[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6XGJGW42MAZHZ1A2TJF0ACQ":3,"skill-same:01M6XGJGW42MAZHZ1A2TJF0ACQ":73},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":23,"publishedAt":44,"linkOnly":45,"markdownContent":46,"markdownBody":47,"frontMatter":48,"refName":53,"commitSha":54,"skillDirectory":55,"forksCount":14,"repositoryHtmlUrl":56,"repositoryHomepageUrl":6,"ownerHtmlUrl":57,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":58,"install":59,"resources":72},"01M6XGJGW42MAZHZ1A2TJF0ACQ","skill-installer",null,"Install or remove third-party openscience skills from a public git repository. Use when the user says \"add this skill \u003Curl>\", \"install skill \u003Curl>\", or \"remove skill \u003Cnamespace>\". The skill runs locally via `openscience skill add|list|remove`, fetches the repo, runs a 6-layer safety gate (regex + server-side Haiku classifier), prompts the user to confirm, then writes the skills to ~\u002F.openscience\u002Finstalled-skills\u002F and uploads to the dashboard for cross-machine sync.","fadrienne\u002FMyGitHub","fadrienne","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F197874571?v=4","01M6X64V8VWCRSMFZ4EPS2BWXG","Francesca Adrienne","01M6X64VD5MW4F9PHDWQKS7ZAF",0,"MIT","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fother\u002Fskill-installer\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发","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],"skill installer","installer","classifier","when","for","URL","machine","via","regex","install skill","server-side","uploads","dashboard","use","remove","repo","haiku","repository","server","sync","2026-11-10 10:12:30",false,"---\nname: skill-installer\ndescription: Install or remove third-party openscience skills from a public git repository. Use when the user says \"add this skill \u003Curl>\", \"install skill \u003Curl>\", or \"remove skill \u003Cnamespace>\". The skill runs locally via `openscience skill add|list|remove`, fetches the repo, runs a 6-layer safety gate (regex + server-side Haiku classifier), prompts the user to confirm, then writes the skills to ~\u002F.openscience\u002Finstalled-skills\u002F and uploads to the dashboard for cross-machine sync.\ncategory: other\nlicense: MIT license\nmetadata:\n    skill-author: InkVell Inc.\n---\n\n# Skill Installer\n\n## Overview\n\nInstalls URL-supplied third-party skills into the openscience CLI. The skill itself is a thin wrapper around the bundled `openscience skill` bash subcommand — when the user expresses install\u002Funinstall intent, invoke `openscience skill add|list|remove` via the `bash` tool. The CLI handles fetching, the safety gate, confirmation, on-disk write, and cross-machine sync.\n\n## When to Use This Skill\n\nTrigger on user messages like:\n\n- \"Add this skill: \u003Curl>\"\n- \"Install \u003Curl>\" \u002F \"Install the skill at \u003Curl>\"\n- \"Set up skills from \u003Crepo>\"\n- \"Remove the X skill\" \u002F \"Uninstall \u003Cnamespace>\"\n- \"List my installed skills\"\n\nDo NOT use this skill for:\n\n- Generating new skills (different workflow — see `openscience learn`)\n- Installing system packages (use `bash` directly)\n- Anything that's not a openscience skill source\n\n## How to Use\n\n### Add a skill\n\n```\nopenscience skill add \u003Cgit-url>\n```\n\nAccepted URL forms:\n\n- `https:\u002F\u002Fgithub.com\u002F\u003Cowner>\u002F\u003Crepo>` — default branch, all skills in `skills\u002F**\u002FSKILL.md`\n- `https:\u002F\u002Fgithub.com\u002F\u003Cowner>\u002F\u003Crepo>\u002Ftree\u002F\u003Cref>` — specific ref\u002Ftag\n- `gh:\u003Cowner>\u002F\u003Crepo>` — shorthand\n- `gh:\u003Cowner>\u002F\u003Crepo>@\u003Cref>[\u002F\u003Cpath>]` — shorthand with ref and optional path scope\n- Generic `git+ssh:\u002F\u002F...` \u002F `https:\u002F\u002Fgitlab.com\u002F...