Creator · sergebulaev
Last updated · Sep 3, 2026
Reverse-engineer the hook formula from a viral LinkedIn post URL. Returns which of the 16 canonical 2026 formulas it uses (anaphora, R.I.P., year-pivot, time-anchor, curiosity-gap, contrarian, comment-gate, emotional cold-open, named-gratitude, and 7 more), why it worked, and a b
Creator · sergebulaev
Last updated · Sep 3, 2026
Reverse-engineer the hook formula from a viral LinkedIn post URL. Returns which of the 16 canonical 2026 formulas it uses (anaphora, R.I.P., year-pivot, time-anchor, curiosity-gap, contrarian, comment-gate, emotional cold-open, named-gratitude, and 7 more), why it worked, and a b
Creator · sergebulaev
Last updated · Sep 3, 2026
Reverse-engineer the hook formula from a viral LinkedIn post URL. Returns which of the 16 canonical 2026 formulas it uses (anaphora, R.I.P., year-pivot, time-anchor, curiosity-gap, contrarian, comment-gate, emotional cold-open, named-gratitude, and 7 more), why it worked, and a b
Creator · sergebulaev
Last updated · Sep 3, 2026
Reverse-engineer the hook formula from a viral LinkedIn post URL. Returns which of the 16 canonical 2026 formulas it uses (anaphora, R.I.P., year-pivot, time-anchor, curiosity-gap, contrarian, comment-gate, emotional cold-open, named-gratitude, and 7 more), why it worked, and a b
Review then install
Install targets
Codex install prompt
Install the "linkedin-hook-extractor" agent skill from https://github.com/sergebulaev/linkedin-skills/tree/main/skills/linkedin-hook-extractor. 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: Reverse-engineer the hook formula from a viral LinkedIn post URL. Returns which of the 16 canonical 2026 formulas it uses (anaphora, R.I.P., year-pivot, time-anchor, curiosity-gap, contrarian, comment-gate, emotional cold-open, named-gratitude, and 7 more), why it worked, and a blank template. Use to learn from a competitor's post, not to write your own (use linkedin-post-writer). 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":"sergebulaev-linkedin-hook-extractor","task":"Install linkedin-hook-extractor","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
Document processing
I need my agent to read PDFs, extract tables, and turn documents into structured data.
Agent fit
Claude Code + CLI + Codex
Codex, Claude Code, Cursor, CLI, or custom agents.
Install
Ready
npx skills add sergebulaev/linkedin-skills --skill linkedin-hook-extractor
Maintenance
fresh
4d since push
Risk
Safe to try
Quality score needs review
GitHub quality
653
75/100 Quality · 82/100 Trust
Coverage tags
Review notes
Quality score 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
StrongSolid option that is likely worth shortlisting for production workflows.
Trust
Review then installGood shortlist signal, but the agent should review audit notes, install policy, and outcome evidence before running it.
Audit
Safe to tryA machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
OpenAgentSkill Trust Score v5
Use as the primary candidate after human or sandbox review.
Stars
653 GitHub stars
Repo activity
653 stars, 99 forks
Maintenance
4d since push
License
MIT
Install
npx skills add sergebulaev/linkedin-skills --skill linkedin-hook-extractor
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 sergebulaev/linkedin-skills --skill linkedin-hook-extractorDo not use when
Alternative
16.3K Stars
npx skills add hardikpandya/stop-slop --skill stop-slop
Alternative
37.4K Stars
npx skills add blader/humanizer --skill humanizer
Alternative
5.8K Stars
npx skills add petergyang/no-ai-slop --skill no-ai-slop
Alternative
4.8K Stars
npx skills add cursor/plugins --skill unslop
Agent safety v2
Good audit and safety signals with no high-risk permission hints in public metadata.
Review the audit page, then allow agent install in a sandboxed workflow.
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.
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%20linkedin-hook-extractor%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20linkedin-hook-extractor%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/sergebulaev-linkedin-hook-extractor/install
Agent should check
Copy prompt
Task: Use linkedin-hook-extractor in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20linkedin-hook-extractor%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/sergebulaev-linkedin-hook-extractor/install
Install command: npx skills add sergebulaev/linkedin-skills --skill linkedin-hook-extractor
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/sergebulaev-linkedin-hook-extractor/install
LLM text format
/api/skills/sergebulaev-linkedin-hook-extractor/install?format=text
Find alternatives
/api/skills/search?q=linkedin-hook-extractor&limit=3
Agent prompt
Use linkedin-hook-extractor for this task. Review https://www.openagentskill.com/api/skills/sergebulaev-linkedin-hook-extractor/install, then install with: npx skills add sergebulaev/linkedin-skills --skill linkedin-hook-extractorRegistry 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/sergebulaev-linkedin-hook-extractor
LLM text
/api/registry/manifest/sergebulaev-linkedin-hook-extractor?format=text
Install alias
/api/registry/install/sergebulaev-linkedin-hook-extractor
Recommend
/api/registry/recommend?task=Use%20linkedin-hook-extractor%20in%20an%20agent%20workflow&limit=3
Agent fit
Document processing
Platforms
Claude Code
Audit report
A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
Agent decision cockpit
Use this as a leading candidate, then validate the README and install path in your own agent stack.
