[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6QJ3GSC902PCXSR9DXJEN8W":3,"skill-same:01M6QJ3GSC902PCXSR9DXJEN8W":82},{"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":29,"publishedAt":49,"linkOnly":50,"markdownContent":51,"markdownBody":52,"frontMatter":53,"refName":59,"commitSha":60,"skillDirectory":61,"forksCount":14,"repositoryHtmlUrl":62,"repositoryHomepageUrl":63,"ownerHtmlUrl":64,"ownerBlogUrl":65,"ownerDescription":66,"updatedAt":67,"install":68,"resources":81},"01M6QJ3GSC902PCXSR9DXJEN8W","litreview",null,"Academic literature orientation skill that searches papers via free keyless APIs (PubMed E-utilities + OpenAlex) by default — with the Consensus MCP as an optional enhancement lane when connected — builds a strategic search plan using PICO (default) or SPIDER \u002F Decomposition \u002F hybrid as fallbacks, and synthesizes findings into a formatted Word (.docx) research guide. Grill-me intake (research question specificity + framework hint + tentative depth) before the recon search; a second forcing checkpoint after Phase 2 confirms framework + sub-areas + depth before searches consume budget. Configurable depth (5\u002F10\u002F20 queries) controls coverage vs. speed. Output is a 'launching pad' — an orientation guide that lets a researcher dive in confidently, not a finished review. Use when the user starts literature-oriented research (e.g., 'litreview on [topic]', 'literature review on [topic]', 'I'm starting a literature review on X', 'I'm writing a paper on X', 'help me research X', 'I'm doing research on X', 'can you help me research X'). Do NOT use for single one-off paper searches wanting a quick list — that's a plain PubMed\u002FOpenAlex (or Consensus) query.","Tashima-Tarsh\u002FDisha6.6","Tashima-Tarsh","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F269906498?v=4","01M6PBH98X03XERH0HFT4PFB1P","NITISH KUMAR","01M6PBH9DA0M6KM3Z12P848F0K",0,"MIT","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Flitreview\u002FSKILL.md",[18,23,26],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000002","文档处理","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":27,"categoryName":28,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[5,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48],"guide","phase","enhancement","you","quick","pubmed","literature","free","as","grill","docx","list","question","consensus","plan","Budget","search","writing","MCP","2026-11-10 10:12:30",false,"---\nname: litreview\ndescription: \"Academic literature orientation skill that searches papers via free keyless APIs (PubMed E-utilities + OpenAlex) by default — with the Consensus MCP as an optional enhancement lane when connected — builds a strategic search plan using PICO (default) or SPIDER \u002F Decomposition \u002F hybrid as fallbacks, and synthesizes findings into a formatted Word (.docx) research guide. Grill-me intake (research question specificity + framework hint + tentative depth) before the recon search; a second forcing checkpoint after Phase 2 confirms framework + sub-areas + depth before searches consume budget. Configurable depth (5\u002F10\u002F20 queries) controls coverage vs. speed. Output is a 'launching pad' — an orientation guide that lets a researcher dive in confidently, not a finished review. Use when the user starts literature-oriented research (e.g., 'litreview on [topic]', 'literature review on [topic]', 'I'm starting a literature review on X', 'I'm writing a paper on X', 'help me research X', 'I'm doing research on X', 'can you help me research X'). Do NOT use for single one-off paper searches wanting a quick list — that's a plain PubMed\u002FOpenAlex (or Consensus) query.\"\nlicense: MIT\nmetadata:\n  source_spec: \"megaprompts\u002F09-litreview-megaprompt.md\"\n  build_pattern: \"Path B (direct conversion)\"\n  research_pack_convention: \"Agent Integrity Rules verbatim per PR #657 audit; sibling of pulse\"\n  version: 1.1.0\n---\n\n# Litreview — Academic Literature Orientation\n\n> **Portability:** Works anywhere with outbound HTTPS — the default search lane is **free keyless APIs** (PubMed E-utilities + OpenAlex, no account, no key, no MCP). The **Consensus MCP is an optional enhancement lane** used only when connected in this session. Node.js with `docx` package is required for document generation, and (in CLI) `bash_tool`. Works in Claude Code CLI natively and in Claude.ai with Code Execution.\n\nProduce a **launching pad** — not a finished literature review, but an orientation document that gives a researcher entering an unfamiliar field everything they need to start reading and searching with confidence. Think: what a generous colleague who knows the field would tell you over coffee.\n\n## Search Lanes\n\n| Lane | When | How |\n|---|---|---|\n| **Free lane (default)** | Always available; no key, no plan, no MCP | PubMed E-utilities + OpenAlex via `scripts\u002Ffree_search.py` or direct HTTPS (URL templates below) |\n| **Consensus lane (optional enhancement)** | Only when Consensus MCP tools are available in this session | Run Consensus queries *in addition to* the free lane for its synthesized answer cards |\n\n**Lane check (one runtime check — replaces all tier detection):** if the Consensus MCP tools are **not** available in this session, use the free lane — **do not attempt tier detection**, do not parse marketing copy, do not ask the user about their Consensus plan. If Consensus IS available, additionally run its searches and merge results (dedupe by DOI\u002Ftitle).\n\n### Free-lane URL templates (exact)\n\n**PubMed E-utilities** (keyless; etiquette: ≤3 requests\u002Fsecond):\n\n1. Search → PMIDs: `https:\u002F\u002Feutils.ncbi.nlm.nih.gov\u002Fentrez\u002Feutils\u002Fesearch.fcgi?db=pubmed&term=\u003Curlencoded-query>&retmode=json&retmax=20&sort=relevance`\n   — read `esearchresult.idlist` (PMIDs) and `esearchresult.count`.