[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6QFGATMR5TM321S1HTHJD8E":3,"skill-same:01M6QFGATMR5TM321S1HTHJD8E":176},{"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":23,"publishedAt":44,"linkOnly":45,"markdownContent":46,"markdownBody":47,"frontMatter":48,"refName":55,"commitSha":56,"skillDirectory":57,"forksCount":14,"repositoryHtmlUrl":58,"repositoryHomepageUrl":59,"ownerHtmlUrl":60,"ownerBlogUrl":61,"ownerDescription":62,"updatedAt":63,"install":64,"resources":77},"01M6QFGATMR5TM321S1HTHJD8E","peer-review",null,"Prepare evidence-bounded, constructive peer-review drafts and structured manuscript assessments. Use for authorized review of scientific manuscripts, protocols, preprints, or research proposals; reporting-guideline selection; claim–evidence checks; methods, statistics, reproducibility, ethics, figure\u002Ftable, and citation critique; or revision-response planning.","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\u002Fkal-peer-review\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000021","资料检索与研究","SCENARIO","使用场景",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43],"review","peer review","peer","proposals","for","manuscript","scientific","local","methods","selection","publishing","use","writing","ethics","table","critique","library","guidelines","python","image","2026-11-10 10:12:30",false,"---\nname: peer-review\ndescription: Prepare evidence-bounded, constructive peer-review drafts and structured manuscript assessments. Use for authorized review of scientific manuscripts, protocols, preprints, or research proposals; reporting-guideline selection; claim–evidence checks; methods, statistics, reproducibility, ethics, figure\u002Ftable, and citation critique; or revision-response planning.\nlicense: MIT\ncompatibility: Python 3.11+ standard library. Bundled CLIs are deterministic and local-only; they accept bounded JSON, CSV, or Markdown and make no network, model, image, or external-service calls.\nmetadata:\n  version: '2.2'\n  category: research-writing\n  maintainer: Kalaris Labs\n  tags: peer review, manuscript review, reporting guidelines, academic publishing\n---\n\n# Peer Review\n\nSupport an accountable human reviewer with a rigorous, fair, actionable assessment. Treat every unpublished submission and review as confidential.\n\n## Mandatory safety boundary\n\nBefore reading or analyzing unpublished content:\n\n1. Confirm the user is authorized by the publisher, editor, author, or other material owner.\n2. Check the target venue’s review, confidentiality, co-review, retention, and AI\u002Ftool policies.\n3. Record conflicts, competence limits, requested scope, and specialist-review needs.\n4. Default to local-only processing.\n\nIf authorization is unclear, do not inspect or quote the manuscript. Ask for confirmation or use only the bundled local CLIs, whose reports do not echo manuscript text.\n\nNever:\n\n- Send unpublished manuscript, supplement, review, or editorial text to an external service without specific publisher\u002Fauthor authorization and venue permission\n- Upload confidential content to a public model, search engine, citation service, grammar tool, plagiarism checker, or image service\n- Reuse content for training, benchmarking, product improvement, or unrelated research\n- Read broad environment state, `.env` files, API keys, or credentials\n- Call a network, LLM, or image API from bundled tools\n- Invoke another skill or a PDF\u002Fimage pipeline automatically\n- Impersonate an assigned reviewer, editor, journal, funder, or author\n- Fabricate manuscript details, review findings, citations, analyses, experiments, reproduction, or an editorial outcome\n- Announce a decision that belongs to an editor or panel\n\nDelete local copies and derivatives when policy requires; otherwise retain only what the controlling policy authorizes. Record deletion or retention without copying confidential content into the record.\n\nRead `references\u002Fethical_review_practice.md` before handling confidential material.