[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6G0B0JBFX29TMHS7ZWDBVBX":3,"skill-same:01M6G0B0JBFX29TMHS7ZWDBVBX":76},{"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":44,"linkOnly":45,"markdownContent":46,"markdownBody":47,"frontMatter":48,"refName":52,"commitSha":53,"skillDirectory":54,"forksCount":55,"repositoryHtmlUrl":56,"repositoryHomepageUrl":57,"ownerHtmlUrl":58,"ownerBlogUrl":59,"ownerDescription":60,"updatedAt":61,"install":62,"resources":75},"01M6G0B0JBFX29TMHS7ZWDBVBX","youtube-concept-research",null,"Research a proposed YouTube video concept against successful direct and adjacent content, then extract evidence-backed lessons, concept variants, packaging directions, and differentiated execution guidance","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\u002Fcodex\u002Fyoutube-concept-research\u002Farchive\u002Fv0.5\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],"youtube concept research","youtube","research","extract","retention","guidance","evidence","packaging","channel","v0","direct","format","browse","Video","execution","content","search","2026-11-10 10:12:30",false,"---\nname: youtube-concept-research\ndescription: Research a proposed YouTube video concept against successful direct and adjacent content, then extract evidence-backed lessons, concept variants, packaging directions, and differentiated execution guidance\ntype: research\nversion: v0.5\nargument-hint: \"\\\"\u003Cvideo concept>\\\" [--channel \u003Cslug>] [--comparables N] [--angle search|browse|packaging|retention|format|all]\"\ninterview_depth: light\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 `npx skillpacks 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 Concept Research\n\nInvoke as `$youtube-concept-research`.\n\n## Report-First Approval Gate\n\nDefault to scope-first approval: before synthesized research, inspect only enough repository, user, and source context to propose research scope, source plan, assumptions, output paths, and approval questions in a `review` alignment page plus a concise conversation summary.\n\nDo not perform synthesized research, rank candidates, make recommendations, or write working packets or canonical deliverables until final compiled YAML approves the research scope. Minimal pre-approval discovery may identify available files, source categories, and open questions; label it as scope evidence, not findings.\n\nAfter approved research-scope YAML, perform the research and write only the non-canonical working packet defined in the staged workflow. Then update the `review` alignment page with findings and stop again for feedback-only YAML or final compiled YAML artifact approval before creating or updating canonical research, spec, or task files.\n\nDo 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 - Scope discovery and approval.** Inspect only enough repository, user, and source context to propose research scope, source plan, assumptions, output paths, and approval questions. Build the `review` HTML alignment page before synthesized research. The page must render the proposed scope, available source categories, known context, assumptions\u002Fconfidence, proposed working-packet and canonical output paths, and research-scope approval gates. Stop for final compiled YAML approval of the research scope. Do not perform synthesized research, rank candidates, make recommendations, or write working packets, canonical research, spec, or task files in Stage 1.\n2. **Stage 2 - Research and artifact review.** Only after approved research-scope YAML with no unresolved `needs-clarification`, unresolved `down` feedback, or other unresolved negative feedback, perform the synthesized research, run required source\u002Fcode checks, and 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. Raw evidence or search logs may remain as supporting evidence where this skill already requires them, but synthesized deliverables stay in the working packet. Update the `review` HTML alignment page with the full preliminary packet, evidence matrix, assumptions\u002Fconfidence register, source coverage gaps, proposed canonical file changes, and artifact 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 for artifact approval 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\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: a video concept, rough title, thesis, audience question, or topic idea.