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Use for manuscript sections, references, declarations, tables, figures, or submission preparation when scientific accuracy and traceability matter.","hashgraph-online\u002Fawesome-codex-plugins","hashgraph-online","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F160371962?v=4","01M6CNB6NRJVNNGN14TR01Q6D3","HOL (Hashgraph Online)","01M6JT1HSKKG9V68KQAQGBA7XP",1559,"MIT","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fscientific-writing\u002FSKILL.md",[18,23,26,29],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000018","写作与文案","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",{"categoryNo":30,"categoryName":31,"taxonomyType":32,"taxonomyTypeDesc":33},"01JBSEEDCAT000000000000036","内容创作者","PROFESSION","适用职业",[35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55],"scientific writing","writing","scientific","traceability","submission","api keys","accuracy","for","tables","manuscript","consistency","figures","controls","evidence provenance","free","revise","platform","local","offline","guidelines","python","2026-11-10 10:12:30",false,"---\nname: scientific-writing\ndescription: Draft, revise, and audit scientific manuscripts or reports with explicit evidence provenance, reporting-guideline coverage, authorship accountability, confidentiality controls, and local consistency checks. Use for manuscript sections, references, declarations, tables, figures, or submission preparation when scientific accuracy and traceability matter.\nlicense: MIT\ncompatibility: Requires Python 3.11+ only for optional dependency-free local CLIs; core guidance is platform-neutral. Bundled tools are offline and require no API keys.\nmetadata:\n  version: '2.1'\n  category: research-writing\n  maintainer: Kalaris Labs\n  tags: scientific writing, manuscripts, academic writing, research papers, reporting guidelines\n---\n\n# Scientific Writing\n\n## Purpose\n\nProduce clear scientific prose without inventing evidence or concealing uncertainty.\nKeep drafting, evidence verification, and submission approval as separate stages.\n\nThe accountable human authors control scientific decisions and final approval. AI is\nnot an author, and generated fluency is never evidence [SW-S01, SW-S03].\n\n## Non-negotiable safety rules\n\n### Confidentiality\n\nDo not send unpublished manuscripts, peer-review or editorial material, sensitive or\nrestricted data, PHI or other personal data, proprietary content, or source documents\nto an external service without:\n\n1. explicit authorization from a person or body empowered to grant it; and\n2. a documented review of journal, institutional, funder, consent, ethics, contractual,\n   legal, and data-use policy.\n\nWhen authorization or policy is unclear, keep processing local and use only the minimum\nmetadata needed. De-identification requires expert review; removing obvious names is\nnot sufficient. See `references\u002Fauthorship_ai_confidentiality.md`.\n\n### No fabrication\n\nNever invent or complete:\n\n- citations, references, DOI, PMID, PMCID, ISBN, URLs, or quotations;\n- results, data values, denominators, sample sizes, units, effect estimates,\n  uncertainty, statistical tests, or significance claims;\n- methods, materials, protocol details, software versions, analysis choices, or\n  deviations;\n- registrations, approvals, consent, ethics statements, participant details, or dates;\n- authors, author order, CRediT roles, acknowledgments, or permissions;\n- funding, sponsor roles, conflicts, data or code availability, or AI disclosures.\n\nUse an explicit missing, unverified, or not-applicable state. Do not substitute plausible\nboilerplate.\n\n### Evidence binding\n\nEvery factual or numeric manuscript claim must map to verified evidence IDs. A human\nverifier must open the source, confirm the proposition and locator, verify bibliographic\nmetadata, and record who verified it and when.\n\nSearch snippets, generated summaries, memory, and another work's bibliography may aid\ndiscovery but do not verify a claim. See `references\u002Fevidence_workflow.md`.\n\n### Scientific fidelity\n\n- Preserve uncertainty and alternative explanations.\n- Distinguish confirmatory, exploratory, descriptive, and post hoc work.\n- Keep methods and results consistent.\n- Reconcile units, denominators, sample sizes, populations, time points, and labels.\n- Report negative, null, adverse, unexpected, failed, and inconclusive findings when\n  they belong to the study record.