\n\n## Human accountability\n\nLabel generated text as a working draft. The accountable human must:\n\n- Read the complete authorized submission and relevant supplements\n- Verify every factual statement, calculation, citation, and manuscript location\n- Resolve conflicts and disclose assistance as required\n- Rewrite comments in their own expert judgment\n- Submit through the authorized channel\n\nAutomated coverage, consistency, or lint results are not peer review and do not establish manuscript merit.\n\n## Intake gate\n\nCopy and complete `assets\u002Freview_intake_template.json`, then run:\n\n```bash\npython3 scripts\u002Fvalidate_review_intake.py completed-intake.json\n```\n\nProceed only when status is `READY_FOR_LOCAL_REVIEW`.\n\nThe validator blocks:\n\n- Undocumented authorization\n- Missing human accountability\n- Unassessed or unresolved conflicts\n- Unknown review model or unchecked venue policy\n- Unauthorized AI assistance\n- External service use\n- Data reuse\n- Missing deletion\u002Fretention planning\n\nIt validates declarations, not their truth.\n\n## Review workflow\n\n### 1. Establish scope and available evidence\n\nRecord:\n\n- Submission type and stage\n- Review question and requested focus\n- Target venue and review model\n- Materials actually available: manuscript, supplements, protocol, registration, analysis plan, data\u002Fcode statement, prior decision, or response letter\n- Competence areas and limits\n- Missing material that prevents assessment\n\nDo not infer absent content. Use “not reported” or “not available for review.”\n\n### 2. Orient without deciding\n\nCreate a short neutral map:\n\n- Research question\n- Population or system\n- Design and unit\n- Intervention, exposure, test, or model\n- Comparator\u002Freference\n- Outcomes and timing\n- Principal claims\n\nDo not write an acceptance\u002Frejection recommendation. Identify what evidence would be needed to evaluate each claim.\n\n### 3. Select reporting guidance\n\nCopy `assets\u002Fstudy_profile_template.json` and run:\n\n```bash\npython3 scripts\u002Fselect_reporting_guidelines.py local-profile.json\n```\n\nFor checklist coverage:\n\n```bash\npython3 scripts\u002Fselect_reporting_guidelines.py \\\n  local-profile.json \\\n  --coverage local-coverage.csv\n```\n\nUse the current base guideline, explanation\u002Felaboration, applicable extensions, and target venue policy. See `references\u002Freporting_standards.md`.\n\n**Critical distinction:** reporting completeness is not design quality, risk of bias, validity, or merit. Never convert missing items into an automatic score or publication judgment.\n\n### 4. Map claims to evidence\n\nPrioritize central, causal, mechanistic, safety, diagnostic, prediction, and generalization claims.\n\nFor each claim, record:\n\n- Location and claim ID\n- Supporting result, figure, table, analysis, or citation IDs\n- Direction, magnitude, population, outcome, timepoint, and uncertainty alignment\n- Limitation or alternative explanation\n- Bounded requested action\n\nRun:\n\n```bash\npython3 scripts\u002Fvalidate_claim_evidence.py local-claim-matrix.csv\n```\n\nStart from `assets\u002Fclaim_evidence_matrix_template.csv`. The report emits IDs and counts, not claim text.\n\n### 5. Review methods and statistics\n\nAssess in this order:\n\n1. Question and target quantity\n2. Design and unit of inference\n3. Sampling, allocation, controls, masking, and timing\n4. Sample-size or precision rationale\n5. Inclusion, exclusion, attrition, and missingness\n6. Analysis–design alignment and assumptions\n7. Multiplicity and prespecification\n8. Effect estimates, uncertainty, denominators, and harms\n9. Interpretation, causality, and generalizability\n\nUse `references\u002Fcommon_issues.md` and `references\u002Fstatistical_reproducibility.md`.\n\nFor a structured local audit:\n\n```bash\npython3 scripts\u002Faudit_statistics_reproducibility.py \\\n  local-statistics-reproducibility.json\n```\n\nStart from `assets\u002Fstatistical_reproducibility_template.json`. Request specialist review when a central method exceeds competence; do not hide uncertainty behind a generic critique.