[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-openai-locus-to-gene-mapper-skill":3,"mdc-kb7qi5-key":39,"related-repo-openai-locus-to-gene-mapper-skill":4246,"related-org-openai-locus-to-gene-mapper-skill":4370},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":11,"stars":28,"repoUrl":29,"updatedAt":30,"license":31,"forks":32,"topics":33,"repo":34,"sourceUrl":37,"mdContent":38},"locus-to-gene-mapper-skill","map GWAS loci to candidate genes","Map GWAS loci to ranked candidate genes using a deterministic multi-skill chain (EFO -> GWAS -> coordinates -> Open Targets L2G\u002Fcoloc -> eQTL -> burden\u002Fcoding context), with reproducible tables and optional figures. Use when a user provides a trait\u002FEFO term and\u002For lead variants and needs locus-to-gene prioritization for downstream biology decisions.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},"openai","OpenAI","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Fopenai.png",[12,16,19,22,25],{"name":13,"slug":14,"type":15},"Research","research","tag",{"name":17,"slug":18,"type":15},"Data Pipeline","data-pipeline",{"name":20,"slug":21,"type":15},"Data Analysis","data-analysis",{"name":23,"slug":24,"type":15},"Life Sciences","life-sciences",{"name":26,"slug":27,"type":15},"Bioinformatics","bioinformatics",3992,"https:\u002F\u002Fgithub.com\u002Fopenai\u002Fplugins","2026-04-10T05:12:20.538257",null,465,[],{"repoUrl":29,"stars":28,"forks":32,"topics":35,"description":36},[],"OpenAI Plugins","https:\u002F\u002Fgithub.com\u002Fopenai\u002Fplugins\u002Ftree\u002FHEAD\u002Fplugins\u002Flife-science-research\u002Fskills\u002Flocus-to-gene-mapper-skill","---\nname: locus-to-gene-mapper-skill\ndescription: Map GWAS loci to ranked candidate genes using a deterministic multi-skill chain (EFO -> GWAS -> coordinates -> Open Targets L2G\u002Fcoloc -> eQTL -> burden\u002Fcoding context), with reproducible tables and optional figures. Use when a user provides a trait\u002FEFO term and\u002For lead variants and needs locus-to-gene prioritization for downstream biology decisions.\n---\n\n## Locus-to-Gene Mapper\n\nGenerate a reproducible locus-to-gene mapping for one trait (or a seed set of lead variants), with explicit evidence attribution and conservative confidence labels.\n\nThis skill is optimized for bioinformaticians who need executable, traceable mapping from variant signals to plausible causal genes.\n\n## Required Inputs\n\nProvide at least one anchor source:\n\n- `trait_query` (string), for example `chronic obstructive pulmonary disease`\n- `efo_id` (string), for example `EFO_0000341`\n- `seed_rsids` (list[string]), for example `[\"rs1873625\", \"rs7903146\"]`\n\n## Optional Inputs\n\n- `target_gene` (string), optional gene of interest for highlighting in output\n- `show_child_traits` (bool), default `true`\n- `phenotype_terms` (list[string]), optional additional terms to include when finding anchors\n- `max_anchor_associations` (int), default `1200`\n- `max_loci` (int), default `25`\n- `max_genes_per_locus` (int), default `10`\n- `max_coloc_rows_per_locus` (int), default `100`\n- `max_eqtl_rows_per_variant` (int), default `200`\n- `genebass_burden_sets` (list[string]), default `[\"pLoF\", \"missense|LC\"]`\n- `include_clinvar` (bool), default `true`\n- `include_gnomad_context` (bool), default `true`\n- `include_hpa_tissue_context` (bool), default `true`\n- `include_figures` (bool), default `false`\n- `disable_default_seeds` (bool), default `false`; if `false`, common traits automatically get built-in seed rsIDs\n- `figure_output_dir` (string), default `.\u002Foutput\u002Ffigures`\n- `mapping_output_path` (string), default `.\u002Foutput\u002Flocus_to_gene_mapping.json`\n- `summary_output_path` (string), default `.