\n- State concrete limitations and bound generalizability.\n- Do not convert association into causation or non-significance into equivalence.\n\n## Intake\n\nBefore drafting, obtain or mark unresolved:\n\n- document type, study design, stage, audience, and target venue;\n- current author instructions and policy access date;\n- protocol, registration, analysis plan, amendments, and reporting guideline;\n- manuscript or section scope;\n- verified source manifest and claim registry;\n- methods, results, tables, figures, and supplements;\n- authorship, CRediT, declarations, and approval records;\n- confidentiality classification and authorized processing boundary;\n- data, code, materials, and repository constraints.\n\nDo not ask for restricted source material if metadata or a local user-run audit is\nsufficient.\n\n## Workflow\n\n### 1. Establish the local workspace\n\nFor a new draft, optionally generate fail-closed Markdown, JSON, and CSV scaffolds:\n\n```bash\npython3 scripts\u002Fscaffold_manuscript.py \\\n  --output-dir .\u002Fdraft-workspace \\\n  --document-id local-draft \\\n  --study-design randomized_trial \\\n  --guideline consort-2025\n```\n\nThe generator never overwrites files. Its output is explicitly not submission-ready and\ncontains placeholders that the linter rejects.\n\n### 2. Select reporting guidance\n\nChoose by actual design and article type, then open the current official statement,\nchecklist, explanation document, extensions, and target-journal instructions.\n\n```bash\npython3 scripts\u002Fselect_reporting_guidelines.py select \\\n  --study-design randomized_trial\n```\n\nCurrent major routes researched on 2026-07-24 include CONSORT 2025, SPIRIT 2025,\nPRISMA 2020, STROBE, STARD and STARD-AI, TRIPOD+AI, CARE, ARRIVE 2.0, SQUIRE 2.0,\nand CHEERS 2022 [SW-S06–SW-S18].\n\nThe selector is non-scoring. It does not certify quality, compliance, completeness, or\nacceptance. See `references\u002Freporting_guidelines.md`.\n\n### 3. Build the evidence record\n\nAssign:\n\n- `E` IDs to sources in `source_manifest.json`;\n- `C` IDs to claims in `claims.csv`;\n- `N`, `M`, `O`, and `R` IDs to numeric facts, methods, outcomes, and results in\n  `consistency_manifest.json`.\n\nStore a hash of claim text in CSV rather than raw claim text. During drafting, append:\n\n```text\n[claim:C001] [evidence:E001,E002]\n```\n\nDo not mark a source verified until an accountable human has opened it and confirmed\nthe exact support.\n\n### 4. Create an evidence outline\n\nOutline only from recorded evidence:\n\n- objective or question;\n- section purpose;\n- claim IDs and evidence IDs;\n- methods and result IDs;\n- analysis intent and uncertainty;\n- unresolved conflicts or missing information;\n- applicable reporting topics.\n\nKeep unsupported content in an unresolved-issues list, not manuscript prose.\n\n### 5. Draft without adding facts\n\nTransform the verified outline into venue-appropriate prose. Preserve all IDs during\ndrafting.\n\n- Match title and abstract to the completed main text.\n- Describe methods as performed.\n- Present results in the declared order and analysis population.\n- Separate result from interpretation unless the venue combines them.\n- Compare with prior evidence only after verifying it.\n- Keep conclusions within the observed design, population, and uncertainty.\n\nUse IMRAD only when appropriate. Structured abstracts, lists, combined sections, and\nalternative structures depend on study design and venue. See\n`references\u002Fimrad_structure.md` and `references\u002Fwriting_principles.md`.\n\n### 6. Reconcile methods and results\n\nRecord repeated numeric facts and method-result mappings, then run:\n\n```bash\npython3 scripts\u002Fcheck_consistency.py consistency_manifest.json\n```\n\nResolve every mismatch manually. A changed value may be a legitimate analysis-set\ndifference, but that difference must be named rather than silently normalized.\n\n### 7. Verify citations and claims\n\n```bash\npython3 scripts\u002Fvalidate_manifest.py source_manifest.json \\\n  --kind source --require-verified\npython3 scripts\u002Faudit_claims.py manuscript.md claims.csv source_manifest.json\npython3 scripts\u002Fcheck_references.py source_manifest.json\n```\n\nThe reference checker validates syntax and duplicate identifiers without network\nresolution. A human must still compare every identifier and quotation with the opened\nsource. Follow NLM *Citing Medicine* or the current official style required by the\nvenue [SW-S20, SW-S21].