\n- Optional `--channel \u003Cslug>`: compare the concept against existing channel evidence under `research\u002Fyoutube\u002Fdata\u002F\u003Cslug>\u002F` and strategy artifacts such as channel audit, portfolio, peer benchmark, positioning, or programming.\n- Optional `--comparables N`: target number of comparable videos to inspect. Default 8, max 20.\n- Optional `--angle search|browse|packaging|retention|format|all`: default `all`.\n\n## Process\n\n1. Parse the concept into:\n   - audience job: problem, desire, identity, fear, curiosity, or aspiration served.\n   - content promise: what the viewer expects to learn, feel, decide, or see resolved.\n   - likely discovery mode: search, browse\u002Fhome, suggested, Shorts, or community\u002Ffandom.\n   - adjacent spaces: same topic, same audience job, same format, and counter-positioned angles.\n   - constraints: creator fit, product\u002Fbrand fit, production feasibility, and claims needing external verification.\n2. Build a query set. Include direct topic queries, problem\u002Fsearch-intent queries, browse-curiosity phrasings, adjacent-category analogs, and same-format\u002Fdifferent-niche examples.\n3. Find public comparable videos through YouTube search, available local tooling, or supplied URLs. Include:\n   - direct comparables on the same topic.\n   - audience-job comparables serving the same viewer need.\n   - format comparables with a transferable structure.\n   - counter-position comparables that prove a different angle can work.\n4. Require `yt-dlp` for public metadata when video URLs are selected:\n\n   ```bash\n   command -v yt-dlp\n   ```\n\n5. 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.\n6. 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.\n7. Score each comparable for usefulness, not just raw popularity:\n   - views\u002Fday or recent momentum.\n   - apparent outlier status relative to the channel when public channel evidence is available.\n   - title\u002Fthumbnail promise clarity.\n   - transcript\u002Fstructure evidence availability.\n   - audience overlap with the concept.\n   - discovery-mode fit.\n   - production feasibility for the user's channel.\n8. Analyze market lessons:\n   - Packaging: title pattern, thumbnail promise, specificity, stakes, novelty, expectation match, and avoidable clickbait.\n   - Search\u002Fbrowse fit: whether the concept should lean toward query satisfaction, curiosity, identity, trend, story, or suggested-video adjacency.\n   - Hook and retention: first 30-60 seconds, setup length, proof, tension loops, examples, pattern changes, payoff, and CTA.\n   - Format: essay, tutorial, teardown, case study, build-in-public, experiment, challenge, reaction, documentary, interview, Shorts, or hybrid.\n   - Audience signal: comment themes only when comments were captured; separate praise, objections, confusion, requests, and noise.\n9. Produce 3-5 concept variants. At minimum include the best search-led, browse-led, and differentiated\u002Fcounter-positioned variants when evidence supports them.\n10. Recommend one primary direction and explain what to adapt, avoid, or counter-position. If the evidence is weak, recommend the minimum follow-up research rather than over-committing.\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\u002Fconcept-research-\u003Cslug>-YYYY-MM-DD.md\n```\n\nUse this structure:\n\n```markdown\n# YouTube Concept Research - [Concept]\n\n> Concept: [user concept]\n> Channel: [slug or not provided]\n> Date captured: YYYY-MM-DD\n> Evidence: [raw paths and search notes used]\n> Angle: search \u002F browse \u002F packaging \u002F retention \u002F format \u002F all\n\n## Evidence Coverage\n\n| Comparable | Type | Metadata | Transcript | Comments | Notes |\n|---|---|---|---|---|---|\n| [Title](URL) | Direct \u002F audience-job \u002F format \u002F counter-position | Available \u002F Missing | Available \u002F Missing | Available \u002F Missing \u002F Not requested | ... |\n\n## Concept Brief\n\n[Audience job, promise, likely discovery mode, constraints, and claims needing verification.]