\u002Foutput\u002Flocus_to_gene_summary.md`\n\n## Runtime Requirements\n\n- Python `3.11+`\n- `requests`\n- Optional for figure generation: `matplotlib`, `seaborn`, `pandas`\n\n## Bundled Script (Deterministic Runner)\n\n- Primary entrypoint: `scripts\u002Fmap_locus_to_gene.py`\n- This script:\n  - resolves trait\u002FEFO and anchor variants,\n  - resolves seed and anchor rsID coordinates directly through NCBI RefSNP\u002FdbSNP placements,\n  - gathers locus-to-gene evidence through the chained skills,\n  - writes mapping JSON and summary markdown,\n  - optionally renders figures when plotting deps are available.\n\nRun:\n\n```bash\npython locus-to-gene-mapper-skill\u002Fscripts\u002Fmap_locus_to_gene.py \\\n  --input-json \u002Fpath\u002Fto\u002Finput.json \\\n  --print-result\n```\n\nQuick start (no input JSON file):\n\n```bash\npython locus-to-gene-mapper-skill\u002Fscripts\u002Fmap_locus_to_gene.py \\\n  --trait-query \"type 2 diabetes\" \\\n  --print-result\n```\n\nTrait-only runs default to `include_figures=true` unless explicitly disabled with `--no-include-figures`.\n\nMinimal input JSON:\n\n```json\n{\n  \"trait_query\": \"type 2 diabetes\"\n}\n```\n\nBuilt-in default seeds (when `disable_default_seeds=false`):\n\n- `type 2 diabetes` \u002F `t2d` -> `rs7903146`, `rs13266634`, `rs7756992`, `rs5219`, `rs1801282`, `rs4402960`\n- `coronary artery disease` \u002F `cad` -> `rs1333049`, `rs4977574`, `rs9349379`, `rs6725887`, `rs1746048`, `rs3184504`\n- `body mass index` \u002F `bmi` -> `rs9939609`, `rs17782313`, `rs6548238`, `rs10938397`, `rs7498665`, `rs7138803`\n- `asthma` -> `rs7216389`, `rs2305480`, `rs9273349`\n- `rheumatoid arthritis` -> `rs2476601`, `rs3761847`, `rs660895`\n- `alzheimer disease` -> `rs429358`, `rs7412`, `rs6733839`, `rs11136000`, `rs3851179`\n- `ldl cholesterol` \u002F `total cholesterol` -> `rs7412`, `rs429358`, `rs6511720`, `rs629301`, `rs12740374`, `rs11591147`\n\n## Autonomous Execution Contract (Embedded Behavior)\n\nWhen a user asks for locus-to-gene mapping and gives only a trait (for example, `type 2 diabetes`), do the following automatically:\n\n1. Run the bundled script with `--trait-query \"\u003Cuser_trait>\" --print-result` (no manual JSON required).\n2. If it returns `No anchors remained`, rerun once with a built-in default seed rsID for that trait (unless `disable_default_seeds=true`).\n3. Read the generated `mapping_output_path` and `summary_output_path`.\n4. Return this concise response structure:\n   - `Top 5 cross-locus prioritized genes`\n   - `Per-locus top gene (score, confidence)`\n   - `Visualization artifact` (figure path(s) or Mermaid fallback block)\n   - `Warnings and limitations`\n5. For inline image rendering in chat:\n   - read `inline_image_markdown` from script result\n   - emit those lines exactly as plain markdown (no code fences)\n   - if inline rendering still fails, instruct user to upload PNG files into the chat\n\nDo not ask the user to run python manually unless execution is actually blocked.\n\n## Skill Chaining Order (Mandatory)\n\nUse these skills in order. Skip only when an earlier step is not needed by provided inputs.\n\n1. `efo-ontology-skill`\n   - Resolve `trait_query` to canonical EFO term and synonyms.\n   - Expand descendants when `show_child_traits=true`.\n2. `gwas-catalog-skill`\n   - Discover anchor variants for the trait\u002FEFO scope.\n   - Pull association\u002Fstudy metadata for locus context.\n3. Built-in NCBI RefSNP coordinate resolution\n   - Normalize each anchor rsID to GRCh37\u002FGRCh38 top-level chromosome placements.\n4. `opentargets-skill`\n   - Retrieve credible set context, L2G predictions, and colocalisation evidence per locus.\n5. `gtex-eqtl-skill`\n   - Retrieve single-tissue eQTL support for anchor variants.\n6. `genebass-gene-burden-skill`\n   - Retrieve rare-variant burden support for candidate genes.