1.1천건 · 관련도순
Use when needing global or relational understanding of codebases or knowledge corpora. Keywords: GraphRAG, knowledge graph, entity extraction, community summarization, graph traversal, Microsoft GraphRAG.
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Generate proper ENCODE citations for publications, grants, and presentations. Use when the user needs to cite ENCODE data, create bibliography entries, write acknowledgment sections, or ensure compliance with ENCODE data use policy.
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Use for any question about a codebase, its architecture, file relationships, or project content — especially when graphify-out/ exists, where the question should be treated as a graphify query first. Turns any input (code, docs, papers, images, videos) into a persistent knowledge graph with god nodes, community detection, and query/path/explain tools.
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Search academic references for a manuscript and export them to a verified refs CSV, then optionally hand off to cec-sheet. Claims are decomposed into conditions that define when to stop; references are found by keyword search (Mode A targeted / Mode B agentic via Codex CLI, run as two independent search routes) and by citation chaining (Mode C — backward refs, forward citations, similar articles, same author, and reference lists of OA full text/PDF, imozuru-style). All paths end in bundled deterministic tools (scripts/pubmed_search.py, scripts/citation_chain.py) that fetch bibliography live from PubMed, so PMIDs are never fabricated, and each supporting sentence is machine-verified verbatim against the fetched abstract. Triggers: "論文検索", "文献検索", "文脈検索", "コンテキスト検索", "原稿の引用を探して", "原典を探して", "芋づる", "引用をたどって", "被引用", "孫引き", "refs.csv", "codex-refs", "/codex-refs".
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Use when you need code research over an indexed codebase with minimal context: structural graph queries (who calls X, call chains, architecture overview, dead code, cross-service HTTP routes) instead of file-by-file grep/read cycles. Also trigger to minimise token spend on repetitive code exploration.
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Use for any question about a codebase, its architecture, file relationships, or project content — especially when graphify-out/ exists, where the question should be treated as a graphify query first. Turns any input (code, docs, papers, images, videos) into a persistent knowledge graph with god nodes, community detection, and query/path/explain tools.
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기존 지식 그래프와 실제 코드를 대조해 특정 파일, 함수, 모듈의 설계상 역할과 호출 관계, 의존성, 입출력 흐름을 자세히 설명합니다.
지식 그래프의 특정 항목에서 시작해 의존 관계와 간접 연결을 탐색합니다. 관련성이 높은 경로를 순위로 보여 주어 코드 구조와 변경 영향 범위를 파악하도록 돕습니다.
Turn API code into documentation covering available operations, inputs, outputs, authentication, and errors. Add practical request examples and usage guides, with OpenAPI specifications or Postman collections where possible to help developers explore and use the API.
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Create and edit draw.io files for workflows, system architecture, database relationships, and service interactions. Organize shapes and connectors, apply consistent styling, and check file structure so diagrams can be opened and edited in draw.io.
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Turn API code into documentation covering endpoints, inputs, authentication, responses, and errors. Add request examples in multiple languages and usage guides to help developers understand and start using the API.
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Review all changes in a pull request or branch against its base branch. Use a knowledge graph, a map of code relationships, to report affected files and functions, test coverage, API compatibility issues, and recommended actions.
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Plan refactoring with dependency analysis, which shows how code parts rely on each other. Find candidates and unreferenced code, preview and apply renames, and compare the resulting impact with the original plan.
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Turn Figma screens and components into application code using design specifications, screenshots, and supplied assets. Reuse existing project components and shared style values, then check layout, typography, responsive behavior, and accessibility against the design.
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Create editable draw.io diagrams from descriptions, code, infrastructure settings, or database schemas. Update existing diagrams while preserving manual layout, check their structure and architecture rules, and export them to image or document formats for sharing.
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Understand software architecture, trace request flows, and assess the impact of code changes using Ontoly’s graph of software relationships. Answers include supporting graph evidence, confidence, and relevant limitations.
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Evaluate how well existing tests catch code changes, then combine the results with function relationships. Get a report separating harmless findings, missing unit tests, and targets for fuzzing, which tests many generated inputs.
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Write Jest unit tests for component behavior and user interactions. Includes reusable test data builders, mock replacements for dependencies, and test-driven development, where a failing test guides implementation before the code is cleaned up.
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Turns collected repository statistics into an eight-panel PNG infographic. It shows pull request outcomes, timing, code changes, and human versus agent activity, with scripts to recalculate figures and check them against available source records.
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Guide bug bounty work from checking authorized scope and discovering assets to testing web and AI features. Validate practical impact, investigate related weaknesses, and organize reproducible evidence into a vulnerability report.
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Create resumes, reports, papers, and other documents with LaTeX, a document typesetting system, then produce PDFs and page previews. Also convert document formats and handle PDF tasks such as merging files and filling forms.
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Assess how a pull request affects documentation, classify the required updates, and coordinate writing, technical verification, and editorial review. The result is an advisory comment with proposed changes for affected pages or document structure.
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Record a decision, document existing code, or file a supplied research material. Modes: document decision (ADR, RFC, or rule), document code (spec, doc, guide, or scenario for existing behavior), document research (only when a finished report or one external material is already in hand). Use for 'we decided', 'record this decision', 'document why we chose X', 'make it our standard', 'draft an RFC', 'should we switch to Y' proposals, 'resolve the RFC', 'we accepted the proposal', 'document the auth module', 'capture how the payment system works', reference material, how-to instructions, or a user flow with examples over an existing spec. Name the type inside the subject to skip the type question. Planning a feature or an intended user journey → /archcore:plan. Checking docs against code or docs health → /archcore:review.
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Edit, trim, cut, caption, subtitle, reframe, combine, add background music to, or export video, audio, and image files through Cassette. Use this skill whenever the user asks to change, preview, or render a media file in the project — even if they never say "Cassette" or name a tool — for example "trim the intro off demo.mp4", "add subtitles to this clip", "make me a 30-second cut with music", "why is there dead air at the start". Drives the local Oh My Cassette stdio MCP tools in Codex or Claude as one multi-turn conversation with the Cassette agent, with per-turn timeline previews, guided questions, and rendering only on explicit export.
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