Role in stack
Primary pick
Primary fit
Document processing
Trust label
Production-ready
Install path
Command ready
Use when
Evidence
review first
Implementation path
Trust profile
Good shortlist signal, but the agent should review audit notes, install policy, and outcome evidence before running it.
GitHub adoption
INFO653 GitHub stars
Stars/forks activity
INFO653 stars, 99 forks; issue activity unavailable in current metadata
Recent maintenance
PASS4d since push
License clarity
PASSMIT
Good signals
Review before install
Recommended action
Use as the primary candidate after human or sandbox review.
Quality profile
Solid option that is likely worth shortlisting for production workflows.
Workflow fit
Parse messy files
I need my agent to read PDFs, extract tables, and turn documents into structured data.
Investigate faster
I need my agent to research a topic, compare sources, and produce a concise report.
Automate repeated work
I need my agent to automate a repeated workflow across tools and files.
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.
Scrape, clean, and reuse web data
A practical workflow for agents that crawl public pages, extract clean content, normalize data, and hand it to downstream research or RAG workflows.
Alternative shortlist
Similar skills that may fit this task.
Eliminates predictable AI writing patterns from prose while preserving specific meaning and reader trust.
Rewrites AI-sounding text so it reads naturally while preserving every factual claim and matching the writer's voice.
Audits or minimally edits drafts to remove named AI-writing patterns without flattening the author's voice.
Applies Cursor's prose-discipline rules to remove filler and AI writing tells from agent-facing and user-facing text.
--- name: linkedin-hook-extractor description: Reverse-engineer the hook formula from a viral LinkedIn post URL. Returns which of the 16 canonical 2026 formulas it uses (anaphora, R.I.P., year-pivot, time-anchor, curiosity-gap, contrarian, comment-gate, emotional cold-open, named-gratitude, and 7 more), why it worked, and a blank template. Use to learn from a competitor's post, not to write your own (use linkedin-post-writer). ---
# LinkedIn Hook Extractor
Paste a viral LinkedIn post URL. Get back: which hook formula it uses, the exact structure, why it worked, and a blank template mapped to your topic.
## When to use
- User finds a viral post they want to study - User wants to replicate a specific creator's pattern - Before `linkedin-post-writer` to seed a draft with a proven structure
## Input
A LinkedIn post URL (any type: activity, share, ugcPost).
## Output
- **Formula identified** (F1-F16 from `../../references/hook-formulas.md`) with confidence score - **Structural breakdown:** - Hook lines (first 210 chars) - Body architecture (sections + what each does) - Close pattern - Reaction-triggering devices (numbers, named entities, vulnerabilities) - **Why it worked** psychologically - **Blank template** filled with slot markers matched to the original, ready for the user's voice - **Cautions:** anything in the original post that would fail 2026 audit (em dashes, AI vocab, outdated tactics)
## Steps
1. **Parse URL.** `lib.url_parser.parse_linkedin_url` → `post_urn`. 2. **Fetch post body.** If `APIFY_TOKEN` is set, call `lib.ApifyClient.fetch_post(url)`. Otherwise ask the user to paste the text. 3. **Classify.** Match against the 16 formulas using features: - First 2 lines: anaphoric? question? confession? number-led? - Body: numbered list? dated receipts? ledger? teardown? - Close: mirror question? identity reframe? commitment? - F11-F16 cues: in-medias-res emotional scene with no setup (F11 Emotional Cold-Open); "I don't know who needs to hear this" reassurance (F12 Permission Slip); fake-bad-news that resolves positive (F13 Bait-and-Switch); a roll-call of named people thanked (F14 Named Gratitude); "{jargon} explained to kids" glossary (F15 Explain-to-Kids); "outside I'm called X, at home none of it survives" (F16 Status-Strip). 4. **Score confidence.** If multiple formulas fit, return top 2 with fit scores. 5. **Extract structure.** Pull each logical section and label it by formula role. 6. **Generate blank template.** Replace specifics with `{slot}` markers that match the user's topic. 7. **Audit the source.** Flag any AI tells in the original so the user doesn't copy them.
## Example
See `references/examples.md` for worked examples.
## Formulas reference
See `../../references/hook-formulas.md` for the 16 canonical formulas with full skeletons.
## Files
- `SKILL.md` — this file - `references/classification-rules.md` — feature extraction + scoring heuristics
## Related skills
- `linkedin-post-writer` — use the extracted template to draft your own - `linkedin-humanizer --mode audit` — audit your draft before shipping
Source provenance
Decision snapshot
653 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 linkedin-hook-extractor, ready for a manual X post.
linkedin-hook-extractor: Reverse-engineer the hook formula from a viral LinkedIn post URL. Returns which of the 16 can... 653 stars https://www.openagentskill.com/skills/sergebulaev-linkedin-hook-extractor?ref=x
Listing + install path for linkedin-hook-extractor: https://www.openagentskill.com/skills/sergebulaev-linkedin-hook-extractor?ref=x Install: npx skills add sergebulaev/linkedin-skills --skill linkedin-hook-extractor
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 sergebulaev 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/sergebulaev-linkedin-hook-extractor?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/sergebulaev-linkedin-hook-extractor?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/sergebulaev-linkedin-hook-extractor/audit)
[](https://www.openagentskill.com/skills/sergebulaev-linkedin-hook-extractor?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)sergebulaev
@sergebulaev
Share whether this skill looks useful for your agent workflow. Aggregated feedback improves rankings over time.