\n7. `clinvar-variation-skill` (when `include_clinvar=true`)\n   - Add variant clinical\u002Fcoding annotations.\n8. `gnomad-graphql-skill` (when `include_gnomad_context=true`)\n   - Add frequency and gene-level constraint context.\n9. `human-protein-atlas-skill` (when `include_hpa_tissue_context=true`)\n   - Add tissue plausibility context for top genes.\n\nNever perform additional retrieval after final candidate-gene scoring starts.\n\n## Output Contract (Required)\n\nAlways return:\n\n1. `locus_to_gene_mapping.json`\n2. `locus_to_gene_summary.md`\n\n### JSON contract\n\n```json\n{\n  \"meta\": {\n    \"trait_query\": \"...\",\n    \"efo_id\": \"EFO_...\",\n    \"generated_at\": \"ISO-8601\",\n    \"sources_queried\": []\n  },\n  \"anchors\": [\n    {\n      \"rsid\": \"rs...\",\n      \"grch38\": {\"chr\": \"3\", \"pos\": 49629531, \"ref\": \"A\", \"alt\": \"C\"},\n      \"lead_trait\": \"...\",\n      \"p_value\": 2e-11,\n      \"cohort\": \"...\"\n    }\n  ],\n  \"loci\": [\n    {\n      \"locus_id\": \"chr3:49000000-50200000\",\n      \"lead_rsid\": \"rs...\",\n      \"candidate_genes\": [\n        {\n          \"symbol\": \"MST1\",\n          \"ensembl_id\": \"ENSG...\",\n          \"overall_score\": 0.71,\n          \"confidence\": \"High|Medium|Low|VeryLow\",\n          \"evidence\": {\n            \"l2g_max\": 0.83,\n            \"coloc_max_h4\": 0.84,\n            \"eqtl_tissues\": [\"Lung\"],\n            \"rare_variant_support\": \"none|nominal|strong\",\n            \"coding_support\": \"none|noncoding|coding\",\n            \"clinvar_support\": \"none|present\",\n            \"gnomad_context\": \"...\",\n            \"hpa_tissue_support\": [\"lung\"]\n          },\n          \"rationale\": [\n            \"...\"\n          ],\n          \"limitations\": [\n            \"...\"\n          ]\n        }\n      ]\n    }\n  ],\n  \"cross_locus_ranked_genes\": [\n    {\n      \"symbol\": \"...\",\n      \"supporting_loci\": 3,\n      \"mean_score\": 0.62,\n      \"max_score\": 0.81\n    }\n  ],\n  \"warnings\": [],\n  \"limitations\": []\n}\n```\n\n### Markdown summary contract\n\nThe summary must include sections in this exact order:\n\n1. `Objective`\n2. `Inputs and scope`\n3. `Anchor variant summary`\n4. `Per-locus top genes`\n5. `Cross-locus prioritized genes`\n6. `Key caveats`\n7. `Recommended next analyses`\n\n## Optional Figure Contract\n\nOnly produce figures when `include_figures=true`.\n\nIf figures are generated, append this block to JSON:\n\n```json\n{\n  \"figures\": [\n    {\n      \"id\": \"locus_gene_heatmap\",\n      \"path\": \".\u002Foutput\u002Ffigures\u002Flocus_gene_heatmap.png\",\n      \"caption\": \"Top candidate genes by evidence component across loci\"\n    }\n  ]\n}\n```\n\nRecommended figure set:\n\n1. `locus_gene_heatmap.png`\n   - Rows: top genes, columns: evidence components (`L2G`, `coloc`, `eQTL`, `burden`, `coding`).\n2. `locus_score_decomposition.png`\n   - Stacked bars per locus for top 3 genes.\n3. `tissue_support_dotplot.png`\n   - Gene-by-tissue evidence dots from GTEx\u002FHPA context.\n\nIf plotting dependencies are unavailable, skip PNG generation and output Mermaid diagrams in markdown as fallback.\nThe script also returns `inline_image_markdown` and `render_instructions` fields to support inline chat rendering.