\n\n### 6. Review reproducibility and transparency\n\nCheck, as applicable:\n\n- Protocol, registration, amendments, and analysis-plan consistency\n- Data provenance, exclusions, transformations, and accession IDs\n- Software, package, model, and parameter versions\n- Code, environment, seeds, run instructions, and tests\n- Data, code, materials, and model availability or justified restrictions\n- Domain metadata standards\n\nDo not claim reproduction unless authorized inputs were actually run with documented commands, environment, and outputs.\n\n### 7. Review ethics and integrity\n\nCheck applicable approvals, consent, welfare, privacy, community governance, funding, sponsor role, conflicts, authorship\u002Fcontribution, registration, biosafety, and dual-use concerns.\n\nDescribe observable evidence and uncertainty. Do not accuse authors or investigate them. Route credible concerns through the confidential editor channel under venue policy.\n\n### 8. Review figures, tables, and citations\n\nFor figures and tables, assess:\n\n- Consistency with text and supplements\n- Denominators, units, axes, scales, uncertainty, and legends\n- Accessible encoding and sufficient context\n- Image acquisition\u002Fprocessing disclosure and source-data policy\n\nThis skill has no image-generation or PDF-conversion workflow. Use only user-authorized local artifacts and tools.\n\nFor Pandoc-style citations such as `[@ref-id]`:\n\n```bash\npython3 scripts\u002Faudit_citations.py local-manuscript.md local-references.csv\n```\n\nStart from `assets\u002Fcitation_references_template.csv`. This checks key consistency and identifier format only; it does not verify that a source exists or supports a claim.\n\n### 9. Draft actionable comments\n\nGenerate a private scaffold only after intake passes:\n\n```bash\npython3 scripts\u002Fgenerate_review_scaffold.py \\\n  completed-intake.json \\\n  -o private-review.md\n```\n\nEvery major\u002Fminor comment should include:\n\n- **Location**\n- **Observation**\n- **Evidence or criterion**\n- **Why it matters**\n- **Requested action**\n\nPrioritize:\n\n- Claim–evidence alignment\n- Methods and statistical validity\n- Reproducibility and transparency\n- Ethics and participant\u002Fanimal protection\n- Reporting needed for appraisal\n- Figures, tables, limitations, and citations\n\nRequests for new work must be necessary to support a central claim and proportionate to scope. Offer narrowing, clarification, sensitivity analysis, correction, or limitation language when that is sufficient.\n\n### 10. Keep channels separate\n\n**Comments to authors** contain the scientific review, strengths, major\u002Fminor comments, and limitations.\n\n**Confidential comments to editor** contain only policy-appropriate conflicts, competence limits, assistance disclosure, specialist requests, or substantiated integrity\u002Fprocess concerns that require a separate route.\n\nDo not place ordinary criticism only in confidential notes. Do not reveal reviewer identity under an anonymized process.\n\n### 11. Lint and finalize\n\n```bash\npython3 scripts\u002Flint_review.py private-review.md\n```\n\nThe linter checks channel separation, unresolved placeholders, a narrow abusive-language lexicon, role\u002Fdecision phrases, and required actionability fields. It emits line numbers and rule IDs, not review text. Human tone and scientific review remain mandatory.\n\nBefore handoff:\n\n- Verify all locations and evidence.\n- Remove unsupported or speculative criticism.\n- Confirm professional, non-abusive language.\n- State review limits and specialist needs.\n- Disclose permitted assistance.\n- Remove all placeholders.\n- Ensure no invented citation, experiment, reanalysis, or outcome.\n- Follow the documented deletion\u002Fretention rule.\n\n## Local tool index\n\n- `scripts\u002Fvalidate_review_intake.py` — scope, authorization, conflicts, policy, handling\n- `scripts\u002Fselect_reporting_guidelines.py` — dated selector and non-scoring coverage audit\n- `scripts\u002Fvalidate_claim_evidence.py` — claim\u002Fevidence alignment matrix\n- `scripts\u002Faudit_statistics_reproducibility.py` — methods\u002Fstatistics\u002Freproducibility checklist\n- `scripts\u002Faudit_citations.py` — local citation\u002Freference consistency\n- `scripts\u002Fgenerate_review_scaffold.py` — separated private Markdown scaffold\n- `scripts\u002Flint_review.py` — tone, channel, and actionability lint\n\nFull schemas and exit codes: `references\u002Ftool_reference.md`.