66건 · 관련도순
Query the ChEMBL API for molecules, biological targets, activity records, and mechanisms of action. Search by text or look up an identifier, then receive concise summaries or JSON output when machine-readable results are needed.
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Retrieve public eQTL Catalogue association data, linking genes and genetic variants, along with study metadata. Filter requests to specific identifiers and receive concise Markdown summaries or raw JSON when machine-readable output is requested.
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Query gnomAD for variant frequencies, gene constraint measures, and effects on gene transcripts. Get concise Markdown summaries from focused requests, with options to return machine-readable JSON or save the full response to a file.
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Retrieve public collection and dataset metadata from CELLxGENE Discover and turn it into concise summaries. Use targeted collection lookups to review single-cell research data, with raw JSON available when machine-readable output is needed.
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Improve how search engines discover and understand a website by reviewing titles, descriptions, links, and sitemaps. Add structured data, machine-readable descriptions of page content, and use checklists to identify technical and on-page SEO issues.
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Restructure existing web pages so AI assistants can extract and cite answers accurately. Get rewritten sections, comparison tables, FAQs, and JSON-LD structured data, a machine-readable description of page content, while preserving readability.
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Write and debug Python regressions with PyFixest using a reference for model formulas, estimation functions, and standard errors. It also covers extracting results, comparing models, and producing regression tables and plots, including models that account for group differences.
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Turn any codebase into evidence-grounded Markdown docs plus a machine-readable index.json. Every claim cites its source; never invents deployment steps.
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Verify a CCB execution round and produce a machine-readable round result artifact.
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Verify integrated round evidence and return a machine-readable round result for script import.
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Land a change everywhere the same fact is stated — enumerating the full surface inventory (landing copy, docs, machine-readable summaries, changelog badges repeated across every page, sitemap, README, descriptions embedded in code, and untyped frontend consumers of typed responses), generating a surface instead of restating it, drift tests where you cannot generate, never hand-maintaining a copy of a surface another codebase owns, shipping a paired PR when you change a format a different codebase decodes, and keeping overloaded product words apart. Use when adding or renaming a user-facing feature, editing product/marketing copy or docs, renaming a serialized response field, changing a serialization or share-link format, wiring one repo's output into another's checks, or reviewing a PR that touched only one place a fact appears.
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Extract anonymized frame-by-frame body and hand trajectories from an authorized reference video, with skeleton previews, confidence evidence, and machine-readable keypoints. Use when the user asks to 提取博主动作, 提取人体动作轨迹, 做姿态参考, 生成骨架视频, or prepare motion control data for an avatar; do not route graphic-animation replication here.
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Pulls Bigdata.com (RavenPack) financial and news data via the official bigdata-client SDK and /v1/* REST endpoints — structured financials, prices, analyst estimates, entity-sentiment series, annotated chunk search, screener. Use when the Bigdata MCP's tearsheets/search feel thin and you need the machine-readable substrate: mentions of Bigdata.com, RavenPack, a bd_v2_ key, rp_entity_id, or chunk/query_unit cost.
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Retrieve machine-readable context with tanstack libraries, tanstack doc, tanstack search-docs, tanstack create --list-add-ons --json, and --addon-details for agent-safe discovery and preflight validation.
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Inspect a mathematical-modeling workspace, evaluate lean or submission gates per subquestion, update machine-readable manifests, classify change impact, and route one next action without duplicating downstream work.
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Assesses which of the Art. 50(1)-(5) transparency obligations of the EU AI Act apply to a given AI system's provider or deployer, grounded in the final Code of Practice on Transparency of AI-Generated Content (June 2026) and the Commission's draft Art. 50 Guidelines. Covers AI-chatbot disclosure, deepfake and synthetic-content marking/watermarking, emotion-recognition and biometric-categorisation notices, the machine-readable marking duty, the obviousness exceptions, and the implementation timeline. Outputs a formal mini-report plus a per-obligation compliance checklist with gap flags. For breadth-first tier triage use the EU AI Act System Classifier; for raw Art. 50 text and Q&A use the EU AI Act Knowledge Base; for the full role x tier matrix use the EU AI Act Obligations Mapper.
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Before compaction Loopkit extracts decisions into claude-decisions.json (machine-readable). Read it alongside claude-progress.txt at session start — prose is for humans, JSON is for the loop.
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Build a final, machine-readable argument snapshot from current H3 sections and refresh section fingerprints before merge.
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Internal template for creating or refactoring a skill into the repository's reference-first shape. **Trigger**: reference-first template, blueprint skill, create a reusable skill, refactor a script-heavy skill. **Use when**: you need a lean `SKILL.md`, explicit `references/`, machine-readable `assets/`, and a minimal deterministic `run.py`.
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Implement the Kantara Initiative consent receipt specification including machine-readable receipt structure, JWT-based verification mechanisms, receipt lifecycle management, and integration patterns for consent management platforms. Supports ISO/IEC 27560 consent record information structure.
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Structured diagnostic code library for JavaScript/TypeScript. Turns errors and other conditions into typed, machine-readable `Diagnostic` instances with stable codes, docs URLs, and actionable fields. Use this skill whenever the project imports `nostics`, or works with `defineDiagnostics`/`defineProdDiagnostics`, the `Diagnostic` class, diagnostic code registries, or structured error handling. Also covers reporters (`createConsoleReporter`, `createFetchReporter` from nostics/reporters/fetch, `createFileReporter` from nostics/reporters/node, `createDevReporter` from nostics/reporters/dev), formatters (`formatDiagnostic`, `ansiFormatter`, `jsonFormatter`), and Vite plugins (`nosticsStrip` from @nostics/unplugin/strip-transform, `nosticsCollector` from @nostics/unplugin/dev-server-collector). Also use when migrating a library's existing `console.warn`/`console.error`/`warn()` helpers or thrown `Error`s to diagnostics: follow `references/migration.md`.
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Aggregate a retort run's findings.jsonl into a machine-readable assessment.json summary with severity counts, penalty score, requirement coverage, and top findings.
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Evaluate a single retort experiment run. Score the generated code against the task's TASK.md requirements, run its build and tests, compute metrics, and emit a structured evaluation report plus a machine-readable findings file.
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Parse and extract structured content from PDFs, Word documents, and other file formats. Converts documents into clean, machine-readable text for analysis, summarization, data extraction, and content processing workflows.
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