\n\n## Scoring Rules (Deterministic)\n\nFor each candidate gene per locus, compute:\n\n- `l2g_component`: max L2G score for the gene in locus (`0..1`)\n- `coloc_component`: max `h4` (or `clpp` when only CLPP is available), clipped to `0..1`\n- `eqtl_component`: `min(1, relevant_tissue_hits \u002F 3)`\n- `burden_component`:\n  - `1.0` if burden `p \u003C 2.5e-6`\n  - `0.6` if `2.5e-6 \u003C= p \u003C 0.05`\n  - `0.0` otherwise\n- `coding_component`:\n  - `1.0` for coding consequence in target gene with supportive ClinVar annotation\n  - `0.6` for coding consequence in target gene without supportive ClinVar annotation\n  - `0.3` for noncoding-in-gene support only\n  - `0.0` otherwise\n\nOverall score:\n\n`overall_score = 0.40*l2g + 0.25*coloc + 0.15*eqtl + 0.10*burden + 0.10*coding`\n\nConfidence label:\n\n- `High` if score `>= 0.75`\n- `Medium` if `0.55 \u003C= score \u003C 0.75`\n- `Low` if `0.35 \u003C= score \u003C 0.55`\n- `VeryLow` if score `\u003C 0.35`\n\n## Pipeline Contract\n\n### Phase 0: Validate and normalize input\n\n- Enforce that at least one of `trait_query`, `efo_id`, `seed_rsids` is present.\n- Normalize rsID formatting and deduplicate seed variants.\n- Resolve free-text trait to one canonical EFO term when needed.\n\n### Phase 1: Build anchor set\n\n- If trait\u002FEFO input is provided, pull associations and rank anchors by p-value and effect availability.\n- Merge trait-derived anchors with user-supplied `seed_rsids`.\n- Cap anchors using `max_loci` and log dropped anchors in `warnings`.\n\n### Phase 2: Gather locus-to-gene evidence\n\n- Normalize anchor coordinates (both builds when possible).\n- Pull Open Targets locus evidence (credible set\u002FL2G\u002Fcoloc).\n- Pull GTEx variant-level eQTL rows.\n- Pull gene-level burden results for mapped candidate genes.\n- Pull ClinVar and gnomAD context when enabled.\n\n### Phase 3: Harmonize and score\n\n- Build a per-locus candidate-gene table.\n- Compute deterministic component scores and overall score.\n- Create cross-locus aggregate rankings.\n\n### Phase 4: Synthesize outputs\n\n- Write JSON mapping file.\n- Write markdown summary in exact section order.\n- Optionally generate figures and append `figures` metadata.\n\n### Phase 5: QC gates\n\nFail the run when any of the following occurs:\n\n- No anchors after normalization.\n- Unresolved GRCh38 coordinates should be surfaced as `status=degraded`, not treated as an analytically clean pass.\n- Any locus has candidate genes without score fields.\n- `overall_score` outside `0..1`.\n- Summary section order mismatch.\n- Claim of causality without explicit evidence support in rationale text.\n\n## Public Interface\n\n```python\ndef map_locus_to_gene(input_json: dict) -> dict:\n    ...\n```\n\nReturn:\n\n```json\n{\n  \"status\": \"ok\",\n  \"mapping_output_path\": \".\u002Foutput\u002Flocus_to_gene_mapping.json\",\n  \"summary_output_path\": \".\u002Foutput\u002Flocus_to_gene_summary.md\",\n  \"figure_paths\": [],\n  \"warnings\": [],\n  \"limitations\": []\n}\n```\n\n## Non-Invention Rules\n\n- Never invent rsIDs, p-values, scores, cohort labels, tissues, or gene links.\n- Never silently impute missing evidence as positive support.\n- When evidence is 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var(--shiki-light-text-decoration);}html.light .shiki span {color: var(--shiki-light);background: var(--shiki-light-bg);font-style: var(--shiki-light-font-style);font-weight: var(--shiki-light-font-weight);text-decoration: var(--shiki-light-text-decoration);}html .default .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}html.dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}",{"items":4247,"total":4369},[4248,4267,4283,4295,4315,4337,4357],{"slug":4249,"name":4249,"fn":4250,"description":4251,"org":4252,"tags":4253,"stars":28,"repoUrl":29,"updatedAt":4266},"accessibility-and-inclusive-visualization","make data visualizations accessible","Make data visualizations accessible and inclusive. Use when the user needs chart or diagram accessibility guidance, text alternatives for complex visuals, color and contrast review, keyboard support, reduced-motion behavior for animation or parallax, or an accessibility QA workflow for exported figures, UML-like diagrams, and dashboards.