\n\n## References and assets\n\n- `references\u002Fethical_review_practice.md` — COPE\u002FICMJE duties, confidentiality, AI, channels\n- `references\u002Freporting_standards.md` — current major guidelines and verified domain standards\n- `references\u002Fstatistical_reproducibility.md` — methods, statistics, and reproducibility review\n- `references\u002Fcommon_issues.md` — contextual issue patterns and constructive responses\n- `references\u002Fsecurity_validation.md` — baseline remediation and local scan results\n- `assets\u002Fsource_ledger.csv` — authoritative sources verified 2026-07-23\n- `assets\u002Freporting_guidelines.json` — local selector catalog\n- `assets\u002Freview_scaffold_template.md` — private structured draft\n\nThe source ledger is dated. Recheck live primary sources and the target venue policy for a later review, without exposing confidential manuscript text in search queries.\n\n## Agent operating procedure\n\n1. **Check the environment.** Collect the user's draft, data, figures and target venue before writing anything.\n2. **Pin down the inputs.** Confirm formats, identifiers and parameters from the data or the user. Ask rather than guess any value that changes the result.\n3. **Run a small version first.** Draft one section or paragraph and confirm tone, structure and terminology with the user.\n4. **Execute the full task** using the instructions and references above.\n5. **Validate the result.** Every claim traces to the user's data or a verified source; numbers match tables and figures; citations are real (`citation-verification`).\n6. **Report.** State what was run (versions, commands, parameters), what was checked, and what is still uncertain.\n\n| If this happens | Do this |\n|---|---|\n| A claim has no supporting data or citation | Flag it in the text (e.g. `[citation needed]`) instead of inventing support. |\n| A function, flag or endpoint in these instructions is missing in the installed version | Check the installed version's own documentation (`help()`, `--help`, official docs), adapt, and tell the user. Never invent an API. |\n| A required input, identifier or parameter is ambiguous | Ask the user, or state the assumption explicitly before running. |\n\n**Integrity rules**\n\n- Never fabricate results, parameters, identifiers, citations or statistics. If something cannot be run or verified, say so plainly.\n- The user is the author: never add results, references or claims they did not provide or approve.\n- Treat version-specific details here as possibly outdated: confirm them against the official documentation for the installed version.\n- Ask before actions that cost money, consume shared GPUs or cloud quota, touch personal or patient data, or cannot be undone.\n\n## Related skills\n\n- `scientific-writing`: Draft, revise, and audit scientific manuscripts or reports with explicit evidence provenance, reporting-guideline coverage, authorship acco…\n- `reproducibility-statement`: Prepare the reproducibility, transparency and open-science parts of a paper, including data and code availability statements, reproducibili…\n- `cell-press`: Prepare manuscripts for Cell Press journals (Cell, Molecular Cell, Neuron, Immunity, Cell Reports, Cell Systems, iScience, Cell Metabolism,…\n","# Peer Review\n\nSupport an accountable human reviewer with a rigorous, fair, actionable assessment. Treat every unpublished submission and review as confidential.\n\n## Mandatory safety boundary\n\nBefore reading or analyzing unpublished content:\n\n1. Confirm the user is authorized by the publisher, editor, author, or other material owner.\n2. Check the target venue’s review, confidentiality, co-review, retention, and AI\u002Ftool policies.\n3. Record conflicts, competence limits, requested scope, and specialist-review needs.\n4. Default to local-only processing.\n\nIf authorization is unclear, do not inspect or quote the manuscript. Ask for confirmation or use only the bundled local CLIs, whose reports do not echo manuscript text.