\n\n## Search And Discovery Map\n\n| Query \u002F surface | Intent | Evidence | Opportunity | Caveat |\n|---|---|---|---|---|\n| ... | Search \u002F browse \u002F suggested \u002F Shorts \u002F community | ... | ... | ... |\n\n## Comparable Performance Snapshot\n\n| Video | Published | Duration | Views | Likes | Comments | Views\u002Fday | Comparable value |\n|---|---|---:|---:|---:|---:|---:|---|\n| ... | ... | ... | ... | ... | ... | ... | Direct \u002F audience-job \u002F format \u002F counter-position |\n\n## Why These Videos Likely Worked\n\n1. [Hypothesis] - [public evidence]\n2. [Hypothesis] - [public evidence]\n3. [Hypothesis] - [public evidence]\n\n## Audience Jobs And Market Pattern\n\n[What viewers repeatedly respond to, what category promises are common, and what gaps remain.]\n\n## Packaging Lessons\n\n- **Title patterns**: ...\n- **Thumbnail promises**: ...\n- **Specificity and stakes**: ...\n- **Expectation match risks**: ...\n\n## Hook, Structure, And Retention Lessons\n\n- **Opening 30-60 seconds**: ...\n- **Proof and examples**: ...\n- **Tension \u002F payoff**: ...\n- **CTA \u002F ending**: ...\n\n## Concept Variants\n\n| Variant | Discovery mode | Core promise | Evidence | Risk | Recommendation |\n|---|---|---|---|---|---|\n| ... | Search \u002F browse \u002F suggested \u002F Shorts \u002F community | ... | ... | ... | ... |\n\n## Recommended Direction\n\n[Primary concept direction, why it is strongest, what to adapt, what to avoid, and how to differentiate.]\n\n## Title And Thumbnail Territories\n\n| Territory | Example title pattern | Thumbnail direction | Evidence | Risk |\n|---|---|---|---|---|\n| ... | ... | ... | ... | ... |\n\n## Draft Video Architecture\n\n[High-level outline only: hook, setup, proof\u002Fexamples, turn, payoff, CTA. Do not write a full script unless explicitly asked.]\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, missing comments, uncertain search demand, absent owner analytics, private metrics unavailable, or factual claims needing independent verification.]\n```\n\n## Constraints\n\n- Separate observed public evidence from performance hypotheses and recommendations.\n- Do not treat public views as proof of CTR, retention, satisfaction, revenue, subscriber conversion, or profitability.\n- Do not infer private YouTube Studio metrics unless the user provides them.\n- Do not optimize only for clicks; evaluate title\u002Fthumbnail promise against likely watch-time and expectation match.\n- Do not invent metrics, transcripts, comments, thumbnail details, rankings, or distribution causes.\n- Do not recommend copying another creator's title, thumbnail, hook, format identity, or brand. Translate lessons into differentiated execution.\n- Date every search\u002Franking observation and state personalization, geography, and account caveats.\n- When the concept relies on factual, legal, medical, financial, political, or current-event claims, recommend independent source verification outside YouTube.\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-concept-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 `npx skillpacks 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 Concept Research\n\nInvoke as `$youtube-concept-research`.\n\n## Report-First Approval Gate\n\nDefault to scope-first approval: before synthesized research, inspect only enough repository, user, and source context to propose research scope, source plan, assumptions, output paths, and approval questions in a `review` alignment page plus a concise conversation summary.\n\nDo not perform synthesized research, rank candidates, make recommendations, or write working packets or canonical deliverables until final compiled YAML approves the research scope. Minimal pre-approval discovery may identify available files, source categories, and open questions; label it as scope evidence, not findings.\n\nAfter approved research-scope YAML, perform the research and write only the non-canonical working packet defined in the staged workflow. Then update the `review` alignment page with findings and stop again for feedback-only YAML or final compiled YAML artifact approval before creating or updating canonical research, spec, or task files.\n\nDo 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 - Scope discovery and approval.** Inspect only enough repository, user, and source context to propose research scope, source plan, assumptions, output paths, and approval questions. Build the `review` HTML alignment page before synthesized research. The page must render the proposed scope, available source categories, known context, assumptions\u002Fconfidence, proposed working-packet and canonical output paths, and research-scope approval gates. Stop for final compiled YAML approval of the research scope. Do not perform synthesized research, rank candidates, make recommendations, or write working packets, canonical research, spec, or task files in Stage 1.