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[4254,4257,4260,4263],{"name":4255,"slug":4256,"type":15},"Accessibility","accessibility",{"name":4258,"slug":4259,"type":15},"Charts","charts",{"name":4261,"slug":4262,"type":15},"Data Visualization","data-visualization",{"name":4264,"slug":4265,"type":15},"Design","design","2026-06-30T19:00:57.102",{"slug":4268,"name":4268,"fn":4269,"description":4270,"org":4271,"tags":4272,"stars":28,"repoUrl":29,"updatedAt":4282},"agent-browser","automate browser interactions for agents","Browser automation CLI for AI agents. Use when the user needs to interact with websites, verify dev server output, test web apps, navigate pages, fill forms, click buttons, take screenshots, extract data, or automate any browser task. Also triggers when a dev server starts so you can verify it visually.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[4273,4276,4279],{"name":4274,"slug":4275,"type":15},"Agents","agents",{"name":4277,"slug":4278,"type":15},"Browser Automation","browser-automation",{"name":4280,"slug":4281,"type":15},"Testing","testing","2026-04-06T18:41:03.44016",{"slug":4284,"name":4284,"fn":4285,"description":4286,"org":4287,"tags":4288,"stars":28,"repoUrl":29,"updatedAt":4294},"agent-browser-verify","verify dev server output with automated browser","Automated browser verification for dev servers. Triggers when a dev server starts to run a visual gut-check with agent-browser — verifies the page loads, checks for console errors, validates key UI elements, and reports pass\u002Ffail before continuing.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[4289,4290,4293],{"name":4277,"slug":4278,"type":15},{"name":4291,"slug":4292,"type":15},"Local Development","local-development",{"name":4280,"slug":4281,"type":15},"2026-04-06T18:41:17.526867",{"slug":4296,"name":4296,"fn":4297,"description":4298,"org":4299,"tags":4300,"stars":28,"repoUrl":29,"updatedAt":4314},"agents-sdk","build AI agents on Cloudflare Workers","Build AI agents on Cloudflare Workers using the Agents SDK. Load when creating stateful agents, durable workflows, real-time WebSocket apps, scheduled tasks, MCP servers, or chat applications. Covers Agent class, state management, callable RPC, Workflows integration, and React hooks. Biases towards retrieval from Cloudflare docs over pre-trained knowledge.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[4301,4302,4305,4308,4311],{"name":4274,"slug":4275,"type":15},{"name":4303,"slug":4304,"type":15},"Cloudflare Workers","cloudflare-workers",{"name":4306,"slug":4307,"type":15},"SDK","sdk",{"name":4309,"slug":4310,"type":15},"Serverless","serverless",{"name":4312,"slug":4313,"type":15},"WebSockets","websockets","2026-04-06T18:39:51.717063",{"slug":4316,"name":4316,"fn":4317,"description":4318,"org":4319,"tags":4320,"stars":28,"repoUrl":29,"updatedAt":4336},"ai-elements","build chat UIs with AI Elements","AI Elements component library guidance — pre-built React components for AI interfaces built on shadcn\u002Fui. Use when building chat UIs, message displays, tool call rendering, streaming responses, reasoning panels, or any AI-native interface with the AI SDK.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[4321,4324,4327,4330,4333],{"name":4322,"slug":4323,"type":15},"Frontend","frontend",{"name":4325,"slug":4326,"type":15},"React","react",{"name":4328,"slug":4329,"type":15},"shadcn\u002Fui","shadcn-ui",{"name":4331,"slug":4332,"type":15},"UI Components","ui-components",{"name":4334,"slug":4335,"type":15},"Vercel","vercel","2026-04-06T18:40:59.619419",{"slug":4338,"name":4338,"fn":4339,"description":4340,"org":4341,"tags":4342,"stars":28,"repoUrl":29,"updatedAt":4356},"ai-gateway","configure Vercel AI Gateway","Vercel AI Gateway expert guidance. Use when configuring model routing, provider failover, cost tracking, or managing multiple AI providers through a unified API.