[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6FZST649E02N16XNTG8G9DF":3,"skill-same:01M6FZST649E02N16XNTG8G9DF":77},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":26,"publishedAt":45,"linkOnly":46,"markdownContent":47,"markdownBody":48,"frontMatter":49,"refName":53,"commitSha":54,"skillDirectory":55,"forksCount":56,"repositoryHtmlUrl":57,"repositoryHomepageUrl":58,"ownerHtmlUrl":59,"ownerBlogUrl":60,"ownerDescription":61,"updatedAt":62,"install":63,"resources":76},"01M6FZST649E02N16XNTG8G9DF","youtube-competitive-research",null,"Analyze why an external YouTube video performed well and extract evidence-backed competitive lessons, positioning gaps, and adaptation opportunities","GeorgeQLe\u002Fagentic-skills","GeorgeQLe","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F21134126?v=4","01M6D2JP418S0VGJGF8M93EC3H","George Le","01M6D2JPBAKYD82TA5FS6HKQ3D",1,"MIT","https:\u002F\u002Fgithub.com\u002FGeorgeQLe\u002Fagentic-skills\u002Fblob\u002Fmaster\u002Fpacks\u002Fyoutube-ops\u002Fclaude\u002Fyoutube-competitive-research\u002Farchive\u002Fv0.3\u002FSKILL.md",[18,23],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000016","图像与多媒体","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000021","资料检索与研究",[27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44],"youtube","youtube competitive research","competitive","research","extract","v0","channel","packaging","evidence","analyze","positioning","adaptation","id","retention","why","comments","URL","Video","2026-11-10 10:12:30",false,"---\nname: youtube-competitive-research\ndescription: Analyze why an external YouTube video performed well and extract evidence-backed competitive lessons, positioning gaps, and adaptation opportunities\ntype: research\nversion: v0.3\nargument-hint: \"\u003Cvideo URL or ID...> [--primary-channel \u003Cslug>] [--comments N] [--angle packaging|topic|retention|positioning]\"\ninterview_depth: none\n---\n\n## Pack Availability Guard\n\nBefore telling the user to run a skill from another project-local pack, check `.agents\u002Fproject.json.enabled_packs`. If the target pack is not enabled, recommend `\u002Fpack install \u003Cpack>` instead of the target skill. Global skills are always valid. Skills from this same pack are valid because the current skill is already running from that pack.\n\n# YouTube Competitive Research\n\nInvoke as `\u002Fyoutube-competitive-research`.\n\n## Report-First Approval Gate\n\nDefault to report-only: present findings, evidence coverage, assumptions, recommended artifact path, and proposed file changes in a pre-approval alignment page plus a concise conversation summary for user approval before creating or updating canonical research, spec, or task files.\n\nDo not write or overwrite synthesized deliverables until the user explicitly approves, unless the user invoked an explicit write\u002Fupdate\u002Ffix mode or clearly asked to write files upfront. Raw evidence capture may be persisted before analysis when reproducibility requires it; report those raw paths separately and still gate synthesized research\u002Freport writes.\n\nWhen stopping for approval, build and attempt to open the alignment preview page first, then ask the user to review it and approve, question, or request adjustments. Do not include `Recommended next skill`, `Recommended next command`, or downstream routing language. The approval request itself is the next action. Only emit next-skill routing after the approved artifact has been written or updated.\n\n## Staged Research Workflow\n\nUse this staged workflow for synthesized research or report outputs that would create or update canonical research, spec, or task files.\n\n1. **Stage 1 - Research and clarify.** Perform the research, run required source\u002Fcode checks, and ask any needed clarification questions. Write only a non-canonical working packet: flat mode uses `research\u002F_working\u002Fpreliminary-\u003Cskill>-research.md`; product-path mode uses `research\u002F{slug}\u002F_working\u002Fpreliminary-\u003Cskill>-research.md`. Replace `\u003Cskill>` with this skill's `name` value. Do not create or update canonical research, spec, or task files in Stage 1. Raw evidence or search logs may remain as supporting evidence where this skill already requires them, but synthesized deliverables stay in the working packet.\n2. **Stage 2 - Review alignment.** Consume the working packet and build the `review` HTML alignment page. The page must render the full preliminary packet, evidence matrix, assumptions\u002Fconfidence register, source coverage gaps, proposed canonical file changes, and approval gates. Stop for either feedback-only YAML or final compiled YAML. Feedback-only YAML revises the working packet and page, then remains in Stage 2.