` URLs\n\nWhat happens when the user runs `add`:\n\n1. CLI clones the repo into a temp dir, pins the commit SHA.\n2. Enumerates `skills\u002F\u003Cname>\u002FSKILL.md` files.\n3. Runs Layers 1, 2, 4 (local regex passes) on every SKILL.md + companion script.\n4. POSTs the surviving manifest to `\u002Fapi\u002Fcli\u002Fskill-review` for the Layer-3 Haiku classifier (server-side, sandboxed input with canary integrity check).\n5. Renders a confirm screen showing the manifest + warnings + classifier reasoning + first lines of each SKILL.md.\n6. On `y`, writes everything to `~\u002F.openscience\u002Finstalled-skills\u002F\u003Cnamespace>\u002F\u003Cname>\u002F` and uploads to the dashboard.\n\nThe `\u003Cnamespace>` is derived from the repo name (last URL segment, lowercased).\n\n### List installed skills\n\n```\nopenscience skill list\n```\n\nPrints `\u003Cnamespace>\u002F\u003Cname>` with a `⚠` marker for any skill the safety gate warned on.\n\n### Remove a skill\n\n```\nopenscience skill remove \u003Cnamespace>            # whole namespace\nopenscience skill remove \u003Cnamespace>\u002F\u003Cname>     # single skill within a namespace\n```\n\nSoft-deletes the cloud record (`archived_at` set) and removes the on-disk directory.\n\n## Examples\n\nUser: \"Install the brainstorming skill from Anthropic's superpowers repo.\"\n\nYou: invoke `bash` with `openscience skill add gh:anthropics\u002Fsuperpowers\u002Fskills\u002Fbrainstorming`. Wait for the spinner + confirm prompt. Relay the manifest to the user. Pass through their `y\u002FN` response.\n\nUser: \"Uninstall everything in superpowers.\"\n\nYou: invoke `bash` with `openscience skill remove superpowers`. Report the count of archived skills.\n\nUser: \"What's installed?\"\n\nYou: invoke `bash` with `openscience skill list`. Print the output.\n\n## Safety Notes\n\nThe user-facing `openscience skill add` command runs every install through 6 layers (regex catastrophic-reject, regex classifier-injection-reject, server-side LLM classifier, regex suspicious-warn, user-confirm screen, deferred `allowed-tools` sandboxing). Don't try to bypass any of these by editing `~\u002F.openscience\u002Finstalled-skills\u002F` directly — that breaks the cloud-sync invariant. Always go through the bash subcommand.\n\nIf `openscience skill add` reports `classifier unreachable`, the dashboard backend is down — surface that to the user and stop. Do not retry with a flag that disables the classifier without the user's explicit consent.\n\n## Reference\n\n- Spec: `thesis\u002Fdocs\u002Fsuperpowers\u002Fspecs\u002F2026-05-14-cli-add-skill-from-url-design.md`\n- Plan: `thesis\u002Fdocs\u002Fsuperpowers\u002Fplans\u002F2026-05-14-cli-add-skill-from-url.md`\n- On-disk layout: `~\u002F.openscience\u002Finstalled-skills\u002F\u003Cnamespace>\u002F\u003Cname>\u002FSKILL.md` (mirror of `~\u002F.openscience\u002Flearned-skills\u002F`)\n- Backend endpoints under `\u002Fapi\u002Fcli\u002Finstalled-skills` + `\u002Fapi\u002Fcli\u002Fskill-review`\n","# Skill Installer\n\n## Overview\n\nInstalls URL-supplied third-party skills into the openscience CLI. The skill itself is a thin wrapper around the bundled `openscience skill` bash subcommand — when the user expresses install\u002Funinstall intent, invoke `openscience skill add|list|remove` via the `bash` tool. The CLI handles fetching, the safety gate, confirmation, on-disk write, and cross-machine sync.