Review then install
stop-slop
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16.3K StarsHumanizer
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37.4K Starsno-ai-slop
Audits or minimally edits drafts to remove named AI-writing patterns without flattening the author's voice.
5.8K Starsunslop
Applies Cursor's prose-discipline rules to remove filler and AI writing tells from agent-facing and user-facing text.
4.8K StarsReview then install
Install targets
Codex install prompt
Install the "linkedin-hook-extractor" agent skill from https://github.com/sergebulaev/linkedin-skills/tree/main/skills/linkedin-hook-extractor. 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: Reverse-engineer the hook formula from a viral LinkedIn post URL. Returns which of the 16 canonical 2026 formulas it uses (anaphora, R.I.P., year-pivot, time-anchor, curiosity-gap, contrarian, comment-gate, emotional cold-open, named-gratitude, and 7 more), why it worked, and a blank template. Use to learn from a competitor's post, not to write your own (use linkedin-post-writer). 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":"sergebulaev-linkedin-hook-extractor","task":"Install linkedin-hook-extractor","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
Document processing
I need my agent to read PDFs, extract tables, and turn documents into structured data.
Agent fit
Claude Code + CLI + Codex
Codex, Claude Code, Cursor, CLI, or custom agents.
Install
Ready
npx skills add sergebulaev/linkedin-skills --skill linkedin-hook-extractor
Maintenance
fresh
4d since push
Risk
Safe to try
Quality score needs review
GitHub quality
653
75/100 Quality · 82/100 Trust
Coverage tags
Review notes
Quality score 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
StrongSolid option that is likely worth shortlisting for production workflows.
Trust
Review then installGood shortlist signal, but the agent should review audit notes, install policy, and outcome evidence before running it.
Audit
Safe to tryA machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
OpenAgentSkill Trust Score v5
Use as the primary candidate after human or sandbox review.
Stars
653 GitHub stars
Repo activity
653 stars, 99 forks
Maintenance
4d since push
License
MIT
Install
npx skills add sergebulaev/linkedin-skills --skill linkedin-hook-extractor
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 sergebulaev/linkedin-skills --skill linkedin-hook-extractorDo not use when
Alternative
16.3K Stars
npx skills add hardikpandya/stop-slop --skill stop-slop
Alternative
37.4K Stars
npx skills add blader/humanizer --skill humanizer
Alternative
5.8K Stars
npx skills add petergyang/no-ai-slop --skill no-ai-slop
Alternative
4.8K Stars
npx skills add cursor/plugins --skill unslop
Agent safety v2
Good audit and safety signals with no high-risk permission hints in public metadata.
Review the audit page, then allow agent install in a sandboxed workflow.
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.
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%20linkedin-hook-extractor%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20linkedin-hook-extractor%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/sergebulaev-linkedin-hook-extractor/install
Agent should check
Copy prompt
Task: Use linkedin-hook-extractor in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20linkedin-hook-extractor%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/sergebulaev-linkedin-hook-extractor/install
Install command: npx skills add sergebulaev/linkedin-skills --skill linkedin-hook-extractor
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/sergebulaev-linkedin-hook-extractor/install
LLM text format
/api/skills/sergebulaev-linkedin-hook-extractor/install?format=text
Find alternatives
/api/skills/search?q=linkedin-hook-extractor&limit=3
Agent prompt
Use linkedin-hook-extractor for this task. Review https://www.openagentskill.com/api/skills/sergebulaev-linkedin-hook-extractor/install, then install with: npx skills add sergebulaev/linkedin-skills --skill linkedin-hook-extractorRegistry 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/sergebulaev-linkedin-hook-extractor
LLM text
/api/registry/manifest/sergebulaev-linkedin-hook-extractor?format=text
Install alias
/api/registry/install/sergebulaev-linkedin-hook-extractor
Recommend
/api/registry/recommend?task=Use%20linkedin-hook-extractor%20in%20an%20agent%20workflow&limit=3
Agent fit
Document processing
Platforms
Claude Code
Audit report
A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
Agent decision cockpit
Use this as a leading candidate, then validate the README and install path in your own agent stack.
Role in stack
Primary pick
Primary fit
Document processing
Trust label
Production-ready
Install path
Command ready
Use when
Evidence
review first
Implementation path
Trust profile
Good shortlist signal, but the agent should review audit notes, install policy, and outcome evidence before running it.
GitHub adoption
INFO653 GitHub stars
Stars/forks activity
INFO653 stars, 99 forks; issue activity unavailable in current metadata
Recent maintenance
PASS4d since push
License clarity
PASSMIT
Good signals
Review before install
Recommended action
Use as the primary candidate after human or sandbox review.