\n3. **Stage 3 - Finalize approved artifacts.** Consume final compiled YAML only when it has no unresolved `needs-clarification`, unresolved `down` feedback, or other unresolved negative feedback. Apply approved edits first, archive the working packet to `docs\u002Fhistory\u002Farchive\u002FYYYY-MM-DD\u002FHHMMSS\u002F\u003Coriginal-working-path>`, remove the active working packet, write the approved canonical artifacts to the unchanged output paths below, and convert the alignment page to `confirmed` with the approval record preserved.\n\nCanonical output paths remain unchanged. Search logs and other supporting evidence remain allowed only where this skill's output contract already requires them.\n\nStudy external YouTube videos as competitive or opposition research. The goal is to explain why a video likely worked, what audience job it served, what can be learned, and how to adapt the lesson without copying the competitor. Use `\u002Fyoutube-vid-research` for neutral comprehension and `\u002Fyoutube-peer-benchmark` for channel-level peer comparisons.\n\n## Evidence And Feedback Handling\n\nTreat user feedback as input to evaluate, not as automatic ground truth.\n\n- For factual, evidentiary, technical, or source-backed claims: verify against available evidence. If the user appears to misunderstand the evidence or states something factually incorrect, push back clearly and cite the evidence. Do not rewrite findings merely to agree.\n- For taste, brand, positioning preference, risk appetite, prioritization, or other subjective judgment calls: weigh user feedback heavily and adapt the recommendation unless it conflicts with verified evidence.\n- When feedback mixes facts and preference, separate them explicitly: correct the factual part, then incorporate the preference where it is a legitimate judgment call.\n- When uncertain, say what is known, what is inferred, and what would change the conclusion.\n\n## Inputs\n\n- Required: one or more YouTube video URLs or video IDs.\n- Optional `--primary-channel \u003Cslug>`: compare the lessons against existing evidence for the user's channel under `research\u002Fyoutube\u002Fdata\u002F\u003Cslug>\u002F`.\n- Optional `--comments N`: fetch up to N public top-level comments when public comment tooling\u002FAPI access is available. Default 50, max 200.\n- Optional `--angle packaging|topic|retention|positioning`: default is to cover all four.\n\n## Process\n\n1. Resolve every target into a video ID from watch URLs, Shorts URLs, youtu.be URLs, embed URLs, or raw 11-character IDs.\n2. Require `yt-dlp` for public metadata:\n\n   ```bash\n   command -v yt-dlp\n   ```\n\n3. Select a transcript Python interpreter. Prefer a workspace-local `.venv`; create it if missing. Install `youtube-transcript-api` into `.venv` only when the import check fails and network access is available.\n4. Persist raw evidence under `research\u002Fyoutube\u002Fdata\u002F\u003Cvideo-id>\u002F`:\n   - `metadata-YYYY-MM-DD.json`: raw `yt-dlp --dump-json \"VIDEO_URL\"` output.\n   - `transcript\u002F\u003Cvideo-id>.json`: raw transcript JSON when available.\n   - `transcript\u002Ftranscript-summary.json`: transcript text or failure reason.\n   - `api\u002Fcomment-threads-YYYY-MM-DD.json`: optional public comments when an already-authorized API path is available.\n5. Extract performance signals from public metadata when present: views, likes, comments, age, views\u002Fday, duration, views\u002Fminute, title, thumbnail URLs, description, tags, chapters, upload timing, and channel identity.\n6. Analyze:\n   - Performance hypothesis: why the video likely earned attention relative to public evidence.\n   - Audience job: the problem, desire, identity, fear, or curiosity the video served.\n   - Packaging: title, thumbnail, topic framing, specificity, novelty, and expectation match.\n   - Content\u002Fretention drivers: hook, pacing, proof, examples, conflict, payoff, structure, and CTA.\n   - Distribution context: timing, trend fit, search demand, collaboration, controversy, community, or platform format signals when evidence supports them.\n   - Comment themes only when comments were captured; separate praise, objections, confusion, requests, and spam\u002Fnoise.\n   - Adaptation opportunities for the user's channel, product, or content strategy.\n7. If a primary channel slug is supplied, compare against the user's existing raw channel evidence and state what the competitor proves that the primary channel does not yet prove.