[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6MAZPJCKTDC288AGDS8G2AQ":3,"skill-same:01M6MAZPJCKTDC288AGDS8G2AQ":90},{"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":31,"publishedAt":54,"linkOnly":55,"markdownContent":56,"markdownBody":57,"frontMatter":58,"refName":66,"commitSha":67,"skillDirectory":68,"forksCount":69,"repositoryHtmlUrl":70,"repositoryHomepageUrl":71,"ownerHtmlUrl":72,"ownerBlogUrl":73,"ownerDescription":74,"updatedAt":75,"install":76,"resources":89},"01M6MAZPJCKTDC288AGDS8G2AQ","reproducibility-statement",null,"Prepare the reproducibility, transparency and open-science parts of a paper, including data and code availability statements, reproducibility checklists (NeurIPS, ICML, ICLR, ACL Responsible NLP, Nature reporting summaries), research artifact packaging (Zenodo DOI, CITATION.cff, environment lockfiles, seeds), ethics\u002Fbroader-impact statements, CRediT author contributions, competing interests and AI-use disclosures. Use when a venue requires any of these statements or checklists, or when preparing code\u002Fdata for release alongside a paper.","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\u002Freproducibility-statement\u002FSKILL.md",[18,23,28],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000021","资料检索与研究","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":26,"taxonomyTypeDesc":27},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",{"categoryNo":29,"categoryName":30,"taxonomyType":26,"taxonomyTypeDesc":27},"01JBSEEDCAT000000000000033","数据分析师",[32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53],"reproducibility statement","reproducibility","when","writing","open science","for","availability","doi","impact","transparency","runtime","artifact","packaging","environment","ethics","open","data","reporting","release","code","author contributions","nlp","2026-11-10 10:12:30",false,"---\nname: reproducibility-statement\ndescription: Prepare the reproducibility, transparency and open-science parts of a paper, including data and code availability statements, reproducibility checklists (NeurIPS, ICML, ICLR, ACL Responsible NLP, Nature reporting summaries), research artifact packaging (Zenodo DOI, CITATION.cff, environment lockfiles, seeds), ethics\u002Fbroader-impact statements, CRediT author contributions, competing interests and AI-use disclosures. Use when a venue requires any of these statements or checklists, or when preparing code\u002Fdata for release alongside a paper.\nlicense: MIT\ncompatibility: No runtime dependencies.\nmetadata:\n  version: \"1.0\"\n  category: research-writing\n  maintainer: Kalaris Labs\n  author: Kalaris Labs (Sayan Chowdhury)\n  tags: reproducibility, data availability, code availability, open science, FAIR, checklist, CRediT, Zenodo, ethics statement\n---\n\n# Reproducibility and Transparency Statements\n\n## Workflow\n\n1. List every statement and checklist the venue requires (section 1).\n2. Deposit data and code, and obtain persistent identifiers before writing the statements.\n3. Draft each statement with concrete locations, licenses and access conditions.\n4. Verify every link and accession while logged out. Never claim availability that does not exist yet.\n5. Answer checklists honestly, with a justification for each \"No\".\n\nEditors increasingly desk-reject for missing or vague statements, and reviewers\ncheck them. Treat each as a precise, verifiable claim, never boilerplate.\n\n## 1. Find out what the venue requires\n\nCheck the current author guidelines and checklist for the target venue (the `venue-templates`\nand journal-format skills point to them). Common requirements:\n\n| Venue type | Typical requirement |\n|---|---|\n| ML conferences (NeurIPS, ICML, ICLR) | Paper checklist in the PDF (reproducibility, compute, licenses, broader impact, LLM usage), code encouraged |\n| ACL \u002F ARR venues | Responsible NLP checklist, limitations section (mandatory), ethics statement |\n| Nature Portfolio | Data and code availability statements, reporting summary, source data for figures |\n| PLOS | Mandatory data availability statement; data must be available without restriction unless legally exempt |\n| Clinical journals | Trial registration, data sharing statement (ICMJE), reporting guideline