\n2. PMIDs → metadata: `https:\u002F\u002Feutils.ncbi.nlm.nih.gov\u002Fentrez\u002Feutils\u002Fesummary.fcgi?db=pubmed&id=\u003Cpmid1,pmid2,...>&retmode=json`\n   — per `result[\u003Cpmid>]` read `title`, `authors[].name`, `pubdate`, `fulljournalname`, `articleids[]` (the `idtype: \"doi\"` entry).\n3. Era-gating: append `&datetype=pdat&mindate=2021&maxdate=3000` (recent) or `&maxdate=2015` (historical).\n4. Paper URL: `https:\u002F\u002Fpubmed.ncbi.nlm.nih.gov\u002F\u003CPMID>\u002F`.\n\n**OpenAlex** (keyless; add `&mailto=\u003Cemail>` for the polite pool — faster + more reliable):\n\n1. Search: `https:\u002F\u002Fapi.openalex.org\u002Fworks?search=\u003Curlencoded-query>&per-page=20&mailto=\u003Cemail>`\n   — per `results[]` read `display_name` (title), `publication_year`, `cited_by_count`, `doi`, `id` (OpenAlex URL), `authorships[].author.display_name`, `primary_location.source.display_name` (venue).\n2. Era-gating: `&filter=from_publication_date:2021-01-01` or `&filter=to_publication_date:2015-12-31`.\n3. Review articles: `&filter=type:review`.\n\nOpenAlex's `cited_by_count` is the citation-count source for the cross-search intelligence layer (PubMed esummary returns no counts).\n\n## Agent Integrity Rules (Research-Pack Convention)\n\nInherited from the research-pack convention; locked verbatim per PR #657's cross-skill consistency audit.\n\n- **Source discipline.** Only cite papers returned by THIS session's searches (free lane and\u002For Consensus). Training knowledge labeled `[Not from search — model knowledge]` and excluded from cited count. Sparse results stated explicitly, never silently filled.\n- **Counting discipline.** Three numbers tracked: searches executed \u002F unique papers received (deduplicated by DOI\u002Ftitle) \u002F papers cited. Every cited paper has a retrievable URL from this session (PubMed, DOI, OpenAlex, or Consensus). Use `scripts\u002Fcitation_tracker.py` for deterministic counts.\n- **Rate-limit etiquette.** PubMed E-utilities: ≤3 requests\u002Fsecond keyless. OpenAlex: polite pool via `mailto`. Consensus (if connected): 1 query\u002Fsec, sequential execution mandatory. Default discipline: **sequential, 1 query\u002Fsec across all lanes.**\n- **Retry policy.** On failure → wait 3s → retry once → log. After 3 consecutive failures: stop, alert user, share what was collected.\n- **Lane check.** One runtime check at session start: Consensus MCP tools available or not. No tier detection, ever.\n\nSee [`references\u002Fsearch_budget_allocation.md`](references\u002Fsearch_budget_allocation.md) for the sequential-execution rationale + budget ceilings.\n\n## Error Handling\n\n| Failure | Behavior |\n|---|---|\n| Rate-limit \u002F HTTP error on any lane | Wait 3s, retry once, log outcome |\n| Search returns 0 results | Note explicitly; \"either niche terminology or genuine gap\"; never silently fill |\n| Network unavailable (free lane exits 2) | Stop, alert user — the free lane needs outbound HTTPS; nothing to detect or upgrade |\n| 3 consecutive failures | Stop searching, alert user, share what's collected, ask how to proceed |\n| Sub-area returns thin results (\u003C5 papers) | Flag in audit; suggest manual Google Scholar \u002F Scopus supplementation |\n| User wants to adjust sub-areas | Update table, re-confirm before searching |\n| DOCX validation fails | Unpack XML, fix, repack |\n\n## Phase 0: Grill-Me Intake (3 forcing questions, one at a time)\n\nEach question carries explicit \"why I'm asking\". Stop condition: max 3 before Phase 1.\n\n### Q1 (root) — Research question specificity\n\n> **State the research question in 1–2 sentences. Specific is better — \"How do LLMs perform on clinical reasoning tasks compared to physicians?\" beats \"AI in medicine\". Vague questions produce vague reviews.**\n>\n> *Why I'm asking:* The reconnaissance search hinges on precise terminology. Vague questions produce thin recon results that don't yield a useful framework breakdown.\n\n**Refuse mush.** Re-ask once with examples if user is too broad. If still vague, deliver with explicit \"broad-scope orientation, not depth review\" caveat.\n\n### Q2 (depends on Q1) — Framework hint\n\n> **Framework — pick one or say \"you pick\":**\n>\n> 1. **PICO** (Population \u002F Intervention \u002F Comparison \u002F Outcome — most clinical questions)\n> 2. **SPIDER** (Sample \u002F Phenomenon \u002F Design \u002F Evaluation \u002F Research-type — social\u002Fqualitative)\n> 3. **Decomposition** (Problem \u002F Solution \u002F Evaluation \u002F Limitations — technology-focused)\n> 4. **Hybrid** (you pick which components from which framework)\n> 5. **You pick** — analyze Q1 and recommend\n>\n> *Why I'm asking:* PICO is the default for ~70% of clinical questions but maps poorly to qualitative work or technology evaluation. Picking upfront saves the recon search from suggesting a misaligned framework.\n\nForcing choice with default (\"you pick\"). The skill surfaces its own framework recommendation after the recon search so user can override. Use `scripts\u002Fframework_recommender.py` for the heuristic.\n\nSee [`references\u002Fframework_selection.md`](references\u002Fframework_selection.md) for PICO \u002F SPIDER \u002F Decomposition canon.\n\n### Q3 (depends on Q1) — Tentative depth\n\n> **Tentative depth — pick one. Final confirmation comes after the framework breakdown:**\n>\n> 1. **Quick scan** (5 searches)\n> 2. **Standard review** (10 searches)\n> 3. **Deep dive** (20 searches)\n>\n> *Why I'm asking:* I ask this twice — once now to calibrate the recon search emphasis, once after the framework breakdown to confirm. Tentative answer affects which sub-areas to surface first; final answer drives search budget allocation.\n\nForcing choice. **Re-asked** at the post-Phase-2 checkpoint after the user has seen the framework breakdown.