\n\nNever:\n\n- Send unpublished manuscript, supplement, review, or editorial text to an external service without specific publisher\u002Fauthor authorization and venue permission\n- Upload confidential content to a public model, search engine, citation service, grammar tool, plagiarism checker, or image service\n- Reuse content for training, benchmarking, product improvement, or unrelated research\n- Read broad environment state, `.env` files, API keys, or credentials\n- Call a network, LLM, or image API from bundled tools\n- Invoke another skill or a PDF\u002Fimage pipeline automatically\n- Impersonate an assigned reviewer, editor, journal, funder, or author\n- Fabricate manuscript details, review findings, citations, analyses, experiments, reproduction, or an editorial outcome\n- Announce a decision that belongs to an editor or panel\n\nDelete local copies and derivatives when policy requires; otherwise retain only what the controlling policy authorizes. Record deletion or retention without copying confidential content into the record.\n\nRead `references\u002Fethical_review_practice.md` before handling confidential material.\n\n## Human accountability\n\nLabel generated text as a working draft. The accountable human must:\n\n- Read the complete authorized submission and relevant supplements\n- Verify every factual statement, calculation, citation, and manuscript location\n- Resolve conflicts and disclose assistance as required\n- Rewrite comments in their own expert judgment\n- Submit through the authorized channel\n\nAutomated coverage, consistency, or lint results are not peer review and do not establish manuscript merit.\n\n## Intake gate\n\nCopy and complete `assets\u002Freview_intake_template.json`, then run:\n\n```bash\npython3 scripts\u002Fvalidate_review_intake.py completed-intake.json\n```\n\nProceed only when status is `READY_FOR_LOCAL_REVIEW`.\n\nThe validator blocks:\n\n- Undocumented authorization\n- Missing human accountability\n- Unassessed or unresolved conflicts\n- Unknown review model or unchecked venue policy\n- Unauthorized AI assistance\n- External service use\n- Data reuse\n- Missing deletion\u002Fretention planning\n\nIt validates declarations, not their truth.\n\n## Review workflow\n\n### 1. Establish scope and available evidence\n\nRecord:\n\n- Submission type and stage\n- Review question and requested focus\n- Target venue and review model\n- Materials actually available: manuscript, supplements, protocol, registration, analysis plan, data\u002Fcode statement, prior decision, or response letter\n- Competence areas and limits\n- Missing material that prevents assessment\n\nDo not infer absent content. Use “not reported” or “not available for review.”\n\n### 2. Orient without deciding\n\nCreate a short neutral map:\n\n- Research question\n- Population or system\n- Design and unit\n- Intervention, exposure, test, or model\n- Comparator\u002Freference\n- Outcomes and timing\n- Principal claims\n\nDo not write an acceptance\u002Frejection recommendation. Identify what evidence would be needed to evaluate each claim.\n\n### 3. Select reporting guidance\n\nCopy `assets\u002Fstudy_profile_template.json` and run:\n\n```bash\npython3 scripts\u002Fselect_reporting_guidelines.py local-profile.json\n```\n\nFor checklist coverage:\n\n```bash\npython3 scripts\u002Fselect_reporting_guidelines.py \\\n  local-profile.json \\\n  --coverage local-coverage.csv\n```\n\nUse the current base guideline, explanation\u002Felaboration, applicable extensions, and target venue policy. See `references\u002Freporting_standards.md`.\n\n**Critical distinction:** reporting completeness is not design quality, risk of bias, validity, or merit. Never convert missing items into an automatic score or publication judgment.\n\n### 4. Map claims to evidence\n\nPrioritize central, causal, mechanistic, safety, diagnostic, prediction, and generalization claims.\n\nFor each claim, record:\n\n- Location and claim ID\n- Supporting result, figure, table, analysis, or citation IDs\n- Direction, magnitude, population, outcome, timepoint, and uncertainty alignment\n- Limitation or alternative explanation\n- Bounded requested action\n\nRun:\n\n```bash\npython3 scripts\u002Fvalidate_claim_evidence.py local-claim-matrix.csv\n```\n\nStart from `assets\u002Fclaim_evidence_matrix_template.csv`. The report emits IDs and counts, not claim text.