\n\n## Output\n\nCreate the `research\u002Fyoutube\u002F` and `research\u002Fyoutube\u002Fdata\u002F\u003Cvideo-id>\u002F` directories if they do not exist.\n\nWrite:\n\n```text\nresearch\u002Fyoutube\u002Fcompetitive-research-\u003Cvideo-id-or-slug>-YYYY-MM-DD.md\n```\n\nUse this structure:\n\n```markdown\n# YouTube Competitive Research - [Title or Video Set]\n\n> Videos: [URLs]\n> Primary channel: [slug or not provided]\n> Date captured: YYYY-MM-DD\n> Evidence: [raw paths used]\n> Angle: packaging \u002F topic \u002F retention \u002F positioning \u002F all\n\n## Evidence Coverage\n\n| Video | Metadata | Transcript | Comments | Primary-channel comparison |\n|---|---|---|---|---|\n| [Title](URL) | Available \u002F Missing | Available \u002F Missing | Available \u002F Missing \u002F Not requested | Available \u002F Not provided |\n\n## Performance Snapshot\n\n| Video | Published | Duration | Views | Likes | Comments | Views\u002Fday | Views\u002Fmin |\n|---|---|---:|---:|---:|---:|---:|---:|\n| ... | ... | ... | ... | ... | ... | ... | ... |\n\n## Why It Likely Worked\n\n1. [Hypothesis] - [evidence]\n2. [Hypothesis] - [evidence]\n3. [Hypothesis] - [evidence]\n\n## Audience Job And Positioning\n\n[Audience need, competitor promise, category wedge, and emotional or practical trigger.]\n\n## Packaging And Retention Lessons\n\n- **Title \u002F thumbnail promise**: ...\n- **Topic timing \u002F novelty**: ...\n- **Hook and structure**: ...\n- **Proof and payoff**: ...\n- **Comments \u002F audience signal**: ...\n\n## Adaptation Opportunities\n\n| Opportunity | Adapt, avoid, or counter-position | Evidence | Suggested next move |\n|---|---|---|---|\n| ... | ... | ... | ... |\n\n## Risks Of Copying\n\n[What would be derivative, off-brand, unsupported by evidence, or risky to imitate.]\n\n## Open Questions And Evidence Gaps\n\n[Missing transcript, unavailable comments, absent owner analytics, uncertain distribution cause, or claims needing independent verification.]\n```\n\n## Constraints\n\n- Do not invent metrics, transcript quotes, comments, thumbnail details, or distribution causes.\n- Separate observed public evidence from performance hypotheses.\n- Do not recommend copying a competitor directly; translate evidence into differentiated positioning and execution.\n- Do not treat public views as proof of profitability, subscriber conversion, retention, CTR, or revenue.\n- Do not bypass login walls, bot protections, access controls, or private YouTube Studio data.\n- Archive existing canonical artifacts before replacing them under `docs\u002Fhistory\u002Farchive\u002FYYYY-MM-DD\u002FHHMMSS\u002F\u003Coriginal-relative-path>`.\n\n## Approved Artifact Handoff\n\nAfter an approved synthesized write, explicit write\u002Fupdate mode, or any direct artifact mutation:\n\n- List every created or updated synthesized artifact path in the final response.\n- State the verification performed, such as readback, schema\u002Fcheck command, or why no executable verification applies for a Markdown-only strategy artifact.\n- Check and report the relevant git status for intended artifacts when the project is a git repository. If intended artifacts are modified or untracked, make the next action shipping, committing, or an explicit dirty-artifact handoff before recommending downstream strategy work.\n- Do not imply the research workflow is complete while approved artifacts remain untracked or uncommitted unless the user explicitly asked not to ship.\n- If stopping for approval before writing, the approval request remains the next action; do not include downstream routing.\n\n## Intent-Aware Routing\n\nBefore applying the default `## Next-Skill Routing` sequence, classify the user's immediate intent and route to the missing action that best serves that intent:\n\n- Strategy refresh: recommend the missing or stale positioning, programming, portfolio, metrics, or product-media artifact.\n- Recording prep: recommend the missing series spec, script, build proof, walkthrough guide, or validation artifact needed before recording.\n- Upload prep: recommend packaging, title\u002Fthumbnail, description, chapters, or final metadata work before broader strategy work.\n- Performance review: recommend metrics, cadence, portfolio, peer benchmark, or owner-analytics export work before new content planning.\n- Owner analytics or private\u002Fmanual platform evidence: route to an explicit manual\u002Fguide handoff instead of inventing unavailable metrics.\n- Dirty intended artifacts: route to shipping\u002Fcommit\u002Fhandoff first, not another creator strategy skill.