\n\n### 8. Validate authorship and disclosure\n\nUse journal criteria for authorship. Record the standardized CRediT roles as\ncontribution metadata; CRediT does not itself define authorship [SW-S19].\n\nIf AI was used, humans must verify all affected content and disclose the tool and\npurpose according to current journal and publisher policy. ICMJE's January 2026\nRecommendations require transparency and retain human accountability [SW-S01, SW-S02].\n\n```bash\npython3 scripts\u002Fvalidate_authorship.py authorship.json\n```\n\nDo not generate a disclosure from assumptions. See\n`references\u002Fauthorship_ai_confidentiality.md`.\n\n### 9. Review declarations and open-science statements\n\nVerify each statement independently:\n\n- ethics and consent;\n- registration and protocol;\n- funding and sponsor role;\n- conflicts and relationships;\n- author contributions and acknowledgments;\n- data, code, materials, and protocol availability;\n- AI use.\n\nBe as open as rights and responsibilities permit, but do not expose confidential,\npersonal, proprietary, licensed, or protected information. Record actual access\nconditions. See `references\u002Fresearch_integrity_open_science.md`.\n\n### 10. Use figures and tables only when warranted\n\nFigures and tables are optional and provenance-bound. This skill does not generate\nimages or schematics.\n\nFor every retained display:\n\n- link source data, code, transformations, and evidence IDs;\n- reconcile values with prose and registries;\n- document image processing, permissions, and licenses;\n- include units, denominators, sample sizes, uncertainty, and analysis population;\n- provide alt text and redundant non-color cues;\n- perform a manual accessibility and scientific check at final size.\n\nSee `references\u002Ffigures_tables.md`.\n\n### 11. Record non-scoring guideline coverage\n\nRecord each bundled high-level topic as addressed, not applicable with rationale, or\nmissing:\n\n```bash\npython3 scripts\u002Fselect_reporting_guidelines.py check reporting_coverage.json\n```\n\nThen complete the official checklist using actual manuscript locations. Never claim\nadherence merely because the local coverage file passes.\n\n### 12. Lint and approve\n\n```bash\npython3 scripts\u002Fvalidate_manifest.py manuscript_manifest.json --kind manuscript\npython3 scripts\u002Flint_manuscript.py manuscript.md \\\n  --manifest manuscript_manifest.json\n```\n\nThe linter reports issue codes and line numbers without echoing manuscript text.\nSensitive-content warnings require manual review and are not a de-identification\ncertificate.\n\nOnly accountable humans may:\n\n- resolve scientific ambiguities;\n- approve author order and declarations;\n- approve external disclosure or transfer;\n- set `submission_ready` to true;\n- remove the draft banner;\n- authorize submission.\n\n## Revision and peer review\n\nTreat reviewer material as confidential. Do not upload it to an external service without\nthe required authorization and policy review [SW-S01, SW-S24].\n\nFor each requested change:\n\n1. record the comment without exposing it outside the approved boundary;\n2. classify it as editorial, scientific, statistical, policy, or unresolved;\n3. identify affected claims, evidence, methods, results, and displays;\n4. revise the registries before prose when facts change;\n5. re-run every affected audit;\n6. draft a response that states what changed and where;\n7. obtain human approval.\n\nDo not comply with a request that would fabricate, hide, overstate, or breach policy.\n\n## Current policy caution\n\nCOPE's 2017 Core Practices were retired in 2024. As of 2026-07-24, COPE announced that\na replacement Code of Conduct would be published in 2026; do not describe the archived\nCore Practices as current membership standards [SW-S04, SW-S05]. Distinguish formal\nCOPE positions from discussion documents, webinars, comments, and case advice.\n\n## Formatting and submission\n\nThe former LaTeX assets were removed because a generic polished template could allow\nplausible placeholders to ship. Use the Markdown scaffold and structured records.\nApply the target venue's current controlled template only after verification.\n\nSee:\n\n- `assets\u002FREPORT_FORMATTING_GUIDE.md`\n- `references\u002Fprofessional_report_formatting.md`\n- `references\u002Fjournal_policies.md`\n\nFormatting cannot convert an incomplete evidence record into a submission-ready paper.