\n\n### 5. Review methods and statistics\n\nAssess in this order:\n\n1. Question and target quantity\n2. Design and unit of inference\n3. Sampling, allocation, controls, masking, and timing\n4. Sample-size or precision rationale\n5. Inclusion, exclusion, attrition, and missingness\n6. Analysis–design alignment and assumptions\n7. Multiplicity and prespecification\n8. Effect estimates, uncertainty, denominators, and harms\n9. Interpretation, causality, and generalizability\n\nUse `references\u002Fcommon_issues.md` and `references\u002Fstatistical_reproducibility.md`.\n\nFor a structured local audit:\n\n```bash\npython3 scripts\u002Faudit_statistics_reproducibility.py \\\n  local-statistics-reproducibility.json\n```\n\nStart from `assets\u002Fstatistical_reproducibility_template.json`. Request specialist review when a central method exceeds competence; do not hide uncertainty behind a generic critique.\n\n### 6. Review reproducibility and transparency\n\nCheck, as applicable:\n\n- Protocol, registration, amendments, and analysis-plan consistency\n- Data provenance, exclusions, transformations, and accession IDs\n- Software, package, model, and parameter versions\n- Code, environment, seeds, run instructions, and tests\n- Data, code, materials, and model availability or justified restrictions\n- Domain metadata standards\n\nDo not claim reproduction unless authorized inputs were actually run with documented commands, environment, and outputs.\n\n### 7. Review ethics and integrity\n\nCheck applicable approvals, consent, welfare, privacy, community governance, funding, sponsor role, conflicts, authorship\u002Fcontribution, registration, biosafety, and dual-use concerns.\n\nDescribe observable evidence and uncertainty. Do not accuse authors or investigate them. Route credible concerns through the confidential editor channel under venue policy.\n\n### 8. Review figures, tables, and citations\n\nFor figures and tables, assess:\n\n- Consistency with text and supplements\n- Denominators, units, axes, scales, uncertainty, and legends\n- Accessible encoding and sufficient context\n- Image acquisition\u002Fprocessing disclosure and source-data policy\n\nThis skill has no image-generation or PDF-conversion workflow. Use only user-authorized local artifacts and tools.\n\nFor Pandoc-style citations such as `[@ref-id]`:\n\n```bash\npython3 scripts\u002Faudit_citations.py local-manuscript.md local-references.csv\n```\n\nStart from `assets\u002Fcitation_references_template.csv`. This checks key consistency and identifier format only; it does not verify that a source exists or supports a claim.\n\n### 9. Draft actionable comments\n\nGenerate a private scaffold only after intake passes:\n\n```bash\npython3 scripts\u002Fgenerate_review_scaffold.py \\\n  completed-intake.json \\\n  -o private-review.md\n```\n\nEvery major\u002Fminor comment should include:\n\n- **Location**\n- **Observation**\n- **Evidence or criterion**\n- **Why it matters**\n- **Requested action**\n\nPrioritize:\n\n- Claim–evidence alignment\n- Methods and statistical validity\n- Reproducibility and transparency\n- Ethics and participant\u002Fanimal protection\n- Reporting needed for appraisal\n- Figures, tables, limitations, and citations\n\nRequests for new work must be necessary to support a central claim and proportionate to scope. Offer narrowing, clarification, sensitivity analysis, correction, or limitation language when that is sufficient.\n\n### 10. Keep channels separate\n\n**Comments to authors** contain the scientific review, strengths, major\u002Fminor comments, and limitations.\n\n**Confidential comments to editor** contain only policy-appropriate conflicts, competence limits, assistance disclosure, specialist requests, or substantiated integrity\u002Fprocess concerns that require a separate route.\n\nDo not place ordinary criticism only in confidential notes. Do not reveal reviewer identity under an anonymized process.\n\n### 11. Lint and finalize\n\n```bash\npython3 scripts\u002Flint_review.py private-review.md\n```\n\nThe linter checks channel separation, unresolved placeholders, a narrow abusive-language lexicon, role\u002Fdecision phrases, and required actionability fields. It emits line numbers and rule IDs, not review text. Human tone and scientific review remain mandatory.