\n\n## When to Use This Skill\n\nTrigger on user messages like:\n\n- \"Add this skill: \u003Curl>\"\n- \"Install \u003Curl>\" \u002F \"Install the skill at \u003Curl>\"\n- \"Set up skills from \u003Crepo>\"\n- \"Remove the X skill\" \u002F \"Uninstall \u003Cnamespace>\"\n- \"List my installed skills\"\n\nDo NOT use this skill for:\n\n- Generating new skills (different workflow — see `openscience learn`)\n- Installing system packages (use `bash` directly)\n- Anything that's not a openscience skill source\n\n## How to Use\n\n### Add a skill\n\n```\nopenscience skill add \u003Cgit-url>\n```\n\nAccepted URL forms:\n\n- `https:\u002F\u002Fgithub.com\u002F\u003Cowner>\u002F\u003Crepo>` — default branch, all skills in `skills\u002F**\u002FSKILL.md`\n- `https:\u002F\u002Fgithub.com\u002F\u003Cowner>\u002F\u003Crepo>\u002Ftree\u002F\u003Cref>` — specific ref\u002Ftag\n- `gh:\u003Cowner>\u002F\u003Crepo>` — shorthand\n- `gh:\u003Cowner>\u002F\u003Crepo>@\u003Cref>[\u002F\u003Cpath>]` — shorthand with ref and optional path scope\n- Generic `git+ssh:\u002F\u002F...` \u002F `https:\u002F\u002Fgitlab.com\u002F...` URLs\n\nWhat happens when the user runs `add`:\n\n1. CLI clones the repo into a temp dir, pins the commit SHA.\n2. Enumerates `skills\u002F\u003Cname>\u002FSKILL.md` files.\n3. Runs Layers 1, 2, 4 (local regex passes) on every SKILL.md + companion script.\n4. POSTs the surviving manifest to `\u002Fapi\u002Fcli\u002Fskill-review` for the Layer-3 Haiku classifier (server-side, sandboxed input with canary integrity check).\n5. Renders a confirm screen showing the manifest + warnings + classifier reasoning + first lines of each SKILL.md.\n6. On `y`, writes everything to `~\u002F.openscience\u002Finstalled-skills\u002F\u003Cnamespace>\u002F\u003Cname>\u002F` and uploads to the dashboard.\n\nThe `\u003Cnamespace>` is derived from the repo name (last URL segment, lowercased).\n\n### List installed skills\n\n```\nopenscience skill list\n```\n\nPrints `\u003Cnamespace>\u002F\u003Cname>` with a `⚠` marker for any skill the safety gate warned on.\n\n### Remove a skill\n\n```\nopenscience skill remove \u003Cnamespace>            # whole namespace\nopenscience skill remove \u003Cnamespace>\u002F\u003Cname>     # single skill within a namespace\n```\n\nSoft-deletes the cloud record (`archived_at` set) and removes the on-disk directory.\n\n## Examples\n\nUser: \"Install the brainstorming skill from Anthropic's superpowers repo.\"\n\nYou: invoke `bash` with `openscience skill add gh:anthropics\u002Fsuperpowers\u002Fskills\u002Fbrainstorming`. Wait for the spinner + confirm prompt. Relay the manifest to the user. Pass through their `y\u002FN` response.\n\nUser: \"Uninstall everything in superpowers.\"\n\nYou: invoke `bash` with `openscience skill remove superpowers`. Report the count of archived skills.\n\nUser: \"What's installed?\"\n\nYou: invoke `bash` with `openscience skill list`. Print the output.\n\n## Safety Notes\n\nThe user-facing `openscience skill add` command runs every install through 6 layers (regex catastrophic-reject, regex classifier-injection-reject, server-side LLM classifier, regex suspicious-warn, user-confirm screen, deferred `allowed-tools` sandboxing). Don't try to bypass any of these by editing `~\u002F.openscience\u002Finstalled-skills\u002F` directly — that breaks the cloud-sync invariant. Always go through the bash subcommand.\n\nIf `openscience skill add` reports `classifier unreachable`, the dashboard backend is down — surface that to the user and stop. Do not retry with a flag that disables the classifier without the user's explicit consent.\n\n## Reference\n\n- Spec: `thesis\u002Fdocs\u002Fsuperpowers\u002Fspecs\u002F2026-05-14-cli-add-skill-from-url-design.md`\n- Plan: `thesis\u002Fdocs\u002Fsuperpowers\u002Fplans\u002F2026-05-14-cli-add-skill-from-url.md`\n- On-disk layout: `~\u002F.openscience\u002Finstalled-skills\u002F\u003Cnamespace>\u002F\u003Cname>\u002FSKILL.md` (mirror of `~\u002F.openscience\u002Flearned-skills\u002F`)\n- Backend endpoints under `\u002Fapi\u002Fcli\u002Finstalled-skills` + `\u002Fapi\u002Fcli\u002Fskill-review`\n",{"name":5,"license":49,"category":50,"metadata":51,"description":7},"MIT license","other",{"skill-author":52},"InkVell