\n\n## Bundled files\n\n### Assets\n\n- `assets\u002Fmanuscript_scaffold.md`\n- `assets\u002Fmanuscript_manifest_template.json`\n- `assets\u002Fsource_manifest_template.json`\n- `assets\u002Fclaim_evidence_template.csv`\n- `assets\u002Fconsistency_manifest_template.json`\n- `assets\u002Fauthorship_template.json`\n- `assets\u002Freporting_coverage_template.json`\n- `assets\u002Freporting_guidelines.json`\n\n### Scripts\n\n- `scripts\u002Fscaffold_manuscript.py`\n- `scripts\u002Fvalidate_manifest.py`\n- `scripts\u002Fselect_reporting_guidelines.py`\n- `scripts\u002Faudit_claims.py`\n- `scripts\u002Fcheck_consistency.py`\n- `scripts\u002Fcheck_references.py`\n- `scripts\u002Fvalidate_authorship.py`\n- `scripts\u002Flint_manuscript.py`\n\nAll scripts are local, deterministic, bounded, dependency-free, and network-free. See\n`references\u002Fcli_reference.md`.\n\n### References\n\n- `references\u002Fevidence_workflow.md`\n- `references\u002Fwriting_principles.md`\n- `references\u002Fimrad_structure.md`\n- `references\u002Fcitation_styles.md`\n- `references\u002Freporting_guidelines.md`\n- `references\u002Ffigures_tables.md`\n- `references\u002Fauthorship_ai_confidentiality.md`\n- `references\u002Fresearch_integrity_open_science.md`\n- `references\u002Fjournal_policies.md`\n- `references\u002Fprofessional_report_formatting.md`\n- `references\u002Fcli_reference.md`\n- `references\u002Fsource_ledger.md`\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- `peer-review`: Prepare evidence-bounded, constructive peer-review drafts and structured manuscript assessments.\n- `clinical-reports`: Generates fail-closed draft JSON templates and runs local deterministic structure and consistency checks for clinical reports: CARE case re…\n- `research-lookup`: Compile current scholarly evidence for a scientific manuscript or research brief.\n","# Scientific Writing\n\n## Purpose\n\nProduce clear scientific prose without inventing evidence or concealing uncertainty.\nKeep drafting, evidence verification, and submission approval as separate stages.\n\nThe accountable human authors control scientific decisions and final approval. AI is\nnot an author, and generated fluency is never evidence [SW-S01, SW-S03].\n\n## Non-negotiable safety rules\n\n### Confidentiality\n\nDo not send unpublished manuscripts, peer-review or editorial material, sensitive or\nrestricted data, PHI or other personal data, proprietary content, or source documents\nto an external service without:\n\n1. explicit authorization from a person or body empowered to grant it; and\n2. a documented review of journal, institutional, funder, consent, ethics, contractual,\n   legal, and data-use policy.\n\nWhen authorization or policy is unclear, keep processing local and use only the minimum\nmetadata needed. De-identification requires expert review; removing obvious names is\nnot sufficient. See `references\u002Fauthorship_ai_confidentiality.md`.\n\n### No fabrication\n\nNever invent or complete:\n\n- citations, references, DOI, PMID, PMCID, ISBN, URLs, or quotations;\n- results, data values, denominators, sample sizes, units, effect estimates,\n  uncertainty, statistical tests, or significance claims;\n- methods, materials, protocol details, software versions, analysis choices, or\n  deviations;\n- registrations, approvals, consent, ethics statements, participant details, or dates;\n- authors, author order, CRediT roles, acknowledgments, or permissions;\n- funding, sponsor roles, conflicts, data or code availability, or AI disclosures.\n\nUse an explicit missing, unverified, or not-applicable state. Do not substitute plausible\nboilerplate.\n\n### Evidence binding\n\nEvery factual or numeric manuscript claim must map to verified evidence IDs. A human\nverifier must open the source, confirm the proposition and locator, verify bibliographic\nmetadata, and record who verified it and when.\n\nSearch snippets, generated summaries, memory, and another work's bibliography may aid\ndiscovery but do not verify a claim. See `references\u002Fevidence_workflow.md`.\n\n### Scientific fidelity\n\n- Preserve uncertainty and alternative explanations.\n- Distinguish confirmatory, exploratory, descriptive, and post hoc work.\n- Keep methods and results consistent.\n- Reconcile units, denominators, sample sizes, populations, time points, and labels.\n- Report negative, null, adverse, unexpected, failed, and inconclusive findings when\n  they belong to the study record.\n- State concrete limitations and bound generalizability.