\n\nBefore handoff:\n\n- Verify all locations and evidence.\n- Remove unsupported or speculative criticism.\n- Confirm professional, non-abusive language.\n- State review limits and specialist needs.\n- Disclose permitted assistance.\n- Remove all placeholders.\n- Ensure no invented citation, experiment, reanalysis, or outcome.\n- Follow the documented deletion\u002Fretention rule.\n\n## Local tool index\n\n- `scripts\u002Fvalidate_review_intake.py` — scope, authorization, conflicts, policy, handling\n- `scripts\u002Fselect_reporting_guidelines.py` — dated selector and non-scoring coverage audit\n- `scripts\u002Fvalidate_claim_evidence.py` — claim\u002Fevidence alignment matrix\n- `scripts\u002Faudit_statistics_reproducibility.py` — methods\u002Fstatistics\u002Freproducibility checklist\n- `scripts\u002Faudit_citations.py` — local citation\u002Freference consistency\n- `scripts\u002Fgenerate_review_scaffold.py` — separated private Markdown scaffold\n- `scripts\u002Flint_review.py` — tone, channel, and actionability lint\n\nFull schemas and exit codes: `references\u002Ftool_reference.md`.\n\n## References and assets\n\n- `references\u002Fethical_review_practice.md` — COPE\u002FICMJE duties, confidentiality, AI, channels\n- `references\u002Freporting_standards.md` — current major guidelines and verified domain standards\n- `references\u002Fstatistical_reproducibility.md` — methods, statistics, and reproducibility review\n- `references\u002Fcommon_issues.md` — contextual issue patterns and constructive responses\n- `references\u002Fsecurity_validation.md` — baseline remediation and local scan results\n- `assets\u002Fsource_ledger.csv` — authoritative sources verified 2026-07-23\n- `assets\u002Freporting_guidelines.json` — local selector catalog\n- `assets\u002Freview_scaffold_template.md` — private structured draft\n\nThe source ledger is dated. Recheck live primary sources and the target venue policy for a later review, without exposing confidential manuscript text in search queries.\n\n## Agent operating procedure\n\n1. **Check the environment.** Collect the user's draft, data, figures and target venue before writing anything.\n2. **Pin down the inputs.** Confirm formats, identifiers and parameters from the data or the user. Ask rather than guess any value that changes the result.\n3. **Run a small version first.** Draft one section or paragraph and confirm tone, structure and terminology with the user.\n4. **Execute the full task** using the instructions and references above.\n5. **Validate the result.** Every claim traces to the user's data or a verified source; numbers match tables and figures; citations are real (`citation-verification`).\n6. **Report.** State what was run (versions, commands, parameters), what was checked, and what is still uncertain.\n\n| If this happens | Do this |\n|---|---|\n| A claim has no supporting data or citation | Flag it in the text (e.g. `[citation needed]`) instead of inventing support. |\n| A function, flag or endpoint in these instructions is missing in the installed version | Check the installed version's own documentation (`help()`, `--help`, official docs), adapt, and tell the user. Never invent an API. |\n| A required input, identifier or parameter is ambiguous | Ask the user, or state the assumption explicitly before running. |\n\n**Integrity rules**\n\n- Never fabricate results, parameters, identifiers, citations or statistics. If something cannot be run or verified, say so plainly.\n- The user is the author: never add results, references or claims they did not provide or approve.\n- Treat version-specific details here as possibly outdated: confirm them against the official documentation for the installed version.\n- Ask before actions that cost money, consume shared GPUs or cloud quota, touch personal or patient data, or cannot be undone.