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kangarooking

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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 adap

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Prix non confirmé★ 568 Stars GitHubRegistre mis à jour · 1 sept. 2026agent-skill

Vue d’ensemble

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.

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Documentation source, pas des instructions pour ce site. Vérifiez les permissions avant d’exécuter des commandes.

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 saysPlatform fileStatus
公众号, 微信公众号, WeChat articlereferences/platforms/wechat-public-account.mdImplemented
X, Twitter, 推特references/platforms/x.mdImplemented
YouTube, 油管references/platforms/youtube.mdImplemented
B站, Bilibilireferences/platforms/bilibili.mdImplemented

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:

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.

NeedCommand
Log a title sessionpython3 scripts/log_title_session.py --platform <platform> --topic "..." --recommended-title "..."
Log user feedbackpython3 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
Review recent learningpython3 scripts/analyze_feedback.py --recent 20
Run seed evalspython3 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:

**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:

**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.
Voir la source ↗
Métadonnées du fichier
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.
Voir le texte original
---
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.

Examiner la source

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  • La licence est ambiguë
  • Dependency or permission surface needs review
  • Permission surface may require sandboxing
  • No LICENSE file or license metadata is present in the submitted skill directory, and GitHub reports the repository license as Unknown, so reuse terms are unclear.
  • The SKILL.md references Python scripts and reference files that are not fully visible in the review excerpt, so users cannot audit or verify setup from SKILL.md alone.
  • Setup and dependencies are not documented: there is no Python version, requirements list, or install step, which may cause the skill to fail on first use if scripts require non-standard packages.
  • Quality score needs review
  • Permission surface needs review: secrets or environment access, shell or command execution
  • License clarity: Unknown
  • Dependency/runtime risk: command execution surface, credential or environment access
  • Permission surface: secrets or environment access, shell or command execution
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Commencer par une petite tâche

  1. 1Lisez la source et confirmez entrées, résultats, dépendances et permissions.
  2. 2Demandez un plan à l’agent. Approuvez la configuration et les coûts avant un test isolé.
  3. 3Vérifiez résultats et fichiers modifiés. Signalez uniquement ce qui a été exécuté et conservez la révision source.

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Source et conseils d’utilisation

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Dépôt source
kangarooking/kangarooking-skills
Licence
Inconnu
Version
1.0.0
Dernier push GitHub
31 août 2026
Registre mis à jour
1 sept. 2026
Chemin des instructions
viral-title/SKILL.md

Version déclarée dans le registre ; vérifiez les versions de la source.