Inc.","main","4aa39efd1cdab0b4657ecee16c92a92011a5b10f","openscience\u002Fbackend\u002Fcli\u002Fskills\u002Fother\u002Fskill-installer","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub","https:\u002F\u002Fgithub.com\u002Ffadrienne","2026-10-28 00:43:09",{"source":8,"skillName":5,"commands":60,"prompt":70,"zipAvailable":71},[61,64,67],{"tool":62,"command":63},"npx","npx skills add fadrienne\u002FMyGitHub --skill skill-installer",{"tool":65,"command":66},"bunx","bunx skills add fadrienne\u002FMyGitHub --skill skill-installer",{"tool":68,"command":69},"pnpm","pnpm dlx skills add fadrienne\u002FMyGitHub --skill skill-installer","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fother\u002Fskill-installer\u002FSKILL.md",true,[],[74,101,126,160,187,214],{"skillNo":75,"name":76,"nameZh":6,"description":77,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":78,"categories":79,"keywords":81,"publishedAt":44},"01M7167B28PK20HP7R7ZED2DY0","statistical-power","Sample size calculation, power analysis, and effect size conversion for scientific studies. Use BEFORE data collection (a priori) to determine required N, or after (post-hoc) to compute achieved power. Covers parametric tests (t-test, ANOVA, chi-square, proportions, correlation, regression), simulation-based power for complex\u002Fclustered\u002Fnested designs, and power curves. Use this skill when someone asks 'how many samples do I need', 'what is my power', 'what effect size can I detect', or any variant. Pairs with \u002Fexperimental-design (choose design first, then compute power) and \u002Fstatistical-analysis (analyze data after collection).","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fstatistical-power\u002FSKILL.md",[80],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[82,83,84,85,86,87,88,89,27,90,91,92,93,94,95,96,97,98,99,100],"power","statistical power","effect","scipy","regression","choose","design","uv","Bash","collection","analysis","calculation","ANOVA","conversion","analyze","t-test","chi-square","simulation","sample",{"skillNo":102,"name":103,"nameZh":6,"description":104,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":105,"categories":106,"keywords":110,"publishedAt":44},"01M71677EBCM8EPCE9BZCK8505","statistical-analysis","Guided statistical analysis with test selection and reporting. Use when you need help choosing appropriate tests for your data, assumption checking, power analysis, and APA-formatted results. Best for academic research reporting, test selection guidance. For implementing specific models programmatically use statsmodels.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fstatistical-analysis\u002FSKILL.md",[107],{"categoryNo":108,"categoryName":109,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",[92,111,82,112,113,114,27,115,116,117,28,118,37,119,120,121,122,123,124,125],"statistical analysis","academic research","implementing","selection","you","reporting","data","tests","with","test","research","results","statsmodels","models","help",{"skillNo":127,"name":128,"nameZh":6,"description":129,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":130,"categories":131,"keywords":141,"publishedAt":44},"01M71673R93XAHFH81DBNVNG7H","scientific-writing","Core skill for the deep research and writing tool. Write scientific manuscripts in full paragraphs (never bullet points). Use two-stage process with (1) section outlines with key points using research-lookup then (2) convert to flowing prose. IMRAD structure, citations (APA\u002FAMA\u002FVancouver), figures\u002Ftables, reporting guidelines (CONSORT\u002FSTROBE\u002FPRISMA), for research papers and journal submissions.