\n\n## Related skills\n\n- `scientific-writing`: Draft, revise, and audit scientific manuscripts or reports with explicit evidence provenance, reporting-guideline coverage, authorship acco…\n- `reproducibility-statement`: Prepare the reproducibility, transparency and open-science parts of a paper, including data and code availability statements, reproducibili…\n- `cell-press`: Prepare manuscripts for Cell Press journals (Cell, Molecular Cell, Neuron, Immunity, Cell Reports, Cell Systems, iScience, Cell Metabolism,…\n",{"name":5,"license":15,"metadata":49,"description":7,"compatibility":54},{"tags":50,"version":51,"category":52,"maintainer":53},"peer review, manuscript review, reporting guidelines, academic publishing","2.2","research-writing","Kalaris Labs","Python 3.11+ standard library. Bundled CLIs are deterministic and local-only; they accept bounded JSON, CSV, or Markdown and make no network, model, image, or external-service calls.","main","0da4485785ee599388900be50db1635489d53056",".agents\u002Fskills\u002Fkal-peer-review","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":65,"prompt":75,"zipAvailable":76},[66,69,72],{"tool":67,"command":68},"npx","npx skills add Tashima-Tarsh\u002FDisha6.6 --skill peer-review",{"tool":70,"command":71},"bunx","bunx skills add Tashima-Tarsh\u002FDisha6.6 --skill peer-review",{"tool":73,"command":74},"pnpm","pnpm dlx skills add Tashima-Tarsh\u002FDisha6.6 --skill 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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",[183],{"categoryNo":184,"categoryName":185,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000016","图像与多媒体",[187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,28,203,204,205],"youtube full","youtube","full","transcript","search","monitoring","data","Video","Audio","list","files","do","get","age","trigger","URL","browsing","request","youtube transcripts",{"skillNo":207,"name":208,"nameZh":6,"description":209,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":210,"categories":211,"keywords":212,"publishedAt":44},"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",[],[213,214,215,216,191,217,218,219,220,221,222,223,224,225,35,226,227,228,229,28],"twitter","x twitter growth","growth","research","content","engineering","plan","web search","competitors","analyze","engine","write","strategy","platform","grow","posting","competitive",{"skillNo":231,"name":232,"nameZh":6,"description":233,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":234,"categories":235,"keywords":239,"publishedAt":44},"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",[236],{"categoryNo":237,"categoryName":238,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[240,224,241,242,243,244,245,246,35,247,248,55,249,250,251,252,253,254,255,256],"write a skill","github","create","https","productivity","matt pocock","direct","example","build","new skill","structure","new agent","resources","with","tree","progressive disclosure","author",{"skillNo":258,"name":259,"nameZh":6,"description":260,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":261,"categories":262,"keywords":263,"publishedAt":44},"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",[],[264,265,266,267,268,269,270,271,272,35,273,198,274,275,276,277,278,279,280,281],"wrap up","up","wrap","gemini","deployment","run","file","hardening","closeout","stop","upgrade","founder","close","companion","you","tables","directions","phase",{"skillNo":283,"name":284,"nameZh":6,"description":285,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":286,"categories":287,"keywords":289,"publishedAt":44},"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",[288],{"categoryNo":237,"categoryName":238,"taxonomyType":21,"taxonomyTypeDesc":22},[290,291,292,293,294,242,269,295,296,297,298,299,224,300,197,301,302,303,304,305],"workflow","workflow builder","builder","design","workflows","code","orchestrate","pipeline","loop","multi-agent","context","across","fresh","scripts","judge","claude code workflow",{"skillNo":307,"name":308,"nameZh":6,"description":309,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":310,"categories":311,"keywords":312,"publishedAt":44},"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",[],[24,313,314,315,316,317,318,319,320,298,321,322,323,324,325,326,199,327,328,281],"weekly review","weekly","deterministic scripts","reflect","feeds","inventory","open","team","projects","project-management","scanner","conversation","back","project","sprint","cadence"]