\n2. **Stage 2 - Research and artifact review.** Only after approved research-scope YAML with no unresolved `needs-clarification`, unresolved `down` feedback, or other unresolved negative feedback, perform the synthesized research, run required source\u002Fcode checks, and 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. Raw evidence or search logs may remain as supporting evidence where this skill already requires them, but synthesized deliverables stay in the working packet. Update the `review` HTML alignment page with the full preliminary packet, evidence matrix, assumptions\u002Fconfidence register, source coverage gaps, proposed canonical file changes, and artifact 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 for artifact approval 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\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: a video concept, rough title, thesis, audience question, or topic idea.\n- Optional `--channel \u003Cslug>`: compare the concept against existing channel evidence under `research\u002Fyoutube\u002Fdata\u002F\u003Cslug>\u002F` and strategy artifacts such as channel audit, portfolio, peer benchmark, positioning, or programming.\n- Optional `--comparables N`: target number of comparable videos to inspect. Default 8, max 20.\n- Optional `--angle search|browse|packaging|retention|format|all`: default `all`.\n\n## Process\n\n1. Parse the concept into:\n   - audience job: problem, desire, identity, fear, curiosity, or aspiration served.\n   - content promise: what the viewer expects to learn, feel, decide, or see resolved.\n   - likely discovery mode: search, browse\u002Fhome, suggested, Shorts, or community\u002Ffandom.\n   - adjacent spaces: same topic, same audience job, same format, and counter-positioned angles.\n   - constraints: creator fit, product\u002Fbrand fit, production feasibility, and claims needing external verification.\n2. Build a query set. Include direct topic queries, problem\u002Fsearch-intent queries, browse-curiosity phrasings, adjacent-category analogs, and same-format\u002Fdifferent-niche examples.\n3. Find public comparable videos through YouTube search, available local tooling, or supplied URLs. Include:\n   - direct comparables on the same topic.\n   - audience-job comparables serving the same viewer need.\n   - format comparables with a transferable structure.\n   - counter-position comparables that prove a different angle can work.\n4. Require `yt-dlp` for public metadata when video URLs are selected:\n\n   ```bash\n   command -v yt-dlp\n   ```\n\n5. 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.\n6. 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.\n7. Score each comparable for usefulness, not just raw popularity:\n   - views\u002Fday or recent momentum.\n   - apparent outlier status relative to the channel when public channel evidence is available.\n   - title\u002Fthumbnail promise clarity.\n   - transcript\u002Fstructure evidence availability.\n   - audience overlap with the concept.\n   - discovery-mode fit.\n   - production feasibility for the user's channel.\n8. Analyze market lessons:\n   - Packaging: title pattern, thumbnail promise, specificity, stakes, novelty, expectation match, and avoidable clickbait.\n   - Search\u002Fbrowse fit: whether the concept should lean toward query satisfaction, curiosity, identity, trend, story, or suggested-video adjacency.\n   - Hook and retention: first 30-60 seconds, setup length, proof, tension loops, examples, pattern changes, payoff, and CTA.\n   - Format: essay, tutorial, teardown, case study, build-in-public, experiment, challenge, reaction, documentary, interview, Shorts, or hybrid.\n   - Audience signal: comment themes only when comments were captured; separate praise, objections, confusion, requests, and noise.\n9. Produce 3-5 concept variants. At minimum include the best search-led, browse-led, and differentiated\u002Fcounter-positioned variants when evidence supports them.\n10. Recommend one primary direction and explain what to adapt, avoid, or counter-position. If the evidence is weak, recommend the minimum follow-up research rather than over-committing.