\n\n**Stop condition:** 3 questions max before Phase 1. The post-Phase-2 checkpoint is its own grill-me moment (framework table + sub-area-adjustment + depth-reconfirmation).\n\n## Phase 1: Initial Reconnaissance\n\n**One broad recon search** to map themes, terminology, methodological distinctions.\n\n- Run the lane check (Consensus available or not), then:\n  - Free lane: `python scripts\u002Ffree_search.py --query \"\u003Cbroad version of Q1>\" --source both --max 20` (or the esearch\u002Fworks URL templates above)\n  - **If Consensus is available, additionally** run one broad Consensus search and merge\n- Query: broad version of Q1 (terminology variants are okay; first search casts wide)\n- Record: `citation_tracker.py --action record_search --session NAME --query \"...\"`\n- Record received count: `citation_tracker.py --action record_papers_received --session NAME --count N`\n\nSynthesize for the checkpoint:\n- Themes that surfaced\n- Terminology variations (e.g., \"LLM\" vs \"large language model\" vs \"GPT-style model\")\n- Methodological distinctions (clinical trials vs benchmark eval vs case study)\n- Coverage gaps (sub-questions absent from recon results)\n\n## Phase 2: Framework Selection + Sub-area Generation\n\nChoose framework (from Q2 OR override based on recon):\n- **PICO** — most clinical questions (~70% default)\n- **SPIDER** — social \u002F qualitative\n- **Decomposition** — technology focus (Problem \u002F Solution \u002F Evaluation \u002F Limitations)\n- **Hybrid** — explicit cross-framework mapping\n\nGenerate **4-5 sub-area questions** mapped to framework components. Each becomes a targeted Phase 3 search.\n\n## Checkpoint (grill-me forcing-options moment)\n\nAfter Phase 2, halt and present:\n\n### 3-4 sentence recon summary\n- What themes surfaced\n- Terminology landscape\n- Evidence landscape characterization\n\n### Framework breakdown table\n\n| Framework Component | How It Maps to This Topic | Proposed Sub-area to Explore |\n|---|---|---|\n| (Component 1) | ... | Sub-area 1 |\n| (Component 2) | ... | Sub-area 2 |\n| (Component 3) | ... | Sub-area 3 |\n| (Component 4) | ... | Sub-area 4 |\n| Cross-cutting theme | ... | Sub-area 5 |\n\n### Depth re-confirmation (forcing choice)\n\nSurface the **practical constraint**: search lane in use (free \u002F free+Consensus) + approximate ceiling at ~20 results per query per source.\n\n- Quick scan (5 searches × ~20 results = ~100 papers max per source)\n- Standard review (10 searches × ~20 = ~200 papers per source)\n- Deep dive (20 searches × ~20 = ~400 papers per source)\n\n### Sub-area forcing options\n\n- \"Looks good — proceed with these sub-areas\"\n- \"Adjust: add sub-area on [X]\"\n- \"Adjust: remove and replace [Y] with [Z]\"\n- \"Restart with different framework\"\n\n### Why I'm asking (the rationale)\n\n> A wrong framework or sub-area set wastes the search budget. This is the **last cheap moment** to correct course.\n\n**Wait for user response before Phase 3.** Refuse to start Phase 3 without explicit user choice.\n\n## Phase 3: Targeted Searches\n\nSequential (1 query\u002Fsec), budget per depth tier. Every search runs on the free lane (`free_search.py` or the URL templates); **if Consensus is available, additionally** run the same query there and merge. See [`references\u002Fsearch_budget_allocation.md`](references\u002Fsearch_budget_allocation.md) for full canon.\n\n### Quick scan (5 searches)\n- 5 sub-area searches (one per sub-area)\n- Skip era-gated + review-specific\n\n### Standard review (10 searches)\n- 5 sub-area searches\n- 2 review article searches (top 2 sub-areas): `\"systematic review [topic]\"` \u002F `\"meta-analysis [topic]\"` (OpenAlex: add `&filter=type:review`)\n- 2 era-gated searches (most important sub-area): historical (PubMed `&maxdate=2015` \u002F OpenAlex `to_publication_date:2015-12-31`) + recent (PubMed `&mindate=2021` \u002F OpenAlex `from_publication_date:2021-01-01`)\n- 1 follow-up on highest-cited paper using its key terms + a from-date after its publication year\n\n### Deep dive (20 searches)\n- 5 sub-area searches\n- 5 review article searches (one per sub-area)\n- 4 era-gated searches (top 2 sub-areas, old + new each)\n- 3 follow-ups on top 3 highest-cited papers\n- 3 spare for emerging threads (surprising findings to chase)\n\nThroughout: 1 q\u002Fsec rate limit. Sequential. Confirm response before next call. Record each via `citation_tracker.py`.\n\n## Cross-Search Intelligence\n\nThree trackers across ALL search results — run `scripts\u002Fcross_search_aggregator.py --session NAME` after Phase 3 completes:\n\n1. **Repeat-hit papers** — same paper appearing in 3+ sub-area searches = likely foundational\n2. **Recurring authors** — same author in multiple searches = dominant research group; top 3-5 most frequent matter\n3. **Citation-per-year heuristic** — a 2023 paper with 150 citations >> 2008 paper with 150 citations. Use OpenAlex `cited_by_count` for seminal-work identification.\n\nThese feed the \"Start Here\" + \"Key Research Groups\" + \"Bibliography\" DOCX sections.\n\n## Phase 4: DOCX Research Guide\n\nGenerate via Node.js + `docx` library. 8 sections (see [`references\u002Fdocx_8_sections.md`](references\u002Fdocx_8_sections.md) for full spec):\n\n1. **Topic Overview** — single tight paragraph (4-6 sentences)\n2. **Start Here — Priority Reading Order** — 5-7 papers ordered: best recent review → foundational → 2-3 frontier → gap\u002Fcontroversy. Each: hyperlinked title + authors\u002Fyear + 1-sentence contribution + 1-sentence \"what to look for\"\n3. **How the Field Got Here** — chronological narrative (1-2 paragraphs) + timeline table (5-8 milestones: Year \u002F Milestone \u002F Significance) + terminology evolution note\n4. **Sub-area Guides** (one per sub-area, 4 parts each)\n   - 4a. What the Research Shows (2-3 sentence synthesis with inline citations)\n   - 4b. Key Papers (3-5 hyperlinked papers with citation count, year, 1-sentence importance)\n   - 4c. Key Search Terms (6-10 keywords, synonyms, MeSH, historical terms)\n   - 4d. Boolean Search Strings (2-3 ready-to-paste strings)\n5. **Key Research Groups** — top 3-5 authors\u002Fgroups with affiliations, sub-area coverage, representative paper link (from cross-search aggregator)\n6. **Open Questions & Gaps** — three categories: methodological \u002F population-context \u002F conceptual-theoretical. Each gap explains *why it matters*.