\n\nUse the default next-skill sequence only when no stronger user intent, missing artifact, manual blocker, or dirty-artifact handoff applies.\n\n## Alignment Page\n\nWhen this skill produces durable deliverables (research, specs, plans, reports, prototypes, or any document output), build a full-depth HTML alignment page following `ALIGNMENT-PAGE.md` in this skill's directory. Output: `alignment\u002Fyoutube-competitive-research-{topic}.html`.\n\n## Default Shipping Contract\n\nFollow the shared shipping contract convention in CLAUDE.md.\n","## Pack Availability Guard\n\nBefore telling the user to run a skill from another project-local pack, check `.agents\u002Fproject.json.enabled_packs`. If the target pack is not enabled, recommend `\u002Fpack install \u003Cpack>` instead of the target skill. Global skills are always valid. Skills from this same pack are valid because the current skill is already running from that pack.\n\n# YouTube Competitive Research\n\nInvoke as `\u002Fyoutube-competitive-research`.\n\n## Report-First Approval Gate\n\nDefault to report-only: present findings, evidence coverage, assumptions, recommended artifact path, and proposed file changes in a pre-approval alignment page plus a concise conversation summary for user approval before creating or updating canonical research, spec, or task files.\n\nDo not write or overwrite synthesized deliverables until the user explicitly approves, unless the user invoked an explicit write\u002Fupdate\u002Ffix mode or clearly asked to write files upfront. Raw evidence capture may be persisted before analysis when reproducibility requires it; report those raw paths separately and still gate synthesized research\u002Freport writes.\n\nWhen stopping for approval, build and attempt to open the alignment preview page first, then ask the user to review it and approve, question, or request adjustments. Do not include `Recommended next skill`, `Recommended next command`, or downstream routing language. The approval request itself is the next action. Only emit next-skill routing after the approved artifact has been written or updated.\n\n## Staged Research Workflow\n\nUse this staged workflow for synthesized research or report outputs that would create or update canonical research, spec, or task files.\n\n1. **Stage 1 - Research and clarify.** Perform the research, run required source\u002Fcode checks, and ask any needed clarification questions. Write only a non-canonical working packet: flat mode uses `research\u002F_working\u002Fpreliminary-\u003Cskill>-research.md`; product-path mode uses `research\u002F{slug}\u002F_working\u002Fpreliminary-\u003Cskill>-research.md`. Replace `\u003Cskill>` with this skill's `name` value. Do not create or update canonical research, spec, or task files in Stage 1. Raw evidence or search logs may remain as supporting evidence where this skill already requires them, but synthesized deliverables stay in the working packet.\n2. **Stage 2 - Review alignment.** Consume the working packet and build the `review` HTML alignment page. The page must render the full preliminary packet, evidence matrix, assumptions\u002Fconfidence register, source coverage gaps, proposed canonical file changes, and approval gates. Stop for either feedback-only YAML or final compiled YAML. Feedback-only YAML revises the working packet and page, then remains in Stage 2.\n3. **Stage 3 - Finalize approved artifacts.** Consume final compiled YAML only when it has no unresolved `needs-clarification`, unresolved `down` feedback, or other unresolved negative feedback. Apply approved edits first, archive the working packet to `docs\u002Fhistory\u002Farchive\u002FYYYY-MM-DD\u002FHHMMSS\u002F\u003Coriginal-working-path>`, remove the active working packet, write the approved canonical artifacts to the unchanged output paths below, and convert the alignment page to `confirmed` with the approval record preserved.\n\nCanonical output paths remain unchanged. Search logs and other supporting evidence remain allowed only where this skill's output contract already requires them.\n\nStudy external YouTube videos as competitive or opposition research. The goal is to explain why a video likely worked, what audience job it served, what can be learned, and how to adapt the lesson without copying the competitor. Use `\u002Fyoutube-vid-research` for neutral comprehension and `\u002Fyoutube-peer-benchmark` for channel-level peer comparisons.\n\n## Evidence And Feedback Handling\n\nTreat user feedback as input to evaluate, not as automatic ground truth.