\n- Do not convert association into causation or non-significance into equivalence.\n\n## Intake\n\nBefore drafting, obtain or mark unresolved:\n\n- document type, study design, stage, audience, and target venue;\n- current author instructions and policy access date;\n- protocol, registration, analysis plan, amendments, and reporting guideline;\n- manuscript or section scope;\n- verified source manifest and claim registry;\n- methods, results, tables, figures, and supplements;\n- authorship, CRediT, declarations, and approval records;\n- confidentiality classification and authorized processing boundary;\n- data, code, materials, and repository constraints.\n\nDo not ask for restricted source material if metadata or a local user-run audit is\nsufficient.\n\n## Workflow\n\n### 1. Establish the local workspace\n\nFor a new draft, optionally generate fail-closed Markdown, JSON, and CSV scaffolds:\n\n```bash\npython3 scripts\u002Fscaffold_manuscript.py \\\n  --output-dir .\u002Fdraft-workspace \\\n  --document-id local-draft \\\n  --study-design randomized_trial \\\n  --guideline consort-2025\n```\n\nThe generator never overwrites files. Its output is explicitly not submission-ready and\ncontains placeholders that the linter rejects.\n\n### 2. Select reporting guidance\n\nChoose by actual design and article type, then open the current official statement,\nchecklist, explanation document, extensions, and target-journal instructions.\n\n```bash\npython3 scripts\u002Fselect_reporting_guidelines.py select \\\n  --study-design randomized_trial\n```\n\nCurrent major routes researched on 2026-07-24 include CONSORT 2025, SPIRIT 2025,\nPRISMA 2020, STROBE, STARD and STARD-AI, TRIPOD+AI, CARE, ARRIVE 2.0, SQUIRE 2.0,\nand CHEERS 2022 [SW-S06–SW-S18].\n\nThe selector is non-scoring. It does not certify quality, compliance, completeness, or\nacceptance. See `references\u002Freporting_guidelines.md`.\n\n### 3. Build the evidence record\n\nAssign:\n\n- `E` IDs to sources in `source_manifest.json`;\n- `C` IDs to claims in `claims.csv`;\n- `N`, `M`, `O`, and `R` IDs to numeric facts, methods, outcomes, and results in\n  `consistency_manifest.json`.\n\nStore a hash of claim text in CSV rather than raw claim text. During drafting, append:\n\n```text\n[claim:C001] [evidence:E001,E002]\n```\n\nDo not mark a source verified until an accountable human has opened it and confirmed\nthe exact support.\n\n### 4. Create an evidence outline\n\nOutline only from recorded evidence:\n\n- objective or question;\n- section purpose;\n- claim IDs and evidence IDs;\n- methods and result IDs;\n- analysis intent and uncertainty;\n- unresolved conflicts or missing information;\n- applicable reporting topics.\n\nKeep unsupported content in an unresolved-issues list, not manuscript prose.\n\n### 5. Draft without adding facts\n\nTransform the verified outline into venue-appropriate prose. Preserve all IDs during\ndrafting.\n\n- Match title and abstract to the completed main text.\n- Describe methods as performed.\n- Present results in the declared order and analysis population.\n- Separate result from interpretation unless the venue combines them.\n- Compare with prior evidence only after verifying it.\n- Keep conclusions within the observed design, population, and uncertainty.\n\nUse IMRAD only when appropriate. Structured abstracts, lists, combined sections, and\nalternative structures depend on study design and venue. See\n`references\u002Fimrad_structure.md` and `references\u002Fwriting_principles.md`.\n\n### 6. Reconcile methods and results\n\nRecord repeated numeric facts and method-result mappings, then run:\n\n```bash\npython3 scripts\u002Fcheck_consistency.py consistency_manifest.json\n```\n\nResolve every mismatch manually. A changed value may be a legitimate analysis-set\ndifference, but that difference must be named rather than silently normalized.\n\n### 7. Verify citations and claims\n\n```bash\npython3 scripts\u002Fvalidate_manifest.py source_manifest.json \\\n  --kind source --require-verified\npython3 scripts\u002Faudit_claims.py manuscript.md claims.csv source_manifest.json\npython3 scripts\u002Fcheck_references.py source_manifest.json\n```\n\nThe reference checker validates syntax and duplicate identifiers without network\nresolution. A human must still compare every identifier and quotation with the opened\nsource. Follow NLM *Citing Medicine* or the current official style required by the\nvenue [SW-S20, SW-S21].