Quality profile
Solid option that is likely worth shortlisting for production workflows.
Workflow fit
Parse messy files
I need my agent to read PDFs, extract tables, and turn documents into structured data.
Investigate faster
I need my agent to research a topic, compare sources, and produce a concise report.
Automate repeated work
I need my agent to automate a repeated workflow across tools and files.
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.
Scrape, clean, and reuse web data
A practical workflow for agents that crawl public pages, extract clean content, normalize data, and hand it to downstream research or RAG workflows.
Alternative shortlist
Similar skills that may fit this task.
Eliminates predictable AI writing patterns from prose while preserving specific meaning and reader trust.
Rewrites AI-sounding text so it reads naturally while preserving every factual claim and matching the writer's voice.
Audits or minimally edits drafts to remove named AI-writing patterns without flattening the author's voice.
Applies Cursor's prose-discipline rules to remove filler and AI writing tells from agent-facing and user-facing text.
--- name: linkedin-hook-extractor description: Reverse-engineer the hook formula from a viral LinkedIn post URL. Returns which of the 16 canonical 2026 formulas it uses (anaphora, R.I.P., year-pivot, time-anchor, curiosity-gap, contrarian, comment-gate, emotional cold-open, named-gratitude, and 7 more), why it worked, and a blank template. Use to learn from a competitor's post, not to write your own (use linkedin-post-writer). ---
# LinkedIn Hook Extractor
Paste a viral LinkedIn post URL. Get back: which hook formula it uses, the exact structure, why it worked, and a blank template mapped to your topic.
## When to use
- User finds a viral post they want to study - User wants to replicate a specific creator's pattern - Before `linkedin-post-writer` to seed a draft with a proven structure
## Input
A LinkedIn post URL (any type: activity, share, ugcPost).
## Output
- **Formula identified** (F1-F16 from `../../references/hook-formulas.md`) with confidence score - **Structural breakdown:** - Hook lines (first 210 chars) - Body architecture (sections + what each does) - Close pattern - Reaction-triggering devices (numbers, named entities, vulnerabilities) - **Why it worked** psychologically - **Blank template** filled with slot markers matched to the original, ready for the user's voice - **Cautions:** anything in the original post that would fail 2026 audit (em dashes, AI vocab, outdated tactics)
## Steps
1. **Parse URL.** `lib.url_parser.parse_linkedin_url` → `post_urn`. 2. **Fetch post body.** If `APIFY_TOKEN` is set, call `lib.ApifyClient.fetch_post(url)`. Otherwise ask the user to paste the text. 3. **Classify.** Match against the 16 formulas using features: - First 2 lines: anaphoric? question? confession? number-led? - Body: numbered list? dated receipts? ledger? teardown? - Close: mirror question? identity reframe? commitment? - F11-F16 cues: in-medias-res emotional scene with no setup (F11 Emotional Cold-Open); "I don't know who needs to hear this" reassurance (F12 Permission Slip); fake-bad-news that resolves positive (F13 Bait-and-Switch); a roll-call of named people thanked (F14 Named Gratitude); "{jargon} explained to kids" glossary (F15 Explain-to-Kids); "outside I'm called X, at home none of it survives" (F16 Status-Strip). 4. **Score confidence.** If multiple formulas fit, return top 2 with fit scores. 5. **Extract structure.** Pull each logical section and label it by formula role. 6. **Generate blank template.** Replace specifics with `{slot}` markers that match the user's topic. 7. **Audit the source.** Flag any AI tells in the original so the user doesn't copy them.
## Example
See `references/examples.md` for worked examples.
## Formulas reference
See `../../references/hook-formulas.md` for the 16 canonical formulas with full skeletons.
## Files
- `SKILL.md` — this file - `references/classification-rules.md` — feature extraction + scoring heuristics
## Related skills
- `linkedin-post-writer` — use the extracted template to draft your own - `linkedin-humanizer --mode audit` — audit your draft before shipping
Source provenance
Decision snapshot
653 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 linkedin-hook-extractor, ready for a manual X post.
linkedin-hook-extractor: Reverse-engineer the hook formula from a viral LinkedIn post URL. Returns which of the 16 can... 653 stars https://www.openagentskill.com/skills/sergebulaev-linkedin-hook-extractor?ref=x
Listing + install path for linkedin-hook-extractor: https://www.openagentskill.com/skills/sergebulaev-linkedin-hook-extractor?ref=x Install: npx skills add sergebulaev/linkedin-skills --skill linkedin-hook-extractor
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 sergebulaev 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/sergebulaev-linkedin-hook-extractor?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/sergebulaev-linkedin-hook-extractor?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/sergebulaev-linkedin-hook-extractor/audit)
[](https://www.openagentskill.com/skills/sergebulaev-linkedin-hook-extractor?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)sergebulaev
@sergebulaev
Share whether this skill looks useful for your agent workflow. Aggregated feedback improves rankings over time.
Review then install
stop-slop
Eliminates predictable AI writing patterns from prose while preserving specific meaning and reader trust.
16.3K StarsHumanizer
Rewrites AI-sounding text so it reads naturally while preserving every factual claim and matching the writer's voice.
