npx skills add kangarooking/kangarooking-skills --skill viral-titleRegistry metadata
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
Manifest
/api/registry/manifest/kangarooking-viral-title
LLM text
/api/registry/manifest/kangarooking-viral-title?format=text
Install alias
/api/registry/install/kangarooking-viral-title
Recommend
/api/registry/recommend?task=Use%20viral-title%20in%20an%20agent%20workflow&limit=3
Agent fit
Research agents
Use-case tags
Platforms
Claude Code
Audit report
A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
Agent decision cockpit
Shortlist this skill and compare it with close alternatives before production adoption.
Role in stack
Companion skill
Primary fit
Research agents
Trust label
Strong shortlist
Install path
Command ready
Use when
Evidence
review first
Implementation path
Trust profile
Trust Score v5 found insufficient evidence for agent installation. Treat this as discovery material, not an executable recommendation.
GitHub adoption
INFO568 GitHub stars
Stars/forks activity
INFO568 stars, 94 forks; issue activity unavailable in current metadata
Recent maintenance
PASS6d since push
License clarity
CHECKUnknown
Good signals
Review before install
Recommended action
Choose a stronger alternative or inspect the source manually before any install attempt.
Quality profile
Useful candidate, but compare it with alternatives before adopting.
Workflow fit
Investigate faster
I need my agent to research a topic, compare sources, and produce a concise report.
Publish consistently
I need my agent to turn research and product updates into useful content drafts.
Search private knowledge
I need my agent to build a RAG workflow over documents and retrieve reliable context.
Workflow fit
Find, compare, and synthesize
A workflow for agents that gather sources, compare claims, summarize long material, and draft useful research briefs.
Turn skills into distribution
A workflow for turning newly indexed skills into SEO briefs, social drafts, comparison pages, and reusable publishing workflows.
Ingest, retrieve, and cite
A workflow for document-heavy agents that ingest files, create searchable knowledge, retrieve relevant context, and answer with grounded sources.
Alternative shortlist
Similar skills that may fit this task.
Generate original one-ink or controlled two-ink editorial images from any theme, sentence, article idea, object, or reference photo. Always use this skill when the user asks for 单色海报、双色印刷、单色调视觉、蓝色/绿色孔版印刷、risograph、网点照片、复古或当代编辑排版、zine poster, monochrome editorial poster, duotone print, or asks to use the mono-color style. It uses an adaptive white, gray, or pale-beige substrate, no more than two printing inks, active negative space, terse human language, and strong serif/grotesk/mono typography without making retro styling the default or copying a source composition, wording, logo, or artwork. Produce both the final generation prompt and the generated raster image unless the user explicitly asks for prompt only.
Research the last 30 days across Reddit, X, YouTube, Hacker News, Polymarket, GitHub, and the web, then synthesize a grounded brief for an AI agent.
Academic Research Skills for Claude Code: research → write → review → revise → finalize
Run autonomous deep research over web and local sources
--- name: viral-title description: 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. ---
# 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 <wechat|x|youtube|bilibili> --query "<topic words>" --mechanism "<optional 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 <platform> --topic "..." --recommended-title "..."` | Log user feedback | `python3 scripts/log_feedback.py --session-id "..." --platform <platform> --selected-title "..." --user-edit "..." --rating 5 --feedback "..."` | Retrieve title examples | `python3 scripts/retrieve_title_examples.py --platform <wechat|x|youtube|bilibili> --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.
Decision snapshot
568 GitHub stars
Audit
Install and adoption review
Agent-proven evidence
Outcome reports after resolve, review, install, and one narrow run.
No agent outcome data yet. The first agent run can report success, setup needs, risk blocks, failure, or not-relevant through /api/agent/outcome.
Install
Free and open source. Review the report before installing into production agents.
Growth loop
Scenario-led draft for viral-title, ready for a manual X post.
A practical pick for design or creative work: viral-title: Generate high-potential viral title candidates for content across WeChat public account articles, X/Twitter posts, YouTube... 568 stars https://www.openagentskill.com/skills/kangarooking-viral-title?ref=x
Listing + install path for viral-title: https://www.openagentskill.com/skills/kangarooking-viral-title?ref=x Install: npx skills add kangarooking/kangarooking-skills --skill viral-title
Listing source
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Attribution links to the public repository or creator profile. Creators can claim the listing to update ownership signals.
Claim this skillOwner claim
This Registry indexed listing is attributed to kangarooking but is not marked official yet. Claim it to add a verified owner signal and make future launch, install, and audit updates easier to trust.
Creator backlink kit
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[](https://www.openagentskill.com/skills/kangarooking-viral-title?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/kangarooking-viral-title?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
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[](https://www.openagentskill.com/skills/kangarooking-viral-title?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)kangarooking
@kangarooking
Share whether this skill looks useful for your agent workflow. Aggregated feedback improves rankings over time.