\n\n### 8. Validate authorship and disclosure\n\nUse journal criteria for authorship. Record the standardized CRediT roles as\ncontribution metadata; CRediT does not itself define authorship [SW-S19].\n\nIf AI was used, humans must verify all affected content and disclose the tool and\npurpose according to current journal and publisher policy. ICMJE's January 2026\nRecommendations require transparency and retain human accountability [SW-S01, SW-S02].\n\n```bash\npython3 scripts\u002Fvalidate_authorship.py authorship.json\n```\n\nDo not generate a disclosure from assumptions. See\n`references\u002Fauthorship_ai_confidentiality.md`.\n\n### 9. Review declarations and open-science statements\n\nVerify each statement independently:\n\n- ethics and consent;\n- registration and protocol;\n- funding and sponsor role;\n- conflicts and relationships;\n- author contributions and acknowledgments;\n- data, code, materials, and protocol availability;\n- AI use.\n\nBe as open as rights and responsibilities permit, but do not expose confidential,\npersonal, proprietary, licensed, or protected information. Record actual access\nconditions. See `references\u002Fresearch_integrity_open_science.md`.\n\n### 10. Use figures and tables only when warranted\n\nFigures and tables are optional and provenance-bound. This skill does not generate\nimages or schematics.\n\nFor every retained display:\n\n- link source data, code, transformations, and evidence IDs;\n- reconcile values with prose and registries;\n- document image processing, permissions, and licenses;\n- include units, denominators, sample sizes, uncertainty, and analysis population;\n- provide alt text and redundant non-color cues;\n- perform a manual accessibility and scientific check at final size.\n\nSee `references\u002Ffigures_tables.md`.\n\n### 11. Record non-scoring guideline coverage\n\nRecord each bundled high-level topic as addressed, not applicable with rationale, or\nmissing:\n\n```bash\npython3 scripts\u002Fselect_reporting_guidelines.py check reporting_coverage.json\n```\n\nThen complete the official checklist using actual manuscript locations. Never claim\nadherence merely because the local coverage file passes.\n\n### 12. Lint and approve\n\n```bash\npython3 scripts\u002Fvalidate_manifest.py manuscript_manifest.json --kind manuscript\npython3 scripts\u002Flint_manuscript.py manuscript.md \\\n  --manifest manuscript_manifest.json\n```\n\nThe linter reports issue codes and line numbers without echoing manuscript text.\nSensitive-content warnings require manual review and are not a de-identification\ncertificate.\n\nOnly accountable humans may:\n\n- resolve scientific ambiguities;\n- approve author order and declarations;\n- approve external disclosure or transfer;\n- set `submission_ready` to true;\n- remove the draft banner;\n- authorize submission.\n\n## Revision and peer review\n\nTreat reviewer material as confidential. Do not upload it to an external service without\nthe required authorization and policy review [SW-S01, SW-S24].\n\nFor each requested change:\n\n1. record the comment without exposing it outside the approved boundary;\n2. classify it as editorial, scientific, statistical, policy, or unresolved;\n3. identify affected claims, evidence, methods, results, and displays;\n4. revise the registries before prose when facts change;\n5. re-run every affected audit;\n6. draft a response that states what changed and where;\n7. obtain human approval.\n\nDo not comply with a request that would fabricate, hide, overstate, or breach policy.\n\n## Current policy caution\n\nCOPE's 2017 Core Practices were retired in 2024. As of 2026-07-24, COPE announced that\na replacement Code of Conduct would be published in 2026; do not describe the archived\nCore Practices as current membership standards [SW-S04, SW-S05]. Distinguish formal\nCOPE positions from discussion documents, webinars, comments, and case advice.\n\n## Formatting and submission\n\nThe former LaTeX assets were removed because a generic polished template could allow\nplausible placeholders to ship. Use the Markdown scaffold and structured records.\nApply the target venue's current controlled template only after verification.\n\nSee:\n\n- `assets\u002FREPORT_FORMATTING_GUIDE.md`\n- `references\u002Fprofessional_report_formatting.md`\n- `references\u002Fjournal_policies.md`\n\nFormatting cannot convert an incomplete evidence record into a submission-ready paper.