37.4K Starsno-ai-slop
Audits or minimally edits drafts to remove named AI-writing patterns without flattening the author's voice.
5.8K Starsunslop
Applies Cursor's prose-discipline rules to remove filler and AI writing tells from agent-facing and user-facing text.
4.8K StarsReview then install
Install targets
Codex install prompt
Install the "linkedin-hook-extractor" agent skill from https://github.com/sergebulaev/linkedin-skills/tree/main/skills/linkedin-hook-extractor. 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: Reverse-engineer the hook formula from a viral LinkedIn post URL. Returns which of the 16 canonical 2026 formulas it uses (anaphora, R.I.P., year-pivot, time-anchor, curiosity-gap, contrarian, comment-gate, emotional cold-open, named-gratitude, and 7 more), why it worked, and a blank template. Use to learn from a competitor's post, not to write your own (use linkedin-post-writer). 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":"sergebulaev-linkedin-hook-extractor","task":"Install linkedin-hook-extractor","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
Document processing
I need my agent to read PDFs, extract tables, and turn documents into structured data.
Agent fit
Claude Code + CLI + Codex
Codex, Claude Code, Cursor, CLI, or custom agents.
Install
Ready
npx skills add sergebulaev/linkedin-skills --skill linkedin-hook-extractor
Maintenance
fresh
4d since push
Risk
Safe to try
Quality score needs review
GitHub quality
653
75/100 Quality · 82/100 Trust
Coverage tags
Review notes
Quality score 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
StrongSolid option that is likely worth shortlisting for production workflows.
Trust
Review then installGood shortlist signal, but the agent should review audit notes, install policy, and outcome evidence before running it.
Audit
Safe to tryA machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
OpenAgentSkill Trust Score v5
Use as the primary candidate after human or sandbox review.
Stars
653 GitHub stars
Repo activity
653 stars, 99 forks
Maintenance
4d since push
License
MIT
Install
npx skills add sergebulaev/linkedin-skills --skill linkedin-hook-extractor
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 sergebulaev/linkedin-skills --skill linkedin-hook-extractorDo not use when
Alternative
16.3K Stars
npx skills add hardikpandya/stop-slop --skill stop-slop
Alternative
37.4K Stars
npx skills add blader/humanizer --skill humanizer
Alternative
5.8K Stars
npx skills add petergyang/no-ai-slop --skill no-ai-slop
Alternative
4.8K Stars
npx skills add cursor/plugins --skill unslop
Agent safety v2
Good audit and safety signals with no high-risk permission hints in public metadata.
Review the audit page, then allow agent install in a sandboxed workflow.
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.
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%20linkedin-hook-extractor%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20linkedin-hook-extractor%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/sergebulaev-linkedin-hook-extractor/install
Agent should check
Copy prompt
Task: Use linkedin-hook-extractor in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20linkedin-hook-extractor%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/sergebulaev-linkedin-hook-extractor/install
Install command: npx skills add sergebulaev/linkedin-skills --skill linkedin-hook-extractor
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/sergebulaev-linkedin-hook-extractor/install
LLM text format
/api/skills/sergebulaev-linkedin-hook-extractor/install?format=text
Find alternatives
/api/skills/search?q=linkedin-hook-extractor&limit=3
Agent prompt
Use linkedin-hook-extractor for this task. Review https://www.openagentskill.com/api/skills/sergebulaev-linkedin-hook-extractor/install, then install with: npx skills add sergebulaev/linkedin-skills --skill linkedin-hook-extractorRegistry 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/sergebulaev-linkedin-hook-extractor
LLM text
/api/registry/manifest/sergebulaev-linkedin-hook-extractor?format=text
Install alias
/api/registry/install/sergebulaev-linkedin-hook-extractor
Recommend
/api/registry/recommend?task=Use%20linkedin-hook-extractor%20in%20an%20agent%20workflow&limit=3
Agent fit
Document processing
Platforms
Claude Code
Audit report
A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
Agent decision cockpit
Use this as a leading candidate, then validate the README and install path in your own agent stack.
Role in stack
Primary pick
Primary fit
Document processing
Trust label
Production-ready
Install path
Command ready
Use when
Evidence
review first
Implementation path
Trust profile
Good shortlist signal, but the agent should review audit notes, install policy, and outcome evidence before running it.
GitHub adoption
INFO653 GitHub stars
Stars/forks activity
INFO653 stars, 99 forks; issue activity unavailable in current metadata
Recent maintenance
PASS4d since push
License clarity
PASSMIT
Good signals
Review before install
Recommended action
Use as the primary candidate after human or sandbox review.
Quality profile
Solid option that is likely worth shortlisting for production workflows.
Workflow fit
Parse messy files
I need my agent to read PDFs, extract tables, and turn documents into structured data.
Investigate faster
I need my agent to research a topic, compare sources, and produce a concise report.
Automate repeated work
I need my agent to automate a repeated workflow across tools and files.
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.
Scrape, clean, and reuse web data
A practical workflow for agents that crawl public pages, extract clean content, normalize data, and hand it to downstream research or RAG workflows.