Do not auto-install
mono-color
Generate original one-ink or controlled two-ink editorial images from any theme, sentence, article idea, object, or reference photo. Always use this skill when the user asks for 单色海报、双色印刷、单色调视觉、蓝色/绿色孔版印刷、risograph、网点照片、复古或当代编辑排版、zine poster, monochrome editorial poster, duotone print, or asks to use the mono-color style. It uses an adaptive white, gray, or pale-beige substrate, no more than two printing inks, active negative space, terse human language, and strong serif/grotesk/mono typography without making retro styling the default or copying a source composition, wording, logo, or artwork. Produce both the final generation prompt and the generated raster image unless the user explicitly asks for prompt only.
1.9K StarsLast30days Skill
Research the last 30 days across Reddit, X, YouTube, Hacker News, Polymarket, GitHub, and the web, then synthesize a grounded brief for an AI agent.
61.0K StarsAcademic Research Skills
Academic Research Skills for Claude Code: research → write → review → revise → finalize
38.4K StarsGPT Researcher
Run autonomous deep research over web and local sources
28.0K StarsDo not auto-install
Install targets
Codex install prompt
Install the "viral-title" agent skill from https://github.com/kangarooking/kangarooking-skills/tree/main/viral-title. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: 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. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {"event_id":"install_<unique-id>","skill_slug":"kangarooking-viral-title","task":"Install viral-title","agent":"codex","outcome":"success","install_used":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.Supply asset profile
Deep research, source comparison, literature review, RAG, knowledge search, and reports.
Scenario
Research agents
I need my agent to research a topic, compare sources, and produce a concise report.
Agent fit
Claude Code + CLI + Codex
Codex, Claude Code, Cursor, CLI, or custom agents.
Install
Ready
npx skills add kangarooking/kangarooking-skills --skill viral-title
Maintenance
fresh
6d since push
Risk
Needs review
License is unclear
GitHub quality
568
69/100 Quality · 65/100 Trust
Coverage tags
Review notes
License is unclear · Dependency or permission surface needs review
Agent adoption scorecard
These scores combine public repository metadata, OpenAgentSkill review signals, maintenance freshness, and install readiness. They are a shortlist signal, not a replacement for human review.
Quality
PromisingUseful candidate, but compare it with alternatives before adopting.
Trust
Do not auto-installTrust Score v5 found insufficient evidence for agent installation. Treat this as discovery material, not an executable recommendation.
Audit
Needs reviewA machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
OpenAgentSkill Trust Score v5
Choose a stronger alternative or inspect the source manually before any install attempt.
Stars
568 GitHub stars
Repo activity
568 stars, 94 forks
Maintenance
6d since push
License
Unknown
Install
npx skills add kangarooking/kangarooking-skills --skill viral-title
Install safety
Agent-readable metadata
Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.
Suited tasks
Suited agents
Install decision
Trust and risk
Outcome loop
Install command
npx skills add kangarooking/kangarooking-skills --skill viral-titleDo not use when
Alternative
1.9K Stars
npx skills add yanliudesign/mono-color-skill --skill mono-color
Alternative
61.0K Stars
npx skills add mvanhorn/last30days-skill -g
Alternative
38.4K Stars
npx skills add Imbad0202/academic-research-skills
Alternative
28.0K Stars
npx skills add assafelovic/gpt-researcher
Agent safety v2
This skill should not be selected by an agent without explicit human security review.
Do not auto-install. Inspect the source, dependencies, and permission surface first.
high
Skill metadata references terminal, CLI, shell, subprocess, or command execution workflows.
medium
Skill likely fetches remote pages, APIs, repositories, or external services.
medium
Skill may read or write project files, documents, generated artifacts, or local workspace state.
high
Skill metadata references credentials, tokens, environment variables, or secret-bearing workflows.
Agent resolve plan
The Resolve API returns the selected skill, alternatives, safety policy, audit notes, install target, and copy-paste prompt an agent can follow without scraping this page.
Open JSON
/api/agent/resolve?task=Use%20viral-title%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20viral-title%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/kangarooking-viral-title/install
Agent should check
Copy prompt
Task: Use viral-title in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20viral-title%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/kangarooking-viral-title/install
Install command: npx skills add kangarooking/kangarooking-skills --skill viral-title
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.Agent handoff
Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.