\n7. **Bibliography** — alphabetical by first author. Every entry has a clickable link: PubMed URL or DOI (free lane) \u002F \"View on Consensus\" (Consensus-sourced). Every inline citation matches a bibliography entry.\n8. **Audit Log** — search summary table (#, query, filters, papers returned, status), counts block, coverage notes including which search lane was used (free \u002F free+Consensus)\n\n### DOCX Technical Requirements\n\nDocument the key `docx` library patterns:\n\n- Page: US Letter, 1-inch margins\n- Lists: `LevelFormat.BULLET` (never unicode bullets)\n- Hyperlinks: `ExternalHyperlink` with `style: \"Hyperlink\"`, full URL (never truncated)\n- Tables: dual widths (`columnWidths` + cell `width`), `ShadingType.CLEAR`\n- Validation step after save (zip-integrity check: `python3 -c \"import zipfile,sys; zipfile.ZipFile(sys.argv[1]).testzip()\" output.docx` — no output = intact — then confirm the required sections are present)\n\nReference the **docx skill** for setup patterns and best practices.\n\n## Output\n\n```\nresearch_guide_\u003Ctopic-slug>_\u003CYYYY-MM-DD>.docx\n```\n\nPlus:\n- Chat summary block: \"Saved: \u003Cpath>. Audit: N searches × M unique papers \u002F K cited. Search lane: \u003Cfree | free+Consensus>.\"\n- Audit log printed inline if user asks for it\n\n## Tooling\n\n| Script | Role |\n|---|---|\n| `scripts\u002Ffree_search.py` | Free keyless search lane — PubMed E-utilities + OpenAlex via stdlib urllib (`--query`, `--source pubmed|openalex|both`, `--max`, `--json`, `--mailto`; exits 2 with a clear message when offline) |\n| `scripts\u002Fcitation_tracker.py` | JSON-backed three-count audit at `~\u002F.litreview_sessions\u002F\u003Csession>.json` |\n| `scripts\u002Fframework_recommender.py` | Heuristic PICO\u002FSPIDER\u002FDecomposition suggestion from research question |\n| `scripts\u002Fcross_search_aggregator.py` | Repeat-hits + recurring-authors + citation-per-year ranking after Phase 3 |\n\n## References\n\n- [`references\u002Fframework_selection.md`](references\u002Fframework_selection.md) — PICO \u002F SPIDER \u002F Decomposition canon (7+ sources)\n- [`references\u002Fsearch_budget_allocation.md`](references\u002Fsearch_budget_allocation.md) — depth tiers + cross-search intelligence + sequential execution rationale (7+ sources)\n- [`references\u002Fdocx_8_sections.md`](references\u002Fdocx_8_sections.md) — research guide DOCX spec + technical requirements (7+ sources)\n\n## Anti-Patterns To Reject\n\n- Parallelizing search calls (any lane)\n- Skipping the interactive checkpoint (running all searches without user confirmation)\n- Padding thin results with training knowledge\n- Defaulting to non-PICO framework without justification\n- Citing papers in chat that didn't come from this session's searches\n- Attempting Consensus plan-tier detection (deleted — the only runtime check is \"are the Consensus MCP tools available in this session?\")\n- Treating Consensus as required (it's an optional enhancement; the free lane is the default)\n- Skipping era-gated searches in standard\u002Fdeep budgets\n- Skipping cross-search intelligence (repeat-hits, recurring authors)\n- Truncating source URLs in hyperlinks\n\n---\n\n**Version:** 1.1.0\n**Source spec:** `megaprompts\u002F09-litreview-megaprompt.md` (maintainer-local draft spec — gitignored, not present in the public repository)\n**Build pattern:** Path B (direct conversion). Sibling of `pulse` (research-pack shape). v1.1.0: free keyless APIs (PubMed + OpenAlex) became the default search lane; Consensus demoted to optional enhancement; plan-tier detection deleted per the 2026-06 newgen audit + ClawHub rule #3 (no paid-service dependencies).\n","# Litreview — Academic Literature Orientation\n\n> **Portability:** Works anywhere with outbound HTTPS — the default search lane is **free keyless APIs** (PubMed E-utilities + OpenAlex, no account, no key, no MCP). The **Consensus MCP is an optional enhancement lane** used only when connected in this session. Node.js with `docx` package is required for document generation, and (in CLI) `bash_tool`. Works in Claude Code CLI natively and in Claude.ai with Code Execution.\n\nProduce a **launching pad** — not a finished literature review, but an orientation document that gives a researcher entering an unfamiliar field everything they need to start reading and searching with confidence. Think: what a generous colleague who knows the field would tell you over coffee.\n\n## Search Lanes\n\n| Lane | When | How |\n|---|---|---|\n| **Free lane (default)** | Always available; no key, no plan, no MCP | PubMed E-utilities + OpenAlex via `scripts\u002Ffree_search.py` or direct HTTPS (URL templates below) |\n| **Consensus lane (optional enhancement)** | Only when Consensus MCP tools are available in this session | Run Consensus queries *in addition to* the free lane for its synthesized answer cards |\n\n**Lane check (one runtime check — replaces all tier detection):** if the Consensus MCP tools are **not** available in this session, use the free lane — **do not attempt tier detection**, do not parse marketing copy, do not ask the user about their Consensus plan. If Consensus IS available, additionally run its searches and merge results (dedupe by DOI\u002Ftitle).