Alternative shortlist
Similar skills that may fit this task.
Eliminates predictable AI writing patterns from prose while preserving specific meaning and reader trust.
Rewrites AI-sounding text so it reads naturally while preserving every factual claim and matching the writer's voice.
Audits or minimally edits drafts to remove named AI-writing patterns without flattening the author's voice.
Applies Cursor's prose-discipline rules to remove filler and AI writing tells from agent-facing and user-facing text.
--- name: linkedin-hook-extractor description: Reverse-engineer the hook formula from a viral LinkedIn post URL. Returns which of the 16 canonical 2026 formulas it uses (anaphora, R.I.P., year-pivot, time-anchor, curiosity-gap, contrarian, comment-gate, emotional cold-open, named-gratitude, and 7 more), why it worked, and a blank template. Use to learn from a competitor's post, not to write your own (use linkedin-post-writer). ---
# LinkedIn Hook Extractor
Paste a viral LinkedIn post URL. Get back: which hook formula it uses, the exact structure, why it worked, and a blank template mapped to your topic.
## When to use
- User finds a viral post they want to study - User wants to replicate a specific creator's pattern - Before `linkedin-post-writer` to seed a draft with a proven structure
## Input
A LinkedIn post URL (any type: activity, share, ugcPost).
## Output
- **Formula identified** (F1-F16 from `../../references/hook-formulas.md`) with confidence score - **Structural breakdown:** - Hook lines (first 210 chars) - Body architecture (sections + what each does) - Close pattern - Reaction-triggering devices (numbers, named entities, vulnerabilities) - **Why it worked** psychologically - **Blank template** filled with slot markers matched to the original, ready for the user's voice - **Cautions:** anything in the original post that would fail 2026 audit (em dashes, AI vocab, outdated tactics)
## Steps
1. **Parse URL.** `lib.url_parser.parse_linkedin_url` → `post_urn`. 2. **Fetch post body.** If `APIFY_TOKEN` is set, call `lib.ApifyClient.fetch_post(url)`. Otherwise ask the user to paste the text. 3. **Classify.** Match against the 16 formulas using features: - First 2 lines: anaphoric? question? confession? number-led? - Body: numbered list? dated receipts? ledger? teardown? - Close: mirror question? identity reframe? commitment? - F11-F16 cues: in-medias-res emotional scene with no setup (F11 Emotional Cold-Open); "I don't know who needs to hear this" reassurance (F12 Permission Slip); fake-bad-news that resolves positive (F13 Bait-and-Switch); a roll-call of named people thanked (F14 Named Gratitude); "{jargon} explained to kids" glossary (F15 Explain-to-Kids); "outside I'm called X, at home none of it survives" (F16 Status-Strip). 4. **Score confidence.** If multiple formulas fit, return top 2 with fit scores. 5. **Extract structure.** Pull each logical section and label it by formula role. 6. **Generate blank template.** Replace specifics with `{slot}` markers that match the user's topic. 7. **Audit the source.** Flag any AI tells in the original so the user doesn't copy them.
## Example
See `references/examples.md` for worked examples.
## Formulas reference
See `../../references/hook-formulas.md` for the 16 canonical formulas with full skeletons.
## Files
- `SKILL.md` — this file - `references/classification-rules.md` — feature extraction + scoring heuristics
## Related skills
- `linkedin-post-writer` — use the extracted template to draft your own - `linkedin-humanizer --mode audit` — audit your draft before shipping
Source provenance
Decision snapshot
653 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 linkedin-hook-extractor, ready for a manual X post.
linkedin-hook-extractor: Reverse-engineer the hook formula from a viral LinkedIn post URL. Returns which of the 16 can... 653 stars https://www.openagentskill.com/skills/sergebulaev-linkedin-hook-extractor?ref=x
Listing + install path for linkedin-hook-extractor: https://www.openagentskill.com/skills/sergebulaev-linkedin-hook-extractor?ref=x Install: npx skills add sergebulaev/linkedin-skills --skill linkedin-hook-extractor
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 sergebulaev 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/sergebulaev-linkedin-hook-extractor?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/sergebulaev-linkedin-hook-extractor?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/sergebulaev-linkedin-hook-extractor/audit)
[](https://www.openagentskill.com/skills/sergebulaev-linkedin-hook-extractor?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)sergebulaev
@sergebulaev
Share whether this skill looks useful for your agent workflow. Aggregated feedback improves rankings over time.
Review then install
stop-slop
Eliminates predictable AI writing patterns from prose while preserving specific meaning and reader trust.
16.3K StarsHumanizer
Rewrites AI-sounding text so it reads naturally while preserving every factual claim and matching the writer's voice.
37.4K Starsno-ai-slop
Audits or minimally edits drafts to remove named AI-writing patterns without flattening the author's voice.
5.8K Starsunslop
Applies Cursor's prose-discipline rules to remove filler and AI writing tells from agent-facing and user-facing text.