\n\n- For factual, evidentiary, technical, or source-backed claims: verify against available evidence. If the user appears to misunderstand the evidence or states something factually incorrect, push back clearly and cite the evidence. Do not rewrite findings merely to agree.\n- For taste, brand, positioning preference, risk appetite, prioritization, or other subjective judgment calls: weigh user feedback heavily and adapt the recommendation unless it conflicts with verified evidence.\n- When feedback mixes facts and preference, separate them explicitly: correct the factual part, then incorporate the preference where it is a legitimate judgment call.\n- When uncertain, say what is known, what is inferred, and what would change the conclusion.\n\n## Inputs\n\n- Required: one or more YouTube video URLs or video IDs.\n- Optional `--primary-channel \u003Cslug>`: compare the lessons against existing evidence for the user's channel under `research\u002Fyoutube\u002Fdata\u002F\u003Cslug>\u002F`.\n- Optional `--comments N`: fetch up to N public top-level comments when public comment tooling\u002FAPI access is available. Default 50, max 200.\n- Optional `--angle packaging|topic|retention|positioning`: default is to cover all four.\n\n## Process\n\n1. Resolve every target into a video ID from watch URLs, Shorts URLs, youtu.be URLs, embed URLs, or raw 11-character IDs.\n2. Require `yt-dlp` for public metadata:\n\n   ```bash\n   command -v yt-dlp\n   ```\n\n3. Select a transcript Python interpreter. Prefer a workspace-local `.venv`; create it if missing. Install `youtube-transcript-api` into `.venv` only when the import check fails and network access is available.\n4. Persist raw evidence under `research\u002Fyoutube\u002Fdata\u002F\u003Cvideo-id>\u002F`:\n   - `metadata-YYYY-MM-DD.json`: raw `yt-dlp --dump-json \"VIDEO_URL\"` output.\n   - `transcript\u002F\u003Cvideo-id>.json`: raw transcript JSON when available.\n   - `transcript\u002Ftranscript-summary.json`: transcript text or failure reason.\n   - `api\u002Fcomment-threads-YYYY-MM-DD.json`: optional public comments when an already-authorized API path is available.\n5. Extract performance signals from public metadata when present: views, likes, comments, age, views\u002Fday, duration, views\u002Fminute, title, thumbnail URLs, description, tags, chapters, upload timing, and channel identity.\n6. Analyze:\n   - Performance hypothesis: why the video likely earned attention relative to public evidence.\n   - Audience job: the problem, desire, identity, fear, or curiosity the video served.\n   - Packaging: title, thumbnail, topic framing, specificity, novelty, and expectation match.\n   - Content\u002Fretention drivers: hook, pacing, proof, examples, conflict, payoff, structure, and CTA.\n   - Distribution context: timing, trend fit, search demand, collaboration, controversy, community, or platform format signals when evidence supports them.\n   - Comment themes only when comments were captured; separate praise, objections, confusion, requests, and spam\u002Fnoise.\n   - Adaptation opportunities for the user's channel, product, or content strategy.\n7. If a primary channel slug is supplied, compare against the user's existing raw channel evidence and state what the competitor proves that the primary channel does not yet prove.\n\n## Output\n\nCreate the `research\u002Fyoutube\u002F` and `research\u002Fyoutube\u002Fdata\u002F\u003Cvideo-id>\u002F` directories if they do not exist.\n\nWrite:\n\n```text\nresearch\u002Fyoutube\u002Fcompetitive-research-\u003Cvideo-id-or-slug>-YYYY-MM-DD.md\n```\n\nUse this structure:\n\n```markdown\n# YouTube Competitive Research - [Title or Video Set]\n\n> Videos: [URLs]\n> Primary channel: [slug or not provided]\n> Date captured: YYYY-MM-DD\n> Evidence: [raw paths used]\n> Angle: packaging \u002F topic \u002F retention \u002F positioning \u002F all\n\n## Evidence Coverage\n\n| Video | Metadata | Transcript | Comments | Primary-channel comparison |\n|---|---|---|---|---|\n| [Title](URL) | Available \u002F Missing | Available \u002F Missing | Available \u002F Missing \u002F Not requested | Available \u002F Not provided |\n\n## Performance Snapshot\n\n| Video | Published | Duration | Views | Likes | Comments | Views\u002Fday | Views\u002Fmin |\n|---|---|---:|---:|---:|---:|---:|---:|\n| ... | ... | ... | ... | ... | ... | ... | ... |\n\n## Why It Likely Worked\n\n1. [Hypothesis] - [evidence]\n2. [Hypothesis] - [evidence]\n3. [Hypothesis] - [evidence]\n\n## Audience Job And Positioning\n\n[Audience need, competitor promise, category wedge, and emotional or practical trigger.]\n\n## Packaging And Retention Lessons\n\n- **Title \u002F thumbnail promise**: ...