\n\n## Bundled files\n\n### Assets\n\n- `assets\u002Fmanuscript_scaffold.md`\n- `assets\u002Fmanuscript_manifest_template.json`\n- `assets\u002Fsource_manifest_template.json`\n- `assets\u002Fclaim_evidence_template.csv`\n- `assets\u002Fconsistency_manifest_template.json`\n- `assets\u002Fauthorship_template.json`\n- `assets\u002Freporting_coverage_template.json`\n- `assets\u002Freporting_guidelines.json`\n\n### Scripts\n\n- `scripts\u002Fscaffold_manuscript.py`\n- `scripts\u002Fvalidate_manifest.py`\n- `scripts\u002Fselect_reporting_guidelines.py`\n- `scripts\u002Faudit_claims.py`\n- `scripts\u002Fcheck_consistency.py`\n- `scripts\u002Fcheck_references.py`\n- `scripts\u002Fvalidate_authorship.py`\n- `scripts\u002Flint_manuscript.py`\n\nAll scripts are local, deterministic, bounded, dependency-free, and network-free. See\n`references\u002Fcli_reference.md`.\n\n### References\n\n- `references\u002Fevidence_workflow.md`\n- `references\u002Fwriting_principles.md`\n- `references\u002Fimrad_structure.md`\n- `references\u002Fcitation_styles.md`\n- `references\u002Freporting_guidelines.md`\n- `references\u002Ffigures_tables.md`\n- `references\u002Fauthorship_ai_confidentiality.md`\n- `references\u002Fresearch_integrity_open_science.md`\n- `references\u002Fjournal_policies.md`\n- `references\u002Fprofessional_report_formatting.md`\n- `references\u002Fcli_reference.md`\n- `references\u002Fsource_ledger.md`\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- `peer-review`: Prepare evidence-bounded, constructive peer-review drafts and structured manuscript assessments.\n- `clinical-reports`: Generates fail-closed draft JSON templates and runs local deterministic structure and consistency checks for clinical reports: CARE case re…\n- `research-lookup`: Compile current scholarly evidence for a scientific manuscript or research brief.\n",{"name":5,"license":15,"metadata":61,"description":7,"compatibility":66},{"tags":62,"version":63,"category":64,"maintainer":65},"scientific writing, manuscripts, academic writing, research papers, reporting guidelines","2.1","research-writing","Kalaris Labs","Requires Python 3.11+ only for optional dependency-free local CLIs; core guidance is platform-neutral. 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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":228,"name":229,"nameZh":6,"description":230,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":222,"licenseFileUrl":6,"sourceHtmlUrl":231,"categories":232,"keywords":233,"publishedAt":226},"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":235,"name":236,"nameZh":6,"description":237,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":222,"licenseFileUrl":6,"sourceHtmlUrl":238,"categories":239,"keywords":240,"publishedAt":226},"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":242,"name":243,"nameZh":6,"description":244,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":222,"licenseFileUrl":6,"sourceHtmlUrl":245,"categories":246,"keywords":247,"publishedAt":56},"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",[],[248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267],"search","deja search","never","error","ready","memory","re","decide","fix","design","debugging","when","you","done","reviewer","io","change","cli","merge","past",{"skillNo":269,"name":270,"nameZh":6,"description":271,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":222,"licenseFileUrl":6,"sourceHtmlUrl":272,"categories":273,"keywords":274,"publishedAt":56},"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",[],[275,276,277,263,278,279,280,281,282,283,284,285,286,287,253,288,289,290,291,292],"deja reflect","reflect","review","with","record","github","next","https","node","Rules","decisions","brew","remember","project rules","project","session","end","reflection",{"skillNo":294,"name":295,"nameZh":6,"description":296,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":222,"licenseFileUrl":6,"sourceHtmlUrl":297,"categories":298,"keywords":306,"publishedAt":56},"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",[299,302,303],{"categoryNo":300,"categoryName":301,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000016","图像与多媒体",{"categoryNo":27,"categoryName":28,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":304,"categoryName":305,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口",[295,307,308,309,310,311,312,289,313,254,314,315,316,317,259,318,319,320,321,322],"URL","Video","short-form","standalone","PDF","viral","tiktok","short","social","MCP","song","image","mcp server","schedule","api","publish"]