4.8K StarsReview then install
Install targets
Codex install prompt
Install the "linkedin-hook-extractor" agent skill from https://github.com/sergebulaev/linkedin-skills/tree/main/skills/linkedin-hook-extractor. 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: Reverse-engineer the hook formula from a viral LinkedIn post URL. Returns which of the 16 canonical 2026 formulas it uses (anaphora, R.I.P., year-pivot, time-anchor, curiosity-gap, contrarian, comment-gate, emotional cold-open, named-gratitude, and 7 more), why it worked, and a blank template. Use to learn from a competitor's post, not to write your own (use linkedin-post-writer). 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":"sergebulaev-linkedin-hook-extractor","task":"Install linkedin-hook-extractor","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
Document processing
I need my agent to read PDFs, extract tables, and turn documents into structured data.
Agent fit
Claude Code + CLI + Codex
Codex, Claude Code, Cursor, CLI, or custom agents.
Install
Ready
npx skills add sergebulaev/linkedin-skills --skill linkedin-hook-extractor
Maintenance
fresh
4d since push
Risk
Safe to try
Quality score needs review
GitHub quality
653
75/100 Quality · 82/100 Trust
Coverage tags
Review notes
Quality score 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
StrongSolid option that is likely worth shortlisting for production workflows.
Trust
Review then installGood shortlist signal, but the agent should review audit notes, install policy, and outcome evidence before running it.
Audit
Safe to tryA machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
OpenAgentSkill Trust Score v5
Use as the primary candidate after human or sandbox review.
Stars
653 GitHub stars
Repo activity
653 stars, 99 forks
Maintenance
4d since push
License
MIT
Install
npx skills add sergebulaev/linkedin-skills --skill linkedin-hook-extractor
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 sergebulaev/linkedin-skills --skill linkedin-hook-extractorDo not use when
Alternative
16.3K Stars
npx skills add hardikpandya/stop-slop --skill stop-slop
Alternative
37.4K Stars
npx skills add blader/humanizer --skill humanizer
Alternative
5.8K Stars
npx skills add petergyang/no-ai-slop --skill no-ai-slop
Alternative
4.8K Stars
npx skills add cursor/plugins --skill unslop
Agent safety v2
Good audit and safety signals with no high-risk permission hints in public metadata.
Review the audit page, then allow agent install in a sandboxed workflow.
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.
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%20linkedin-hook-extractor%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20linkedin-hook-extractor%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/sergebulaev-linkedin-hook-extractor/install
Agent should check
Copy prompt
Task: Use linkedin-hook-extractor in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20linkedin-hook-extractor%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/sergebulaev-linkedin-hook-extractor/install
Install command: npx skills add sergebulaev/linkedin-skills --skill linkedin-hook-extractor
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/sergebulaev-linkedin-hook-extractor/install
LLM text format
/api/skills/sergebulaev-linkedin-hook-extractor/install?format=text
Find alternatives
/api/skills/search?q=linkedin-hook-extractor&limit=3
Agent prompt
Use linkedin-hook-extractor for this task. Review https://www.openagentskill.com/api/skills/sergebulaev-linkedin-hook-extractor/install, then install with: npx skills add sergebulaev/linkedin-skills --skill linkedin-hook-extractorRegistry 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/sergebulaev-linkedin-hook-extractor
LLM text
/api/registry/manifest/sergebulaev-linkedin-hook-extractor?format=text
Install alias
/api/registry/install/sergebulaev-linkedin-hook-extractor
Recommend
/api/registry/recommend?task=Use%20linkedin-hook-extractor%20in%20an%20agent%20workflow&limit=3
Agent fit
Document processing
Platforms
Claude Code
Audit report
A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
Agent decision cockpit
Use this as a leading candidate, then validate the README and install path in your own agent stack.
Role in stack
Primary pick
Primary fit
Document processing
Trust label
Production-ready
Install path
Command ready
Use when
Evidence
review first
Implementation path
Trust profile
Good shortlist signal, but the agent should review audit notes, install policy, and outcome evidence before running it.
GitHub adoption
INFO653 GitHub stars
Stars/forks activity
INFO653 stars, 99 forks; issue activity unavailable in current metadata
Recent maintenance
PASS4d since push
License clarity
PASSMIT
Good signals
Review before install
Recommended action
Use as the primary candidate after human or sandbox review.
Quality profile
Solid option that is likely worth shortlisting for production workflows.
Workflow fit
Parse messy files
I need my agent to read PDFs, extract tables, and turn documents into structured data.
Investigate faster
I need my agent to research a topic, compare sources, and produce a concise report.
Automate repeated work
I need my agent to automate a repeated workflow across tools and files.
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.
Scrape, clean, and reuse web data
A practical workflow for agents that crawl public pages, extract clean content, normalize data, and hand it to downstream research or RAG workflows.
Alternative shortlist
Similar skills that may fit this task.
Eliminates predictable AI writing patterns from prose while preserving specific meaning and reader trust.
Rewrites AI-sounding text so it reads naturally while preserving every factual claim and matching the writer's voice.
Audits or minimally edits drafts to remove named AI-writing patterns without flattening the author's voice.
Applies Cursor's prose-discipline rules to remove filler and AI writing tells from agent-facing and user-facing text.