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fscientific-writing\u002FSKILL.md",[132,135,136],{"categoryNo":133,"categoryName":134,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":137,"categoryName":138,"taxonomyType":139,"taxonomyTypeDesc":140},"01JBSEEDCAT000000000000036","内容创作者","PROFESSION","适用职业",[142,143,144,145,146,147,148,149,150,151,90,152,153,154,155,156,157,158,116,159],"writing","scientific","scientific writing","Citations","deep","deep research","never","papers","journal","false","api","prisma","structure","openrouter","Outlines","core skill","read","features",{"skillNo":161,"name":162,"nameZh":6,"description":163,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":164,"categories":165,"keywords":170,"publishedAt":44},"01M7166ZPKXBF4EBPHVTJPQBB1","scientific-visualization","Meta-skill for publication-ready figures. Use when creating journal submission figures requiring multi-panel layouts, significance annotations, error bars, colorblind-safe palettes, and specific journal formatting (Nature, Science, Cell). Orchestrates matplotlib\u002Fseaborn\u002Fplotly with publication styles. For quick exploration use seaborn or plotly directly.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fscientific-visualization\u002FSKILL.md",[166,169],{"categoryNo":167,"categoryName":168,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000013","分析与可视化",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[171,143,172,173,174,175,176,177,178,150,27,179,180,181,182,183,184,185,28,186],"visualization","scientific visualization","error","ready","quick","exploration","meta-skill","Meta","safe","plotly","annotations","formatting","Publication","figures","panel","submission",{"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":15,"licenseFileUrl":6,"sourceHtmlUrl":191,"categories":192,"keywords":199,"publishedAt":44},"01M7166VXJWQSSC3RQZP60WBGR","research-code","Debug, profile, review, and restructure Python research and analysis code. Use when analysis scripts crash, produce wrong results, run too slowly, or need a correctness review. Covers: reading tracebacks and diagnosing root causes, hypothesis-driven debugging, reviewing code for common statistical\u002Fnumerical errors, profiling data pipelines, improving reproducibility (seeds, environments, determinism), and structuring analysis scripts for sharing. Distinct from \u002Fexploratory-data-analysis (which understands data) and \u002Fstatistical-analysis (which chooses and runs tests) — this skill focuses on the code itself.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fresearch-code\u002FSKILL.md",[193,196],{"categoryNo":194,"categoryName":195,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":197,"categoryName":198,"taxonomyType":139,"taxonomyTypeDesc":140},"01JBSEEDCAT000000000000030","开发工程师",[200,201,121,202,203,204,205,89,206,27,90,92,207,208,209,210,211,212,158,213],"code","research code","which","memory","data-analysis","errors","debugging","common","review","data pipelines","run","scripts","line","write",{"skillNo":215,"name":216,"nameZh":6,"description":217,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":218,"licenseFileUrl":6,"sourceHtmlUrl":219,"categories":220,"keywords":227,"publishedAt":44},"01M7166CF5HSR7SVZYZFWTDX55","markdown-mermaid-writing","Comprehensive markdown and Mermaid diagram writing skill. Use when creating any scientific document, report, analysis, or visualization. Establishes text-based diagrams as the default documentation standard with full style guides (markdown + mermaid), 24 diagram type references, and 9 document templates.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fmarkdown-mermaid-writing\u002FSKILL.md",[221,224,225,226],{"categoryNo":222,"categoryName":223,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000002","文档处理",{"categoryNo":133,"categoryName":134,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":137,"categoryName":138,"taxonomyType":139,"taxonomyTypeDesc":140},[142,228,229,230,231,232,171,233,234,27,90,92,235,236,237,238,239,240,143,158],"markdown mermaid writing","mermaid","markdown","community","diagrams","project","default","team","guides","text","documentation","references","diagram"]