\n\n### Free-lane URL templates (exact)\n\n**PubMed E-utilities** (keyless; etiquette: ≤3 requests\u002Fsecond):\n\n1. Search → PMIDs: `https:\u002F\u002Feutils.ncbi.nlm.nih.gov\u002Fentrez\u002Feutils\u002Fesearch.fcgi?db=pubmed&term=\u003Curlencoded-query>&retmode=json&retmax=20&sort=relevance`\n   — read `esearchresult.idlist` (PMIDs) and `esearchresult.count`.\n2. PMIDs → metadata: `https:\u002F\u002Feutils.ncbi.nlm.nih.gov\u002Fentrez\u002Feutils\u002Fesummary.fcgi?db=pubmed&id=\u003Cpmid1,pmid2,...>&retmode=json`\n   — per `result[\u003Cpmid>]` read `title`, `authors[].name`, `pubdate`, `fulljournalname`, `articleids[]` (the `idtype: \"doi\"` entry).\n3. Era-gating: append `&datetype=pdat&mindate=2021&maxdate=3000` (recent) or `&maxdate=2015` (historical).\n4. Paper URL: `https:\u002F\u002Fpubmed.ncbi.nlm.nih.gov\u002F\u003CPMID>\u002F`.\n\n**OpenAlex** (keyless; add `&mailto=\u003Cemail>` for the polite pool — faster + more reliable):\n\n1. Search: `https:\u002F\u002Fapi.openalex.org\u002Fworks?search=\u003Curlencoded-query>&per-page=20&mailto=\u003Cemail>`\n   — per `results[]` read `display_name` (title), `publication_year`, `cited_by_count`, `doi`, `id` (OpenAlex URL), `authorships[].author.display_name`, `primary_location.source.display_name` (venue).\n2. Era-gating: `&filter=from_publication_date:2021-01-01` or `&filter=to_publication_date:2015-12-31`.\n3. Review articles: `&filter=type:review`.\n\nOpenAlex's `cited_by_count` is the citation-count source for the cross-search intelligence layer (PubMed esummary returns no counts).\n\n## Agent Integrity Rules (Research-Pack Convention)\n\nInherited from the research-pack convention; locked verbatim per PR #657's cross-skill consistency audit.\n\n- **Source discipline.** Only cite papers returned by THIS session's searches (free lane and\u002For Consensus). Training knowledge labeled `[Not from search — model knowledge]` and excluded from cited count. Sparse results stated explicitly, never silently filled.\n- **Counting discipline.** Three numbers tracked: searches executed \u002F unique papers received (deduplicated by DOI\u002Ftitle) \u002F papers cited. Every cited paper has a retrievable URL from this session (PubMed, DOI, OpenAlex, or Consensus). Use `scripts\u002Fcitation_tracker.py` for deterministic counts.\n- **Rate-limit etiquette.** PubMed E-utilities: ≤3 requests\u002Fsecond keyless. OpenAlex: polite pool via `mailto`. Consensus (if connected): 1 query\u002Fsec, sequential execution mandatory. Default discipline: **sequential, 1 query\u002Fsec across all lanes.**\n- **Retry policy.** On failure → wait 3s → retry once → log. After 3 consecutive failures: stop, alert user, share what was collected.\n- **Lane check.** One runtime check at session start: Consensus MCP tools available or not. No tier detection, ever.\n\nSee [`references\u002Fsearch_budget_allocation.md`](references\u002Fsearch_budget_allocation.md) for the sequential-execution rationale + budget ceilings.\n\n## Error Handling\n\n| Failure | Behavior |\n|---|---|\n| Rate-limit \u002F HTTP error on any lane | Wait 3s, retry once, log outcome |\n| Search returns 0 results | Note explicitly; \"either niche terminology or genuine gap\"; never silently fill |\n| Network unavailable (free lane exits 2) | Stop, alert user — the free lane needs outbound HTTPS; nothing to detect or upgrade |\n| 3 consecutive failures | Stop searching, alert user, share what's collected, ask how to proceed |\n| Sub-area returns thin results (\u003C5 papers) | Flag in audit; suggest manual Google Scholar \u002F Scopus supplementation |\n| User wants to adjust sub-areas | Update table, re-confirm before searching |\n| DOCX validation fails | Unpack XML, fix, repack |\n\n## Phase 0: Grill-Me Intake (3 forcing questions, one at a time)\n\nEach question carries explicit \"why I'm asking\". Stop condition: max 3 before Phase 1.\n\n### Q1 (root) — Research question specificity\n\n> **State the research question in 1–2 sentences. Specific is better — \"How do LLMs perform on clinical reasoning tasks compared to physicians?\" beats \"AI in medicine\". Vague questions produce vague reviews.**\n>\n> *Why I'm asking:* The reconnaissance search hinges on precise terminology. Vague questions produce thin recon results that don't yield a useful framework breakdown.\n\n**Refuse mush.** Re-ask once with examples if user is too broad. If still vague, deliver with explicit \"broad-scope orientation, not depth review\" caveat.\n\n### Q2 (depends on Q1) — Framework hint\n\n> **Framework — pick one or say \"you pick\":**\n>\n> 1. **PICO** (Population \u002F Intervention \u002F Comparison \u002F Outcome — most clinical questions)\n> 2. **SPIDER** (Sample \u002F Phenomenon \u002F Design \u002F Evaluation \u002F Research-type — social\u002Fqualitative)\n> 3. **Decomposition** (Problem \u002F Solution \u002F Evaluation \u002F Limitations — technology-focused)\n> 4. **Hybrid** (you pick which components from which framework)\n> 5. **You pick** — analyze Q1 and recommend\n>\n> *Why I'm asking:* PICO is the default for ~70% of clinical questions but maps poorly to qualitative work or technology evaluation. Picking upfront saves the recon search from suggesting a misaligned framework.\n\nForcing choice with default (\"you pick\"). The skill surfaces its own framework recommendation after the recon search so user can override. Use `scripts\u002Fframework_recommender.py` for the heuristic.\n\nSee [`references\u002Fframework_selection.md`](references\u002Fframework_selection.md) for PICO \u002F SPIDER \u002F Decomposition canon.\n\n### Q3 (depends on Q1) — Tentative depth\n\n> **Tentative depth — pick one. Final confirmation comes after the framework breakdown:**\n>\n> 1. **Quick scan** (5 searches)\n> 2. **Standard review** (10 searches)\n> 3. **Deep dive** (20 searches)\n>\n> *Why I'm asking:* I ask this twice — once now to calibrate the recon search emphasis, once after the framework breakdown to confirm. Tentative answer affects which sub-areas to surface first; final answer drives search budget allocation.