Install handoff
/api/skills/kangarooking-viral-title/install
LLM text format
/api/skills/kangarooking-viral-title/install?format=text
Find alternatives
/api/skills/search?q=viral-title&limit=3
Agent prompt
Use viral-title for this task. Review https://www.openagentskill.com/api/skills/kangarooking-viral-title/install, then install with: npx skills add kangarooking/kangarooking-skills --skill viral-titleRegistry metadata
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
Manifest
/api/registry/manifest/kangarooking-viral-title
LLM text
/api/registry/manifest/kangarooking-viral-title?format=text
Install alias
/api/registry/install/kangarooking-viral-title
Recommend
/api/registry/recommend?task=Use%20viral-title%20in%20an%20agent%20workflow&limit=3
Agent fit
Research agents
Use-case tags
Platforms
Claude Code
Audit report
A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
Agent decision cockpit
Shortlist this skill and compare it with close alternatives before production adoption.
Role in stack
Companion skill
Primary fit
Research agents
Trust label
Strong shortlist
Install path
Command ready
Use when
Evidence
review first
Implementation path
Trust profile
Trust Score v5 found insufficient evidence for agent installation. Treat this as discovery material, not an executable recommendation.
GitHub adoption
INFO568 GitHub stars
Stars/forks activity
INFO568 stars, 94 forks; issue activity unavailable in current metadata
Recent maintenance
PASS6d since push
License clarity
CHECKUnknown
Good signals
Review before install
Recommended action
Choose a stronger alternative or inspect the source manually before any install attempt.
Quality profile
Useful candidate, but compare it with alternatives before adopting.
Workflow fit
Investigate faster
I need my agent to research a topic, compare sources, and produce a concise report.
Publish consistently
I need my agent to turn research and product updates into useful content drafts.
Search private knowledge
I need my agent to build a RAG workflow over documents and retrieve reliable context.
Workflow fit
Find, compare, and synthesize
A workflow for agents that gather sources, compare claims, summarize long material, and draft useful research briefs.
Turn skills into distribution
A workflow for turning newly indexed skills into SEO briefs, social drafts, comparison pages, and reusable publishing workflows.
Ingest, retrieve, and cite
A workflow for document-heavy agents that ingest files, create searchable knowledge, retrieve relevant context, and answer with grounded sources.
Alternative shortlist
Similar skills that may fit this task.
Generate original one-ink or controlled two-ink editorial images from any theme, sentence, article idea, object, or reference photo. Always use this skill when the user asks for 单色海报、双色印刷、单色调视觉、蓝色/绿色孔版印刷、risograph、网点照片、复古或当代编辑排版、zine poster, monochrome editorial poster, duotone print, or asks to use the mono-color style. It uses an adaptive white, gray, or pale-beige substrate, no more than two printing inks, active negative space, terse human language, and strong serif/grotesk/mono typography without making retro styling the default or copying a source composition, wording, logo, or artwork. Produce both the final generation prompt and the generated raster image unless the user explicitly asks for prompt only.
Research the last 30 days across Reddit, X, YouTube, Hacker News, Polymarket, GitHub, and the web, then synthesize a grounded brief for an AI agent.
Academic Research Skills for Claude Code: research → write → review → revise → finalize
Run autonomous deep research over web and local sources
--- name: viral-title description: 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. ---
# 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 <wechat|x|youtube|bilibili> --query "<topic words>" --mechanism "<optional 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 <platform> --topic "..." --recommended-title "..."` | Log user feedback | `python3 scripts/log_feedback.py --session-id "..." --platform <platform> --selected-title "..." --user-edit "..." --rating 5 --feedback "..."` | Retrieve title examples | `python3 scripts/retrieve_title_examples.py --platform <wechat|x|youtube|bilibili> --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.
Decision snapshot
568 GitHub stars
Audit
Install and adoption review
Agent-proven evidence
Outcome reports after resolve, review, install, and one narrow run.
No agent outcome data yet. The first agent run can report success, setup needs, risk blocks, failure, or not-relevant through /api/agent/outcome.
Install
Free and open source. Review the report before installing into production agents.
Growth loop
Scenario-led draft for viral-title, ready for a manual X post.
A practical pick for design or creative work: viral-title: Generate high-potential viral title candidates for content across WeChat public account articles, X/Twitter posts, YouTube... 568 stars https://www.openagentskill.com/skills/kangarooking-viral-title?ref=x
Listing + install path for viral-title: https://www.openagentskill.com/skills/kangarooking-viral-title?ref=x Install: npx skills add kangarooking/kangarooking-skills --skill viral-title
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secrets or environment access, shell or command execution
Agent outcomes
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Docs
Strong README/SKILL.md context
Risk summary
Install readiness
Permission surface
secrets or environment access, shell or command execution
Agent outcomes
No agent outcome data yet
Docs
Strong README/SKILL.md context
Risk summary
Install readiness
Permission surface
secrets or environment access, shell or command execution
Agent outcomes
No agent outcome data yet
Docs
Strong README/SKILL.md context
Risk summary
Install readiness
Permission surface
secrets or environment access, shell or command execution
Agent outcomes
No agent outcome data yet
Docs
Strong README/SKILL.md context
Risk summary
Install readiness