\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\u002Fconcept-research-\u003Cslug>-YYYY-MM-DD.md\n```\n\nUse this structure:\n\n```markdown\n# YouTube Concept Research - [Concept]\n\n> Concept: [user concept]\n> Channel: [slug or not provided]\n> Date captured: YYYY-MM-DD\n> Evidence: [raw paths and search notes used]\n> Angle: search \u002F browse \u002F packaging \u002F retention \u002F format \u002F all\n\n## Evidence Coverage\n\n| Comparable | Type | Metadata | Transcript | Comments | Notes |\n|---|---|---|---|---|---|\n| [Title](URL) | Direct \u002F audience-job \u002F format \u002F counter-position | Available \u002F Missing | Available \u002F Missing | Available \u002F Missing \u002F Not requested | ... |\n\n## Concept Brief\n\n[Audience job, promise, likely discovery mode, constraints, and claims needing verification.]\n\n## Search And Discovery Map\n\n| Query \u002F surface | Intent | Evidence | Opportunity | Caveat |\n|---|---|---|---|---|\n| ... | Search \u002F browse \u002F suggested \u002F Shorts \u002F community | ... | ... | ... |\n\n## Comparable Performance Snapshot\n\n| Video | Published | Duration | Views | Likes | Comments | Views\u002Fday | Comparable value |\n|---|---|---:|---:|---:|---:|---:|---|\n| ... | ... | ... | ... | ... | ... | ... | Direct \u002F audience-job \u002F format \u002F counter-position |\n\n## Why These Videos Likely Worked\n\n1. [Hypothesis] - [public evidence]\n2. [Hypothesis] - [public evidence]\n3. [Hypothesis] - [public evidence]\n\n## Audience Jobs And Market Pattern\n\n[What viewers repeatedly respond to, what category promises are common, and what gaps remain.]\n\n## Packaging Lessons\n\n- **Title patterns**: ...\n- **Thumbnail promises**: ...\n- **Specificity and stakes**: ...\n- **Expectation match risks**: ...\n\n## Hook, Structure, And Retention Lessons\n\n- **Opening 30-60 seconds**: ...\n- **Proof and examples**: ...\n- **Tension \u002F payoff**: ...\n- **CTA \u002F ending**: ...\n\n## Concept Variants\n\n| Variant | Discovery mode | Core promise | Evidence | Risk | Recommendation |\n|---|---|---|---|---|---|\n| ... | Search \u002F browse \u002F suggested \u002F Shorts \u002F community | ... | ... | ... | ... |\n\n## Recommended Direction\n\n[Primary concept direction, why it is strongest, what to adapt, what to avoid, and how to differentiate.]\n\n## Title And Thumbnail Territories\n\n| Territory | Example title pattern | Thumbnail direction | Evidence | Risk |\n|---|---|---|---|---|\n| ... | ... | ... | ... | ... |\n\n## Draft Video Architecture\n\n[High-level outline only: hook, setup, proof\u002Fexamples, turn, payoff, CTA. Do not write a full script unless explicitly asked.]\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, missing comments, uncertain search demand, absent owner analytics, private metrics unavailable, or factual claims needing independent verification.]\n```\n\n## Constraints\n\n- Separate observed public evidence from performance hypotheses and recommendations.\n- Do not treat public views as proof of CTR, retention, satisfaction, revenue, subscriber conversion, or profitability.\n- Do not infer private YouTube Studio metrics unless the user provides them.\n- Do not optimize only for clicks; evaluate title\u002Fthumbnail promise against likely watch-time and expectation match.\n- Do not invent metrics, transcripts, comments, thumbnail details, rankings, or distribution causes.\n- Do not recommend copying another creator's title, thumbnail, hook, format identity, or brand. Translate lessons into differentiated execution.\n- Date every search\u002Franking observation and state personalization, geography, and account caveats.\n- When the concept relies on factual, legal, medical, financial, political, or current-event claims, recommend independent source verification outside YouTube.\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-concept-research-{topic}.html`.\n\n## Default Shipping Contract\n\nFollow the shared shipping contract convention in CLAUDE.md.\n",{"name":5,"type":29,"version":49,"description":7,"argument-hint":50,"interview_depth":51},"v0.5","\"\u003Cvideo concept>\" [--channel \u003Cslug>] [--comparables N] [--angle search|browse|packaging|retention|format|all]","light","master","96e2912af886dd1606b1b97df707715aef062b02","packs\u002Fyoutube-ops\u002Fcodex\u002Fyoutube-concept-research\u002Farchive\u002Fv0.5",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":63,"prompt":73,"zipAvailable":74},[64,67,70],{"tool":65,"command":66},"npx","npx skills add 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