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[4343,4346,4349,4352,4355],{"name":4344,"slug":4345,"type":15},"AI Infrastructure","ai-infrastructure",{"name":4347,"slug":4348,"type":15},"Cost Optimization","cost-optimization",{"name":4350,"slug":4351,"type":15},"LLM","llm",{"name":4353,"slug":4354,"type":15},"Performance","performance",{"name":4334,"slug":4335,"type":15},"2026-04-06T18:40:44.377464",{"slug":4358,"name":4358,"fn":4359,"description":4360,"org":4361,"tags":4362,"stars":28,"repoUrl":29,"updatedAt":4368},"ai-generation-persistence","implement persistence patterns for AI generations","AI generation persistence patterns — unique IDs, addressable URLs, database storage, and cost tracking for every LLM generation",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[4363,4364,4367],{"name":4347,"slug":4348,"type":15},{"name":4365,"slug":4366,"type":15},"Database","database",{"name":4350,"slug":4351,"type":15},"2026-04-06T18:41:08.513425",600,{"items":4371,"total":4568},[4372,4393,4416,4433,4449,4466,4485,4497,4511,4525,4537,4552],{"slug":4373,"name":4373,"fn":4374,"description":4375,"org":4376,"tags":4377,"stars":4390,"repoUrl":4391,"updatedAt":4392},"prior-auth-packet-builder","build healthcare prior authorization packets","Build a concise prior authorization packet from local case files and payer policy docs.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[4378,4381,4384,4387],{"name":4379,"slug":4380,"type":15},"Documents","documents",{"name":4382,"slug":4383,"type":15},"Healthcare","healthcare",{"name":4385,"slug":4386,"type":15},"Insurance","insurance",{"name":4388,"slug":4389,"type":15},"Regulatory Compliance","regulatory-compliance",28169,"https:\u002F\u002Fgithub.com\u002Fopenai\u002Fopenai-agents-python","2026-04-16T05:11:39.180399",{"slug":4394,"name":4394,"fn":4395,"description":4396,"org":4397,"tags":4398,"stars":4413,"repoUrl":4414,"updatedAt":4415},"aspnet-core","build ASP.NET Core web applications","Build, review, refactor, or architect ASP.NET Core web applications using current official guidance for .NET web development. Use when working on Blazor Web Apps, Razor Pages, MVC, Minimal APIs, controller-based Web APIs, SignalR, gRPC, middleware, dependency injection, configuration, authentication, authorization, testing, performance, deployment, or ASP.NET Core upgrades.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[4399,4402,4404,4407,4410],{"name":4400,"slug":4401,"type":15},".NET","dotnet",{"name":4403,"slug":4394,"type":15},"ASP.NET Core",{"name":4405,"slug":4406,"type":15},"Blazor","blazor",{"name":4408,"slug":4409,"type":15},"C#","csharp",{"name":4411,"slug":4412,"type":15},"Web Development","web-development",23787,"https:\u002F\u002Fgithub.com\u002Fopenai\u002Fskills","2026-04-12T05:07:02.819491",{"slug":4417,"name":4417,"fn":4418,"description":4419,"org":4420,"tags":4421,"stars":4413,"repoUrl":4414,"updatedAt":4432},"chatgpt-apps","build ChatGPT Apps SDK applications","Build, scaffold, refactor, and troubleshoot ChatGPT Apps SDK applications that combine an MCP server and widget UI. Use when Codex needs to design tools, register UI resources, wire the MCP Apps bridge or ChatGPT compatibility APIs, apply Apps SDK metadata or CSP or domain settings, or produce a docs-aligned project scaffold. Prefer a docs-first workflow by invoking the openai-docs skill or OpenAI developer docs MCP tools before generating code.