checklist (CONSORT, STROBE, ...) |\n| Cell Press | STAR Methods with Key Resources Table and resource availability section |\n\n## 2. Data availability statement\n\nState **where**, **how** and **under what conditions**, with persistent identifiers:\n\n> The processed data supporting the findings are available at Zenodo (https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.XXXXXXX)\n> under CC BY 4.0. Raw sequencing reads are deposited in the SRA under BioProject PRJNAXXXXXX. Patient-level\n> clinical data cannot be shared publicly due to [regulation]. Requests can be made to [data access\n> committee contact], and access requires a data use agreement.\n\n- Use domain repositories where they exist (GEO\u002FSRA\u002FENA, PDB, PRIDE, OpenNeuro, Dryad, ICPSR,\n  PANGAEA). Otherwise use general ones (Zenodo, Figshare, OSF, Dataverse).\n- \"Available upon reasonable request\" alone is increasingly rejected. Explain the restriction and the access route.\n- Reserve DOIs before submission (Zenodo supports reserved DOIs). Use private reviewer links if the venue allows.\n\n## 3. Code availability and artifact packaging\n\nChecklist for the released repository:\n- [ ] Tagged release matching the paper version, archived to Zenodo (GitHub integration mints a DOI)\n- [ ] `README` with installation, a minimal example, and the exact commands that reproduce each table\u002Ffigure\n- [ ] Pinned environment: `uv.lock`\u002F`requirements.txt` with versions, `environment.yml`, or a container image digest\n- [ ] Random seeds, number of runs, and hardware noted, with variance reported (mean ± std over N seeds)\n- [ ] Data download\u002Fpreprocessing scripts (never ship data you're not licensed to redistribute)\n- [ ] Pretrained weights or checkpoints with checksums, if central to the claims\n- [ ] `LICENSE` (OSI-approved for code; CC BY for text\u002Fdata), and third-party licenses respected\n- [ ] `CITATION.cff` so GitHub shows \"Cite this repository\"\n- [ ] Anonymized for double-blind review (anonymous GitHub mirrors), de-anonymized for camera-ready\n\n## 4. Compute and environmental reporting (ML and simulation)\n\nReport hardware (GPU\u002FCPU type and count), total compute (GPU-hours) for main experiments\n*and* the full project, including hyperparameter search. Report software versions of key frameworks.\n\n## 5. Other statements\n\n- **Author contributions (CRediT)**: map each author to the 14 roles (Conceptualization,\n  Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation,\n  Writing – original draft, Writing – review & editing, Visualization, Supervision,\n  Project administration, Funding acquisition).\n- **Competing interests**: declare financial and non-financial interests, or state \"none\".\n- **Funding**: funder names and grant numbers exactly as the funder requires.\n- **Ethics**: IRB\u002Fethics committee name and approval number, consent procedures, animal\n  welfare approvals, dual-use considerations.\n- **Use of AI tools**: follow the venue's policy. Most require disclosure of generative AI use in\n  writing or analysis and forbid listing AI as an author. Describe what was used and how outputs were verified.\n- **Limitations \u002F broader impact**: concrete failure modes, populations or settings where results\n  may not hold, potential misuse, and mitigations.\n\n## Rules\n\n- Every statement must be true at submission time. Verify each link and accession works (logged out, private window).\n- Don't claim \"fully reproducible\" unless someone other than the author has rerun it from the README.\n- Answer checklists honestly. \"No\" with a justification beats an unjustified \"Yes\".\n\n## Related skills\n\n`ml-paper-writing`, `scientific-writing`, `venue-templates`, `datalad`, `lamindb`,\n`research-grants` (data management plans).\n","# Reproducibility and Transparency Statements\n\n## Workflow\n\n1. List every statement and checklist the venue requires (section 1).\n2. Deposit data and code, and obtain persistent identifiers before writing the statements.\n3. Draft each statement with concrete locations, licenses and access conditions.\n4. Verify every link and accession while logged out. Never claim availability that does not exist yet.\n5. Answer checklists honestly, with a justification for each \"No\".