\n\nForcing choice. **Re-asked** at the post-Phase-2 checkpoint after the user has seen the framework breakdown.\n\n**Stop condition:** 3 questions max before Phase 1. The post-Phase-2 checkpoint is its own grill-me moment (framework table + sub-area-adjustment + depth-reconfirmation).\n\n## Phase 1: Initial Reconnaissance\n\n**One broad recon search** to map themes, terminology, methodological distinctions.\n\n- Run the lane check (Consensus available or not), then:\n  - Free lane: `python scripts\u002Ffree_search.py --query \"\u003Cbroad version of Q1>\" --source both --max 20` (or the esearch\u002Fworks URL templates above)\n  - **If Consensus is available, additionally** run one broad Consensus search and merge\n- Query: broad version of Q1 (terminology variants are okay; first search casts wide)\n- Record: `citation_tracker.py --action record_search --session NAME --query \"...\"`\n- Record received count: `citation_tracker.py --action record_papers_received --session NAME --count N`\n\nSynthesize for the checkpoint:\n- Themes that surfaced\n- Terminology variations (e.g., \"LLM\" vs \"large language model\" vs \"GPT-style model\")\n- Methodological distinctions (clinical trials vs benchmark eval vs case study)\n- Coverage gaps (sub-questions absent from recon results)\n\n## Phase 2: Framework Selection + Sub-area Generation\n\nChoose framework (from Q2 OR override based on recon):\n- **PICO** — most clinical questions (~70% default)\n- **SPIDER** — social \u002F qualitative\n- **Decomposition** — technology focus (Problem \u002F Solution \u002F Evaluation \u002F Limitations)\n- **Hybrid** — explicit cross-framework mapping\n\nGenerate **4-5 sub-area questions** mapped to framework components. Each becomes a targeted Phase 3 search.\n\n## Checkpoint (grill-me forcing-options moment)\n\nAfter Phase 2, halt and present:\n\n### 3-4 sentence recon summary\n- What themes surfaced\n- Terminology landscape\n- Evidence landscape characterization\n\n### Framework breakdown table\n\n| Framework Component | How It Maps to This Topic | Proposed Sub-area to Explore |\n|---|---|---|\n| (Component 1) | ... | Sub-area 1 |\n| (Component 2) | ... | Sub-area 2 |\n| (Component 3) | ... | Sub-area 3 |\n| (Component 4) | ... | Sub-area 4 |\n| Cross-cutting theme | ... | Sub-area 5 |\n\n### Depth re-confirmation (forcing choice)\n\nSurface the **practical constraint**: search lane in use (free \u002F free+Consensus) + approximate ceiling at ~20 results per query per source.\n\n- Quick scan (5 searches × ~20 results = ~100 papers max per source)\n- Standard review (10 searches × ~20 = ~200 papers per source)\n- Deep dive (20 searches × ~20 = ~400 papers per source)\n\n### Sub-area forcing options\n\n- \"Looks good — proceed with these sub-areas\"\n- \"Adjust: add sub-area on [X]\"\n- \"Adjust: remove and replace [Y] with [Z]\"\n- \"Restart with different framework\"\n\n### Why I'm asking (the rationale)\n\n> A wrong framework or sub-area set wastes the search budget. This is the **last cheap moment** to correct course.\n\n**Wait for user response before Phase 3.** Refuse to start Phase 3 without explicit user choice.\n\n## Phase 3: Targeted Searches\n\nSequential (1 query\u002Fsec), budget per depth tier. Every search runs on the free lane (`free_search.py` or the URL templates); **if Consensus is available, additionally** run the same query there and merge. See [`references\u002Fsearch_budget_allocation.md`](references\u002Fsearch_budget_allocation.md) for full canon.\n\n### Quick scan (5 searches)\n- 5 sub-area searches (one per sub-area)\n- Skip era-gated + review-specific\n\n### Standard review (10 searches)\n- 5 sub-area searches\n- 2 review article searches (top 2 sub-areas): `\"systematic review [topic]\"` \u002F `\"meta-analysis [topic]\"` (OpenAlex: add `&filter=type:review`)\n- 2 era-gated searches (most important sub-area): historical (PubMed `&maxdate=2015` \u002F OpenAlex `to_publication_date:2015-12-31`) + recent (PubMed `&mindate=2021` \u002F OpenAlex `from_publication_date:2021-01-01`)\n- 1 follow-up on highest-cited paper using its key terms + a from-date after its publication year\n\n### Deep dive (20 searches)\n- 5 sub-area searches\n- 5 review article searches (one per sub-area)\n- 4 era-gated searches (top 2 sub-areas, old + new each)\n- 3 follow-ups on top 3 highest-cited papers\n- 3 spare for emerging threads (surprising findings to chase)\n\nThroughout: 1 q\u002Fsec rate limit. Sequential. Confirm response before next call. Record each via `citation_tracker.py`.\n\n## Cross-Search Intelligence\n\nThree trackers across ALL search results — run `scripts\u002Fcross_search_aggregator.py --session NAME` after Phase 3 completes:\n\n1. **Repeat-hit papers** — same paper appearing in 3+ sub-area searches = likely foundational\n2. **Recurring authors** — same author in multiple searches = dominant research group; top 3-5 most frequent matter\n3. **Citation-per-year heuristic** — a 2023 paper with 150 citations >> 2008 paper with 150 citations. Use OpenAlex `cited_by_count` for seminal-work identification.\n\nThese feed the \"Start Here\" + \"Key Research Groups\" + \"Bibliography\" DOCX sections.\n\n## Phase 4: DOCX Research Guide\n\nGenerate via Node.js + `docx` library. 