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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.","hashgraph-online\u002Fawesome-codex-plugins","hashgraph-online","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F160371962?v=4","01M6CNB6NRJVNNGN14TR01Q6D3","HOL (Hashgraph Online)","01M6JT1HSKKG9V68KQAQGBA7XP",1575,"MIT","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fpeer-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","ethics","drafts","revision","scientific","statistics","local","claim","selection","only","writing","model","markdown","critique","network","reporting","library","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 Treat every unpublished submission and review as confidential.\n\n## Mandatory safety boundary\n\nBefore reading or analyzing unpublished content:\n\n1.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","2b63f856ce19c2a797929cacabd9e0fc936812fa","plugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fpeer-review",491,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins","https:\u002F\u002Fhol.org\u002Fplugins\u002Fbest-codex-plugins","https:\u002F\u002Fgithub.com\u002Fhashgraph-online","https:\u002F\u002Fhol.org","Open-source standards and developer tools for interoperable AI agents: discovery, identity, communication, payments, privacy and security.","2026-11-14 14:06:13",{"source":8,"skillName":5,"commands":66,"prompt":76,"zipAvailable":77},[67,70,73],{"tool":68,"command":69},"npx","npx skills add hashgraph-online\u002Fawesome-codex-plugins --skill peer-review",{"tool":71,"command":72},"bunx","bunx skills add hashgraph-online\u002Fawesome-codex-plugins --skill 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an existing Playwright, Cypress, Selenium, Appium, Detox, Maestro, or TesterArmy suite to e2e one slice at a time, with the old suite running beside the new one until each ported test is proven. Keeps a MIGRATION.md progress file so any later session resumes where the last stopped. Optionally plants a defect to show both suites catch it. Use for \u002Fmigrate-to-e2e, \"migrate our tests to e2e\", \"port these Maestro flows\", \"move our TesterArmy tests\", or when a project has an e2e config and an older suite still running.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Ftester-army\u002Fe2e\u002Fskills\u002Fmigrate-to-e2e\u002FSKILL.md",[],[],"2026-11-11 15:11:14",{"skillNo":188,"name":189,"nameZh":6,"description":190,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":182,"licenseFileUrl":6,"sourceHtmlUrl":191,"categories":192,"keywords":193,"publishedAt":186},"01M746ZPTAX1Q0FBKAB1CYND7E","e2e","Writes, runs, and debugs end-to-end tests for web and mobile apps with e2e, the agentic test runner, which mixes agent.act goals with exact locator assertions on a Playwright browser or an iOS or Android device. Also drives a project's running app live over the e2e MCP server to look at a screen, check a UI change, take a screenshot or record a video, and try a locator; and runs bug bashes, parallel e2e explore runs with each bug proven by a repro test. Use when a project depends on e2e or has an e2e.config.ts, when asked for end-to-end, browser, mobile, or agentic UI tests, to check a change in the running app, to QA or bug bash an app or branch, or when an e2e run fails.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Ftester-army\u002Fe2e\u002Fskills\u002Fe2e\u002FSKILL.md",[],[],{"skillNo":195,"name":196,"nameZh":6,"description":197,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":182,"licenseFileUrl":6,"sourceHtmlUrl":198,"categories":199,"keywords":200,"publishedAt":186},"01M746ZJN784HVHMDP9MQGCSGG","create-verification-skill","Generate a project-local verify-\u003Capp> skill on top of e2e, so any coding agent can launch the app, check it is worth driving, exercise a feature the way a user does, keep evidence, and bug bash it. Writes the skill, a feature map with one explore charter per feature, and proves it once. Use for \u002Fcreate-verification-skill, \"make a verification skill for this repo\", or when a project has no scripted way to prove UI behavior.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Ftester-army\u002Fe2e\u002Fskills\u002Fcreate-verification-skill\u002FSKILL.md",[],[],{"skillNo":202,"name":203,"nameZh":6,"description":204,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":182,"licenseFileUrl":6,"sourceHtmlUrl":205,"categories":206,"keywords":207,"publishedAt":44},"01M716A6ETNB9B1SQ3GXZC75VR","deja-search","deja-vu memory — search the user's past AI coding sessions with the deja CLI. Holds what they already settled and never wrote into the repo (naming rules, banned or already-tried tools, required checks before merge, preferred values, who owns what). Use before your first edit in a task, before you call a change done or ready to merge, before you recommend a tool, library, design or reviewer, before re-debugging an error that may already be solved, when they say 'didn't we fix this before', 'what did we decide about X' or 'I already have that', and before telling them something on this machine does not exist.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Fvshulcz\u002Fdeja-vu\u002Fskills\u002Fdeja-search\u002FSKILL.md",[],[208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227],"search","deja search","never","error","ready","memory","re","decide","fix","design","debugging","when","you","done","reviewer","io","change","cli","merge","past",{"skillNo":229,"name":230,"nameZh":6,"description":231,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":182,"licenseFileUrl":6,"sourceHtmlUrl":232,"categories":233,"keywords":234,"publishedAt":44},"01M716A3J2W5SENSE9VGMXB375","deja-reflect","End-of-session reflection and memory curation with deja-vu. Review what was decided, what commands broke and what repaired them, and record durable project rules or decisions with deja remember so the next session starts with them.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Fvshulcz\u002Fdeja-vu\u002Fskills\u002Fdeja-reflect\u002FSKILL.md",[],[235,236,24,223,237,238,239,240,241,242,243,244,245,246,213,247,248,249,250,251],"deja reflect","reflect","with","record","github","next","https","node","Rules","decisions","brew","remember","project rules","project","session","end","reflection",{"skillNo":253,"name":254,"nameZh":6,"description":255,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":182,"licenseFileUrl":6,"sourceHtmlUrl":256,"categories":257,"keywords":267,"publishedAt":44},"01M715T3RR4BAYSR57HPPTJ0PF","revid","Make, edit, export and post AI videos with Revid (revid.ai) through the Revid MCP server. Use whenever the user wants a short-form or long-form video made (from an idea, a script, a URL, an article, a PDF, a tweet, a product page, their own clips, or a song), wants to find what is going viral on TikTok, YouTube, Instagram or X before making something, wants to edit or re-export an existing Revid project, generate a standalone AI image or clip, make a series episode, or publish and schedule a finished video to connected social accounts. Covers setup (hosted MCP with OAuth, or an API key), the 36 tools, credits, polling and the rules for publishing. Routes to the more specific revid-* skills when one fits.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Ftmakersas\u002Frevid-skills\u002Fskills\u002Frevid\u002FSKILL.md",[258,261,264],{"categoryNo":259,"categoryName":260,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000016","图像与多媒体",{"categoryNo":262,"categoryName":263,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":265,"categoryName":266,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口",[254,268,269,270,271,272,273,248,274,214,275,276,277,278,219,43,279,280,281,282],"URL","Video","short-form","standalone","PDF","viral","tiktok","short","social","MCP","song","mcp server","schedule","api","publish"]