\n\nEditors increasingly desk-reject for missing or vague statements, and reviewers\ncheck them. Treat each as a precise, verifiable claim, never boilerplate.\n\n## 1. Find out what the venue requires\n\nCheck the current author guidelines and checklist for the target venue (the `venue-templates`\nand journal-format skills point to them). Common requirements:\n\n| Venue type | Typical requirement |\n|---|---|\n| ML conferences (NeurIPS, ICML, ICLR) | Paper checklist in the PDF (reproducibility, compute, licenses, broader impact, LLM usage), code encouraged |\n| ACL \u002F ARR venues | Responsible NLP checklist, limitations section (mandatory), ethics statement |\n| Nature Portfolio | Data and code availability statements, reporting summary, source data for figures |\n| PLOS | Mandatory data availability statement; data must be available without restriction unless legally exempt |\n| Clinical journals | Trial registration, data sharing statement (ICMJE), reporting guideline checklist (CONSORT, STROBE, ...) |\n| Cell Press | STAR Methods with Key Resources Table and resource availability section |\n\n## 2. Data availability statement\n\nState **where**, **how** and **under what conditions**, with persistent identifiers:\n\n> The processed data supporting the findings are available at Zenodo (https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.XXXXXXX)\n> under CC BY 4.0. Raw sequencing reads are deposited in the SRA under BioProject PRJNAXXXXXX. Patient-level\n> clinical data cannot be shared publicly due to [regulation]. Requests can be made to [data access\n> committee contact], and access requires a data use agreement.\n\n- Use domain repositories where they exist (GEO\u002FSRA\u002FENA, PDB, PRIDE, OpenNeuro, Dryad, ICPSR,\n  PANGAEA). Otherwise use general ones (Zenodo, Figshare, OSF, Dataverse).\n- \"Available upon reasonable request\" alone is increasingly rejected. Explain the restriction and the access route.\n- Reserve DOIs before submission (Zenodo supports reserved DOIs). Use private reviewer links if the venue allows.\n\n## 3. Code availability and artifact packaging\n\nChecklist for the released repository:\n- [ ] Tagged release matching the paper version, archived to Zenodo (GitHub integration mints a DOI)\n- [ ] `README` with installation, a minimal example, and the exact commands that reproduce each table\u002Ffigure\n- [ ] Pinned environment: `uv.lock`\u002F`requirements.txt` with versions, `environment.yml`, or a container image digest\n- [ ] Random seeds, number of runs, and hardware noted, with variance reported (mean ± std over N seeds)\n- [ ] Data download\u002Fpreprocessing scripts (never ship data you're not licensed to redistribute)\n- [ ] Pretrained weights or checkpoints with checksums, if central to the claims\n- [ ] `LICENSE` (OSI-approved for code; CC BY for text\u002Fdata), and third-party licenses respected\n- [ ] `CITATION.cff` so GitHub shows \"Cite this repository\"\n- [ ] Anonymized for double-blind review (anonymous GitHub mirrors), de-anonymized for camera-ready\n\n## 4. Compute and environmental reporting (ML and simulation)\n\nReport hardware (GPU\u002FCPU type and count), total compute (GPU-hours) for main experiments\n*and* the full project, including hyperparameter search. Report software versions of key frameworks.\n\n## 5. Other statements\n\n- **Author contributions (CRediT)**: map each author to the 14 roles (Conceptualization,\n  Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation,\n  Writing – original draft, Writing – review & editing, Visualization, Supervision,\n  Project administration, Funding acquisition).\n- **Competing interests**: declare financial and non-financial interests, or state \"none\".\n- **Funding**: funder names and grant numbers exactly as the funder requires.\n- **Ethics**: IRB\u002Fethics committee name and approval number, consent procedures, animal\n  welfare approvals, dual-use considerations.\n- **Use of AI tools**: follow the venue's policy. Most require disclosure of generative AI use in\n  writing or analysis and forbid listing AI as an author. Describe what was used and how outputs were verified.\n- **Limitations \u002F broader impact**: concrete failure modes, populations or settings where results\n  may not hold, potential misuse, and mitigations.