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[4422,4425,4428,4431],{"name":4423,"slug":4424,"type":15},"Apps SDK","apps-sdk",{"name":4426,"slug":4427,"type":15},"ChatGPT","chatgpt",{"name":4429,"slug":4430,"type":15},"MCP","mcp",{"name":9,"slug":8,"type":15},"2026-04-12T05:07:05.468097",{"slug":4434,"name":4434,"fn":4435,"description":4436,"org":4437,"tags":4438,"stars":4413,"repoUrl":4414,"updatedAt":4448},"cli-creator","build CLIs from API docs","Build a composable CLI for Codex from API docs, an OpenAPI spec, existing curl examples, an SDK, a web app, an admin tool, or a local script. Use when the user wants Codex to create a command-line tool that can run from any repo, expose composable read\u002Fwrite commands, return stable JSON, manage auth, and pair with a companion skill.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[4439,4442,4445],{"name":4440,"slug":4441,"type":15},"API Development","api-development",{"name":4443,"slug":4444,"type":15},"CLI","cli",{"name":4446,"slug":4447,"type":15},"Codex","codex","2026-04-12T05:07:04.132762",{"slug":4450,"name":4450,"fn":4451,"description":4452,"org":4453,"tags":4454,"stars":4413,"repoUrl":4414,"updatedAt":4465},"cloudflare-deploy","deploy projects to Cloudflare","Deploy applications and infrastructure to Cloudflare using Workers, Pages, and related platform services. Use when the user asks to deploy, host, publish, or set up a project on Cloudflare.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[4455,4458,4461,4462],{"name":4456,"slug":4457,"type":15},"Cloudflare","cloudflare",{"name":4459,"slug":4460,"type":15},"Cloudflare Pages","cloudflare-pages",{"name":4303,"slug":4304,"type":15},{"name":4463,"slug":4464,"type":15},"Deployment","deployment","2026-04-12T05:07:14.275118",{"slug":4467,"name":4467,"fn":4468,"description":4469,"org":4470,"tags":4471,"stars":4413,"repoUrl":4414,"updatedAt":4484},"define-goal","define and set measurable project goals","Help the user define a concrete, measurable goal before starting work, especially when they ask to use the goal tool, create a goal, set an objective, clarify success criteria, or turn a fuzzy intention into a quantitative outcome. Use this skill for goal creation and goal refinement only; it does not manage durable snapshots, decision logs, or long-running execution artifacts.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[4472,4475,4478,4481],{"name":4473,"slug":4474,"type":15},"Productivity","productivity",{"name":4476,"slug":4477,"type":15},"Project Management","project-management",{"name":4479,"slug":4480,"type":15},"Strategy","strategy",{"name":4482,"slug":4483,"type":15},"Task Management","task-management","2026-05-23T06:17:16.870838",{"slug":4486,"name":4486,"fn":4487,"description":4488,"org":4489,"tags":4490,"stars":4413,"repoUrl":4414,"updatedAt":4496},"figma","translate Figma designs into code","Use the Figma MCP server to fetch design context, screenshots, variables, and assets from Figma, and to translate Figma nodes into production code. Trigger when a task involves Figma URLs, node IDs, design-to-code implementation, or Figma MCP setup and troubleshooting.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[4491,4492,4494,4495],{"name":4264,"slug":4265,"type":15},{"name":4493,"slug":4486,"type":15},"Figma",{"name":4322,"slug":4323,"type":15},{"name":4429,"slug":4430,"type":15},"2026-04-12T05:06:47.939943",{"slug":4498,"name":4498,"fn":4499,"description":4500,"org":4501,"tags":4502,"stars":4413,"repoUrl":4414,"updatedAt":4510},"figma-code-connect-components","connect Figma designs to code components","Connects Figma design components to code components using Code Connect mapping tools. Use when user says \"code connect\", \"connect this component to code\", \"map this component\", \"link component to code\", \"create code connect mapping\", or wants to establish mappings between Figma designs and code implementations. For canvas writes via `use_figma`, use `figma-use`.