8 sections (see [`references\u002Fdocx_8_sections.md`](references\u002Fdocx_8_sections.md) for full spec):\n\n1. **Topic Overview** — single tight paragraph (4-6 sentences)\n2. **Start Here — Priority Reading Order** — 5-7 papers ordered: best recent review → foundational → 2-3 frontier → gap\u002Fcontroversy. Each: hyperlinked title + authors\u002Fyear + 1-sentence contribution + 1-sentence \"what to look for\"\n3. **How the Field Got Here** — chronological narrative (1-2 paragraphs) + timeline table (5-8 milestones: Year \u002F Milestone \u002F Significance) + terminology evolution note\n4. **Sub-area Guides** (one per sub-area, 4 parts each)\n   - 4a. What the Research Shows (2-3 sentence synthesis with inline citations)\n   - 4b. Key Papers (3-5 hyperlinked papers with citation count, year, 1-sentence importance)\n   - 4c. Key Search Terms (6-10 keywords, synonyms, MeSH, historical terms)\n   - 4d. Boolean Search Strings (2-3 ready-to-paste strings)\n5. **Key Research Groups** — top 3-5 authors\u002Fgroups with affiliations, sub-area coverage, representative paper link (from cross-search aggregator)\n6. **Open Questions & Gaps** — three categories: methodological \u002F population-context \u002F conceptual-theoretical. Each gap explains *why it matters*.\n7. **Bibliography** — alphabetical by first author. Every entry has a clickable link: PubMed URL or DOI (free lane) \u002F \"View on Consensus\" (Consensus-sourced). Every inline citation matches a bibliography entry.\n8. **Audit Log** — search summary table (#, query, filters, papers returned, status), counts block, coverage notes including which search lane was used (free \u002F free+Consensus)\n\n### DOCX Technical Requirements\n\nDocument the key `docx` library patterns:\n\n- Page: US Letter, 1-inch margins\n- Lists: `LevelFormat.BULLET` (never unicode bullets)\n- Hyperlinks: `ExternalHyperlink` with `style: \"Hyperlink\"`, full URL (never truncated)\n- Tables: dual widths (`columnWidths` + cell `width`), `ShadingType.CLEAR`\n- Validation step after save (zip-integrity check: `python3 -c \"import zipfile,sys; zipfile.ZipFile(sys.argv[1]).testzip()\" output.docx` — no output = intact — then confirm the required sections are present)\n\nReference the **docx skill** for setup patterns and best practices.\n\n## Output\n\n```\nresearch_guide_\u003Ctopic-slug>_\u003CYYYY-MM-DD>.docx\n```\n\nPlus:\n- Chat summary block: \"Saved: \u003Cpath>. Audit: N searches × M unique papers \u002F K cited. Search lane: \u003Cfree | free+Consensus>.\"\n- Audit log printed inline if user asks for it\n\n## Tooling\n\n| Script | Role |\n|---|---|\n| `scripts\u002Ffree_search.py` | Free keyless search lane — PubMed E-utilities + OpenAlex via stdlib urllib (`--query`, `--source pubmed|openalex|both`, `--max`, `--json`, `--mailto`; exits 2 with a clear message when offline) |\n| `scripts\u002Fcitation_tracker.py` | JSON-backed three-count audit at `~\u002F.litreview_sessions\u002F\u003Csession>.json` |\n| `scripts\u002Fframework_recommender.py` | Heuristic PICO\u002FSPIDER\u002FDecomposition suggestion from research question |\n| `scripts\u002Fcross_search_aggregator.py` | Repeat-hits + recurring-authors + citation-per-year ranking after Phase 3 |\n\n## References\n\n- [`references\u002Fframework_selection.md`](references\u002Fframework_selection.md) — PICO \u002F SPIDER \u002F Decomposition canon (7+ sources)\n- [`references\u002Fsearch_budget_allocation.md`](references\u002Fsearch_budget_allocation.md) — depth tiers + cross-search intelligence + sequential execution rationale (7+ sources)\n- [`references\u002Fdocx_8_sections.md`](references\u002Fdocx_8_sections.md) — research guide DOCX spec + technical requirements (7+ sources)\n\n## Anti-Patterns To Reject\n\n- Parallelizing search calls (any lane)\n- Skipping the interactive checkpoint (running all searches without user confirmation)\n- Padding thin results with training knowledge\n- Defaulting to non-PICO framework without justification\n- Citing papers in chat that didn't come from this session's searches\n- Attempting Consensus plan-tier detection (deleted — the only runtime check is \"are the Consensus MCP tools available in this session?\")\n- Treating Consensus as required (it's an optional enhancement; the free lane is the default)\n- Skipping era-gated searches in standard\u002Fdeep budgets\n- Skipping cross-search intelligence (repeat-hits, recurring authors)\n- Truncating source URLs in hyperlinks\n\n---\n\n**Version:** 1.1.0\n**Source spec:** `megaprompts\u002F09-litreview-megaprompt.md` (maintainer-local draft spec — gitignored, not present in the public repository)\n**Build pattern:** Path B (direct conversion). Sibling of `pulse` (research-pack shape). v1.1.0: free keyless APIs (PubMed + OpenAlex) became the default search lane; Consensus demoted to optional enhancement; plan-tier detection deleted per the 2026-06 newgen audit + ClawHub rule #3 (no paid-service dependencies).\n",{"name":5,"license":15,"metadata":54,"description":7},{"version":55,"source_spec":56,"build_pattern":57,"research_pack_convention":58},"1.1.0","megaprompts\u002F09-litreview-megaprompt.md","Path B (direct conversion)","Agent Integrity Rules verbatim per PR #657 audit; sibling of pulse","main","0da4485785ee599388900be50db1635489d53056",".agents\u002Fskills\u002Flitreview","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6","https:\u002F\u002Fdisha6.6.thenitishkr.in\u002F","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh","www.thenitishkr","thenitishkr | AI • Cyber-security • Research\r\n","2026-11-08 03:33:25",{"source":8,"skillName":5,"commands":69,"prompt":79,"zipAvailable":80},[70,73,76],{"tool":71,"command":72},"npx","npx skills add Tashima-Tarsh\u002FDisha6.6 --skill litreview",{"tool":74,"command":75},"bunx","bunx skills add Tashima-Tarsh\u002FDisha6.6 --skill litreview",{"tool":77,"command":78},"pnpm","pnpm dlx skills add Tashima-Tarsh\u002FDisha6.6 --skill litreview","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Flitreview\u002FSKILL.md",true,[],[83,111,135,160,183,207],{"skillNo":84,"name":85,"nameZh":6,"description":86,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":87,"categories":88,"keywords":92,"publishedAt":49},"01M6QRD07D4TGNDK6VJ4EHF8BV","youtube-full","Use when the user needs YouTube transcripts, video search, channel browsing, playlist extraction, or content monitoring. Trigger phrases: 'get the transcript for', 'search YouTube for', 'what are the latest videos on', 'list this playlist', 'monitor this channel', or any request involving a YouTube URL, video ID, or @handle. Do NOT use for downloading video or audio files, YouTube engagement data (likes, comments), or private\u002Fage-restricted videos.