\n- **Topic timing \u002F novelty**: ...\n- **Hook and structure**: ...\n- **Proof and payoff**: ...\n- **Comments \u002F audience signal**: ...\n\n## Adaptation Opportunities\n\n| Opportunity | Adapt, avoid, or counter-position | Evidence | Suggested next move |\n|---|---|---|---|\n| ... | ... | ... | ... |\n\n## Risks Of Copying\n\n[What would be derivative, off-brand, unsupported by evidence, or risky to imitate.]\n\n## Open Questions And Evidence Gaps\n\n[Missing transcript, unavailable comments, absent owner analytics, uncertain distribution cause, or claims needing independent verification.]\n```\n\n## Constraints\n\n- Do not invent metrics, transcript quotes, comments, thumbnail details, or distribution causes.\n- Separate observed public evidence from performance hypotheses.\n- Do not recommend copying a competitor directly; translate evidence into differentiated positioning and execution.\n- Do not treat public views as proof of profitability, subscriber conversion, retention, CTR, or revenue.\n- Do not bypass login walls, bot protections, access controls, or private YouTube Studio data.\n- Archive existing canonical artifacts before replacing them under `docs\u002Fhistory\u002Farchive\u002FYYYY-MM-DD\u002FHHMMSS\u002F\u003Coriginal-relative-path>`.\n\n## Approved Artifact Handoff\n\nAfter an approved synthesized write, explicit write\u002Fupdate mode, or any direct artifact mutation:\n\n- List every created or updated synthesized artifact path in the final response.\n- State the verification performed, such as readback, schema\u002Fcheck command, or why no executable verification applies for a Markdown-only strategy artifact.\n- Check and report the relevant git status for intended artifacts when the project is a git repository. If intended artifacts are modified or untracked, make the next action shipping, committing, or an explicit dirty-artifact handoff before recommending downstream strategy work.\n- Do not imply the research workflow is complete while approved artifacts remain untracked or uncommitted unless the user explicitly asked not to ship.\n- If stopping for approval before writing, the approval request remains the next action; do not include downstream routing.\n\n## Intent-Aware Routing\n\nBefore applying the default `## Next-Skill Routing` sequence, classify the user's immediate intent and route to the missing action that best serves that intent:\n\n- Strategy refresh: recommend the missing or stale positioning, programming, portfolio, metrics, or product-media artifact.\n- Recording prep: recommend the missing series spec, script, build proof, walkthrough guide, or validation artifact needed before recording.\n- Upload prep: recommend packaging, title\u002Fthumbnail, description, chapters, or final metadata work before broader strategy work.\n- Performance review: recommend metrics, cadence, portfolio, peer benchmark, or owner-analytics export work before new content planning.\n- Owner analytics or private\u002Fmanual platform evidence: route to an explicit manual\u002Fguide handoff instead of inventing unavailable metrics.\n- Dirty intended artifacts: route to shipping\u002Fcommit\u002Fhandoff first, not another creator strategy skill.\n\nUse the default next-skill sequence only when no stronger user intent, missing artifact, manual blocker, or dirty-artifact handoff applies.\n\n## Alignment Page\n\nWhen this skill produces durable deliverables (research, specs, plans, reports, prototypes, or any document output), build a full-depth HTML alignment page following `ALIGNMENT-PAGE.md` in this skill's directory. Output: `alignment\u002Fyoutube-competitive-research-{topic}.html`.\n\n## Default Shipping Contract\n\nFollow the shared shipping contract convention in CLAUDE.md.\n",{"name":5,"type":30,"version":50,"description":7,"argument-hint":51,"interview_depth":52},"v0.3","\u003Cvideo URL or ID...> [--primary-channel \u003Cslug>] [--comments N] [--angle packaging|topic|retention|positioning]","none","master","96e2912af886dd1606b1b97df707715aef062b02","packs\u002Fyoutube-ops\u002Fclaude\u002Fyoutube-competitive-research\u002Farchive\u002Fv0.3",0,"https:\u002F\u002Fgithub.com\u002FGeorgeQLe\u002Fagentic-skills","https:\u002F\u002Fagentic-skills-blush.vercel.app","https:\u002F\u002Fgithub.com\u002FGeorgeQLe","georgeqle.com","George Le is a futurist and serial tech entrepreneur who wants to work toward a world where humanity is free from robotic tasks.","2026-11-10 04:07:24",{"source":8,"skillName":5,"commands":64,"prompt":74,"zipAvailable":75},[65,68,71],{"tool":66,"command":67},"npx","npx skills 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