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[4503,4504,4507,4508,4509],{"name":4264,"slug":4265,"type":15},{"name":4505,"slug":4506,"type":15},"Design System","design-system",{"name":4493,"slug":4486,"type":15},{"name":4322,"slug":4323,"type":15},{"name":4331,"slug":4332,"type":15},"2026-05-10T05:59:52.971881",{"slug":4512,"name":4512,"fn":4513,"description":4514,"org":4515,"tags":4516,"stars":4413,"repoUrl":4414,"updatedAt":4524},"figma-create-design-system-rules","generate design system rules from Figma","Generates custom design system rules for the user's codebase. Use when user says \"create design system rules\", \"generate rules for my project\", \"set up design rules\", \"customize design system guidelines\", or wants to establish project-specific conventions for Figma-to-code workflows. Requires Figma MCP server connection.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[4517,4518,4519,4522,4523],{"name":4264,"slug":4265,"type":15},{"name":4505,"slug":4506,"type":15},{"name":4520,"slug":4521,"type":15},"Documentation","documentation",{"name":4493,"slug":4486,"type":15},{"name":4322,"slug":4323,"type":15},"2026-05-16T06:07:47.821474",{"slug":4526,"name":4526,"fn":4527,"description":4528,"org":4529,"tags":4530,"stars":4413,"repoUrl":4414,"updatedAt":4536},"figma-implement-design","translate Figma designs into application code","Translates Figma designs into production-ready application code with 1:1 visual fidelity. Use when implementing UI code from Figma files, when user mentions \"implement design\", \"generate code\", \"implement component\", provides Figma URLs, or asks to build components matching Figma specs. For Figma canvas writes via `use_figma`, use `figma-use`.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[4531,4532,4533,4534,4535],{"name":4264,"slug":4265,"type":15},{"name":4493,"slug":4486,"type":15},{"name":4322,"slug":4323,"type":15},{"name":4331,"slug":4332,"type":15},{"name":4411,"slug":4412,"type":15},"2026-05-16T06:07:40.583615",{"slug":4538,"name":4538,"fn":4539,"description":4540,"org":4541,"tags":4542,"stars":4413,"repoUrl":4414,"updatedAt":4551},"hatch-pet","create animated pets for Codex","Create, repair, validate, visually QA, and package Codex-compatible animated pets and pet spritesheets from character art, generated images, company or prospect brand cues, or visual references. Use when a user wants a lightweight-worker Codex pet workflow, a non-pixel custom pet style, a prospect or company mascot pet, or a full 8x9 animated pet atlas with transparent unused cells, QA contact sheets, and pet.json packaging. This skill composes the installed $imagegen system skill for visual generation and uses bundled scripts for deterministic spritesheet assembly.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[4543,4546,4547,4550],{"name":4544,"slug":4545,"type":15},"Animation","animation",{"name":4446,"slug":4447,"type":15},{"name":4548,"slug":4549,"type":15},"Creative","creative",{"name":4264,"slug":4265,"type":15},"2026-05-02T05:31:48.48485",{"slug":4553,"name":4553,"fn":4554,"description":4555,"org":4556,"tags":4557,"stars":4413,"repoUrl":4414,"updatedAt":4567},"imagegen","generate and edit raster images","Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output should be a bitmap asset rather than repo-native code or vector. Do not use when the task is better handled by editing existing SVG\u002Fvector\u002Fcode-native assets, extending an established icon or logo system, or building the visual directly in HTML\u002FCSS\u002Fcanvas.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[4558,4559,4560,4563,4566],{"name":4548,"slug":4549,"type":15},{"name":4264,"slug":4265,"type":15},{"name":4561,"slug":4562,"type":15},"Image Generation","image-generation",{"name":4564,"slug":4565,"type":15},"Images","images",{"name":9,"slug":8,"type":15},"2026-05-15T06:23:24.312127",675]