\n\n## Rules\n\n- Every statement must be true at submission time. Verify each link and accession works (logged out, private window).\n- Don't claim \"fully reproducible\" unless someone other than the author has rerun it from the README.\n- Answer checklists honestly. \"No\" with a justification beats an unjustified \"Yes\".\n\n## Related skills\n\n`ml-paper-writing`, `scientific-writing`, `venue-templates`, `datalad`, `lamindb`,\n`research-grants` (data management plans).\n",{"name":5,"license":15,"metadata":59,"description":7,"compatibility":65},{"tags":60,"author":61,"version":62,"category":63,"maintainer":64},"reproducibility, data availability, code availability, open science, FAIR, checklist, CRediT, Zenodo, ethics statement","Kalaris Labs (Sayan Chowdhury)","1.0","research-writing","Kalaris Labs","No runtime dependencies.","main","2b63f856ce19c2a797929cacabd9e0fc936812fa","plugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Freproducibility-statement",474,"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.","2026-11-11 14:40:14",{"source":8,"skillName":5,"commands":77,"prompt":87,"zipAvailable":88},[78,81,84],{"tool":79,"command":80},"npx","npx skills add hashgraph-online\u002Fawesome-codex-plugins --skill reproducibility-statement",{"tool":82,"command":83},"bunx","bunx skills add hashgraph-online\u002Fawesome-codex-plugins --skill reproducibility-statement",{"tool":85,"command":86},"pnpm","pnpm dlx skills add hashgraph-online\u002Fawesome-codex-plugins --skill reproducibility-statement","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Freproducibility-statement\u002FSKILL.md",true,[],[91,100,107,114,140,165],{"skillNo":92,"name":93,"nameZh":6,"description":94,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":95,"licenseFileUrl":6,"sourceHtmlUrl":96,"categories":97,"keywords":98,"publishedAt":99},"01M746ZWACVKMZ1BJXJZHYJSSX","migrate-to-e2e","Moves 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":101,"name":102,"nameZh":6,"description":103,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":95,"licenseFileUrl":6,"sourceHtmlUrl":104,"categories":105,"keywords":106,"publishedAt":99},"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":108,"name":109,"nameZh":6,"description":110,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":95,"licenseFileUrl":6,"sourceHtmlUrl":111,"categories":112,"keywords":113,"publishedAt":99},"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":115,"name":116,"nameZh":6,"description":117,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":95,"licenseFileUrl":6,"sourceHtmlUrl":118,"categories":119,"keywords":120,"publishedAt":54},"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",[],[121,122,123,124,125,126,127,128,129,130,131,34,132,133,134,135,136,137,138,139],"search","deja search","never","error","ready","memory","re","decide","fix","design","debugging","you","done","reviewer","io","change","cli","merge","past",{"skillNo":141,"name":142,"nameZh":6,"description":143,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":95,"licenseFileUrl":6,"sourceHtmlUrl":144,"categories":145,"keywords":146,"publishedAt":54},"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",[],[147,148,149,135,150,151,152,153,154,155,156,157,158,159,126,160,161,162,163,164],"deja reflect","reflect","review","with","record","github","next","https","node","Rules","decisions","brew","remember","project rules","project","session","end","reflection",{"skillNo":166,"name":167,"nameZh":6,"description":168,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":95,"licenseFileUrl":6,"sourceHtmlUrl":169,"categories":170,"keywords":180,"publishedAt":54},"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",[171,174,177],{"categoryNo":172,"categoryName":173,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000016","图像与多媒体",{"categoryNo":175,"categoryName":176,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":178,"categoryName":179,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口",[167,181,182,183,184,185,186,161,187,127,188,189,190,191,34,192,193,194,195,196],"URL","Video","short-form","standalone","PDF","viral","tiktok","short","social","MCP","song","image","mcp server","schedule","api","publish"]