Qualité

66/100

Prometteur

Confiance

55/100

Do not auto-install

Audit

71/100

Revue nécessaire

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  • La licence est ambiguë
  • Dependency or permission surface needs review
  • Permission surface may require sandboxing
  • No LICENSE file or license metadata is present in the submitted skill directory, and GitHub reports the repository license as Unknown, so reuse terms are unclear.
  • The SKILL.md references Python scripts and reference files that are not fully visible in the review excerpt, so users cannot audit or verify setup from SKILL.md alone.
  • Setup and dependencies are not documented: there is no Python version, requirements list, or install step, which may cause the skill to fail on first use if scripts require non-standard packages.
  • Quality score needs review
  • Permission surface needs review: secrets or environment access, shell or command execution
  • License clarity: Unknown
  • Dependency/runtime risk: command execution surface, credential or environment access
  • Permission surface: secrets or environment access, shell or command execution
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      "risk_blocked": 0,
      "setup_required": 0,
      "avg_output_quality": null,
      "production_outcomes": 0,
      "last_outcome_at": null,
      "label": "No agent outcome data yet"
    },
    "auto_install": {
      "allowed": false,
      "sandbox_required": true,
      "reason": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
    },
    "best_for": [
      "research",
      "agent-skill"
    ],
    "known_risks": [
      "No LICENSE file or license metadata is present in the submitted skill directory, and GitHub reports the repository license as Unknown, so reuse terms are unclear.",
      "License is unclear",
      "Quality score needs review",
      "Permission surface needs review: secrets or environment access, shell or command execution",
      "License clarity: Unknown",
      "Dependency/runtime risk: command execution surface, credential or environment access",
      "Permission surface: secrets or environment access, shell or command execution"
    ]
  },
  "agent_proven": {
    "version": "agent-proven-v1",
    "score": 0,
    "tier": "unproven",
    "label": "Needs first agent run",
    "summary": "No agent outcome reports yet. Use Resolve, run one narrow sandbox task, then report the result.",
    "metrics": {
      "totalOutcomes": 0,
      "successfulOutcomes": 0,
      "failedOutcomes": 0,
      "installAttempts": 0,
      "installSuccessRate": null,
      "successRate": null,
      "recentSuccessRate": null,
      "recentFailureRate": null,
      "riskBlocked": 0,
      "setupRequired": 0,
      "notRelevant": 0,
      "avgOutputQuality": null,
      "avgTimeToUsefulMs": null,
      "productionOutcomes": 0,
      "humanReviewRequired": 0,
      "uniqueAgents": 0,
      "lastOutcomeAt": null
    },
    "signals": [],
    "penalties": [
      "No real agent outcome evidence yet"
    ]
  },
  "audit": {
    "score": 71,
    "risk_level": "needs_review",
    "risk_label": "Needs review",
    "warnings": [
      "License is unclear",
      "Dependency or permission surface needs review",
      "Permission surface may require sandboxing",
      "No LICENSE file or license metadata is present in the submitted skill directory, and GitHub reports the repository license as Unknown, so reuse terms are unclear.",
      "The SKILL.md references Python scripts and reference files that are not fully visible in the review excerpt, so users cannot audit or verify setup from SKILL.md alone.",
      "Setup and dependencies are not documented: there is no Python version, requirements list, or install step, which may cause the skill to fail on first use if scripts require non-standard packages.",
      "Quality score needs review",
      "Permission surface needs review: secrets or environment access, shell or command execution"
    ]
  },
  "safety_gate": {
    "tier": "blocked",
    "label": "Blocked for auto-install",
    "auto_install_policy": "block",
    "auto_install_allowed": false,
    "human_review_required": true,
    "blocked": true,
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
  },
  "quality": {
    "score": 66,
    "label": "Promising"
  },
  "supply": {
    "track": "Research and knowledge work",
    "scenario": "Research agents",
    "maintenance": "1mo since push",
    "risk": "Needs review"
  },
  "alternative_skills": [
    {
      "slug": "krillinai-krillinai-render-horizontal",
      "name": "krillinai-render-horizontal",
      "url": "https://www.openagentskill.com/skills/krillinai-krillinai-render-horizontal",
      "stars": 12876,
      "install_command": "npx skills add krillinai/OpenCreator --skill krillinai-render-horizontal",
      "trust_score": 82,
      "audit_score": 85
    },
    {
      "slug": "orchestra-research-nemo-curator",
      "name": "nemo-curator",
      "url": "https://www.openagentskill.com/skills/orchestra-research-nemo-curator",
      "stars": 13443,
      "install_command": "npx skills add Orchestra-Research/AI-Research-SKILLs --skill nemo-curator",
      "trust_score": 81,
      "audit_score": 85
    }
  ],
  "do_not_use_when": [
    "teams that need a vendor-supported SLA",
    "production agents without a repository review",
    "No LICENSE file or license metadata is present in the submitted skill directory, and GitHub reports the repository license as Unknown, so reuse terms are unclear.",
    "High-risk permission hints: Shell or command execution, Secrets or environment access",
    "License is unclear",
    "Dependency or permission surface needs review",
    "Permission surface may require sandboxing",
    "The SKILL.md references Python scripts and reference files that are not fully visible in the review excerpt, so users cannot audit or verify setup from SKILL.md alone."
  ],
  "agent_contract": {
    "task_input": "Use viral-title in an agent workflow",
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
    "install_policy": "block",
    "minimum_review_before_use": [
      "Trust: 63/100 Manual review",
      "Audit: 71/100 Needs review",
      "Safety: 27/100 Avoid automatic install",
      "Review repository, license, install command, and permission surface before production use."
    ],
    "expected_agent_output": {
      "selected_skill": "kangarooking-viral-title (viral-title)",
      "install_command": "npx skills add kangarooking/kangarooking-skills --skill viral-title",
      "risk_summary": "Needs review; Blocked for auto-install; Review before production",
      "verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
    }
  },
  "outcome_feedback": {
    "endpoint": "https://www.openagentskill.com/api/agent/outcome",
    "method": "POST",
    "requires_resolve_event_id": true,
    "event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
    "expected_outcomes": [
      "success",
      "failed",
      "not_relevant",
      "blocked_by_risk",
      "setup_required"
    ],
    "payload_template": {
      "event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
      "skill_slug": "kangarooking-viral-title",
      "task": "Use viral-title in an agent workflow",
      "agent": "codex",
      "outcome": "success",
      "install_used": true,
      "risk_blocked": false,
      "setup_required": false,
      "task_success": true,
      "output_quality": 4,
      "error_type": null,
      "human_review_required": false,
      "workspace": "sandbox",
      "time_to_useful_ms": 120000,
      "notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
    }
  },
  "endpoints": {
    "web": "https://www.openagentskill.com/skills/kangarooking-viral-title",
    "api": "https://www.openagentskill.com/api/agent/skills/kangarooking-viral-title",
    "audit": "https://www.openagentskill.com/skills/kangarooking-viral-title/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=kangarooking-viral-title&task=Use%20viral-title%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20viral-title%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20viral-title%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/kangarooking-viral-title/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/kangarooking-viral-title"
  }
}

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kangarooking
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Index communautaire OpenAgentSkill

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