--- name: linkedin-hook-extractor description: Reverse-engineer the hook formula from a viral LinkedIn post URL. Returns which of the 16 canonical 2026 formulas it uses (anaphora, R.I.P., year-pivot, time-anchor, curiosity-gap, contrarian, comment-gate, emotional cold-open, named-gratitude, and 7 more), why it worked, and a blank template. Use to learn from a competitor's post, not to write your own (use linkedin-post-writer). ---
# LinkedIn Hook Extractor
Paste a viral LinkedIn post URL. Get back: which hook formula it uses, the exact structure, why it worked, and a blank template mapped to your topic.
## When to use
- User finds a viral post they want to study - User wants to replicate a specific creator's pattern - Before `linkedin-post-writer` to seed a draft with a proven structure
## Input
A LinkedIn post URL (any type: activity, share, ugcPost).
## Output
- **Formula identified** (F1-F16 from `../../references/hook-formulas.md`) with confidence score - **Structural breakdown:** - Hook lines (first 210 chars) - Body architecture (sections + what each does) - Close pattern - Reaction-triggering devices (numbers, named entities, vulnerabilities) - **Why it worked** psychologically - **Blank template** filled with slot markers matched to the original, ready for the user's voice - **Cautions:** anything in the original post that would fail 2026 audit (em dashes, AI vocab, outdated tactics)
## Steps
1. **Parse URL.** `lib.url_parser.parse_linkedin_url` → `post_urn`. 2. **Fetch post body.** If `APIFY_TOKEN` is set, call `lib.ApifyClient.fetch_post(url)`. Otherwise ask the user to paste the text. 3. **Classify.** Match against the 16 formulas using features: - First 2 lines: anaphoric? question? confession? number-led? - Body: numbered list? dated receipts? ledger? teardown? - Close: mirror question? identity reframe? commitment? - F11-F16 cues: in-medias-res emotional scene with no setup (F11 Emotional Cold-Open); "I don't know who needs to hear this" reassurance (F12 Permission Slip); fake-bad-news that resolves positive (F13 Bait-and-Switch); a roll-call of named people thanked (F14 Named Gratitude); "{jargon} explained to kids" glossary (F15 Explain-to-Kids); "outside I'm called X, at home none of it survives" (F16 Status-Strip). 4. **Score confidence.** If multiple formulas fit, return top 2 with fit scores. 5. **Extract structure.** Pull each logical section and label it by formula role. 6. **Generate blank template.** Replace specifics with `{slot}` markers that match the user's topic. 7. **Audit the source.** Flag any AI tells in the original so the user doesn't copy them.
## Example
See `references/examples.md` for worked examples.
## Formulas reference
See `../../references/hook-formulas.md` for the 16 canonical formulas with full skeletons.
## Files
- `SKILL.md` — this file - `references/classification-rules.md` — feature extraction + scoring heuristics
## Related skills
- `linkedin-post-writer` — use the extracted template to draft your own - `linkedin-humanizer --mode audit` — audit your draft before shipping
Source provenance
Decision snapshot
653 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 linkedin-hook-extractor, ready for a manual X post.
linkedin-hook-extractor: Reverse-engineer the hook formula from a viral LinkedIn post URL. Returns which of the 16 can... 653 stars https://www.openagentskill.com/skills/sergebulaev-linkedin-hook-extractor?ref=x
Listing + install path for linkedin-hook-extractor: https://www.openagentskill.com/skills/sergebulaev-linkedin-hook-extractor?ref=x Install: npx skills add sergebulaev/linkedin-skills --skill linkedin-hook-extractor
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 sergebulaev 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/sergebulaev-linkedin-hook-extractor?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/sergebulaev-linkedin-hook-extractor?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/sergebulaev-linkedin-hook-extractor/audit)
[](https://www.openagentskill.com/skills/sergebulaev-linkedin-hook-extractor?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)sergebulaev
@sergebulaev
Share whether this skill looks useful for your agent workflow. Aggregated feedback improves rankings over time.
Review then install
stop-slop
Eliminates predictable AI writing patterns from prose while preserving specific meaning and reader trust.
16.3K StarsHumanizer
Rewrites AI-sounding text so it reads naturally while preserving every factual claim and matching the writer's voice.
37.4K Starsno-ai-slop
Audits or minimally edits drafts to remove named AI-writing patterns without flattening the author's voice.
5.8K Starsunslop
Applies Cursor's prose-discipline rules to remove filler and AI writing tells from agent-facing and user-facing text.
4.8K StarsPermission surface
filesystem or document access, network or browser access
Agent outcomes
No agent outcome data yet
Docs
Strong README/SKILL.md context
Risk summary
Install readiness
Permission surface
filesystem or document access, network or browser access
Agent outcomes
No agent outcome data yet
Docs
Strong README/SKILL.md context
Risk summary
Install readiness
Permission surface
filesystem or document access, network or browser access
Agent outcomes
No agent outcome data yet
Docs
Strong README/SKILL.md context
Risk summary
Install readiness
Permission surface
filesystem or document access, network or browser access
Agent outcomes
No agent outcome data yet
Docs
Strong README/SKILL.md context
Risk summary
Install readiness