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fyoutube-full\u002FSKILL.md",[89],{"categoryNo":90,"categoryName":91,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000016","图像与多媒体",[93,94,95,96,46,97,98,99,100,41,101,102,103,104,105,106,107,108,109,110],"youtube full","youtube","full","transcript","monitoring","data","Video","Audio","files","do","get","age","trigger","URL","for","browsing","request","youtube transcripts",{"skillNo":112,"name":113,"nameZh":6,"description":114,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":115,"categories":116,"keywords":117,"publishedAt":49},"01M6QRCJ3112VYPA97YG7KGMC2","x-twitter-growth","X\u002FTwitter growth engine for building audience, crafting viral content, and analyzing engagement. Use when the user wants to grow on X\u002FTwitter, write tweets or threads, analyze their X profile, research competitors on X, plan a posting strategy, or optimize engagement. Complements social-content (generic multi-platform) with X-specific depth: algorithm mechanics, thread engineering, reply strategy, profile optimization, and competitive intelligence via web search.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fx-twitter-growth\u002FSKILL.md",[],[118,119,120,121,46,122,123,44,124,125,126,127,128,129,130,131,132,133,134,107],"twitter","x twitter growth","growth","research","content","engineering","web search","competitors","analyze","engine","write","strategy","use","platform","grow","posting","competitive",{"skillNo":136,"name":137,"nameZh":6,"description":138,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":139,"categories":140,"keywords":142,"publishedAt":49},"01M6QRBN3EHQNJY42RERP7A149","write-a-skill","Create new agent skills with proper structure, progressive disclosure, and bundled resources. Use when user wants to create, write, build, or author a new skill.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fwrite-a-skill\u002FSKILL.md",[141],{"categoryNo":27,"categoryName":28,"taxonomyType":21,"taxonomyTypeDesc":22},[143,128,144,145,146,147,148,149,130,150,151,59,152,153,154,155,156,157,158,159],"write a skill","github","create","https","productivity","matt pocock","direct","example","build","new skill","structure","new agent","resources","with","tree","progressive disclosure","author",{"skillNo":161,"name":162,"nameZh":6,"description":163,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":164,"categories":165,"keywords":166,"publishedAt":49},"01M6QRB6G8P33PNGAF9Y69EJ9T","wrap-up","Close out a launched Claude Managed Agent — recap every primitive the founder now owns, regenerate the single-file overview page, and suggest the next 1-2 upgrades. Use when the user says \"wrap up\", \"close this out\", \"what do I own now\", \"give me the summary\", \"recap the agent\", or when the orchestrator routes phase=wrap-up. primitives_inventory.py tables everything owned (agent, environment, session, memory, outcome, deployment); overview_page.py regenerates a self-contained .\u002Fmy-agent\u002Fagent-overview.html; upgrade_suggester.py ranks the next moves from recorded deferrals plus standing hardening steps. Companion to run-without-you; the last stop before phase=done.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fwrap-up\u002FSKILL.md",[],[167,168,169,170,171,172,173,174,175,130,176,102,177,178,179,180,33,181,182,31],"wrap up","up","wrap","gemini","deployment","run","file","hardening","closeout","stop","upgrade","founder","close","companion","tables","directions",{"skillNo":184,"name":185,"nameZh":6,"description":186,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":187,"categories":188,"keywords":190,"publishedAt":49},"01M6QRA5AYC1RBFZ39GTWGS3N8","workflow-builder","Design and write deterministic multi-agent workflow scripts (.js files in .claude\u002Fworkflows\u002F) for Claude Code's Workflow tool. Use when a user wants to build, create, author, scaffold, or run a custom Claude Code workflow, orchestrate sub-agents (fan-out, pipeline, loop, judge-panel), or automate a repeatable multi-step task across fresh-context agents.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fworkflow-builder\u002FSKILL.md",[189],{"categoryNo":27,"categoryName":28,"taxonomyType":21,"taxonomyTypeDesc":22},[191,192,193,194,195,145,172,196,197,198,199,200,128,201,101,202,203,204,205,206],"workflow","workflow builder","builder","design","workflows","code","orchestrate","pipeline","loop","multi-agent","context","across","fresh","scripts","judge","claude code workflow",{"skillNo":208,"name":209,"nameZh":6,"description":210,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":211,"categories":212,"keywords":213,"publishedAt":49},"01M6QR7QMCZESKQA0ERW1E657M","weekly-review","Use when someone wants to run a weekly review, close open loops, audit stalled projects and commitments, get their system back to trusted, restart a lapsed review habit, or says \"\u002Fcs:weekly-review\". Walks David Allen's three-phase loop — GET CLEAR, GET CURRENT, GET CREATIVE — with deterministic scripts that inventory open loops, gate the checklist with named gaps, and score commitment health 0-100.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fweekly-review\u002FSKILL.md",[],[214,215,216,217,218,219,220,221,222,199,223,224,225,226,227,228,103,229,230,31],"review","weekly review","weekly","deterministic scripts","reflect","feeds","inventory","open","team","projects","project-management","scanner","conversation","back","project","sprint","cadence"]