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Analyze relationships between people, objects, or papers as networks in Python. Use NetworkX to find shortest paths, identify important nodes and communities, visualize connections, exchange graph data, and generate sample networks for testing.
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Typst academic paper assistant for existing `.typ` paper projects in English or Chinese. Use this skill whenever the user wants to compile, audit, or improve a Typst paper, including format checks, bibliography validation for BibTeX or Hayagriva, grammar/sentence/logic review, expression polishing, translation, title optimization, pseudocode review, algorithm block cleanup, de-AI editing, experiment-section review, table structure validation, three-line table generation, abstract structure diagnosis, or journal adaptation. Trigger even when the user only mentions one Typst file, one bibliography issue, one pseudocode block, one section rewrite, "three-line table", "check abstract", or "reformat for another journal". Also trigger when the user mentions ".typ files", "typst compile error", "typst export", "typst bibliography", `algorithm-figure`, `lovelace`, or `algorithmic` even without saying the word "Typst" explicitly.
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Systematically reviews code for SQL injection, XSS, SSRF, broken access control, cryptographic failures, and other common OWASP Top 10 vulnerabilities, providing vulnerable code examples and ready-to-use remediation guidance. Trigger this skill when users ask for a security review, vulnerability scan, or penetration testing assistance, or mention keywords like OWASP, SQL injection, XSS, code audit, or security checklist.
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Use whenever the agent needs to add, create, hand-author, patch, or place Motion Graphic JSX assets in a OpenChatCut project. This is the direct-authoring path: use create_motion_graphic_from_code / edit_asset / edit_item, not motion-graphic-gen or submit_motion_graphic. Covers project/timeline intake, project visual language, editable properties, asset binding, inline JSX authoring, existing asset updates, timeline placement, and verification.
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Query the local code graph (functions, classes, calls, imports) through the Deeplake mount at memory/graph/. Use when the user asks structural questions about the codebase — "what calls X?", "what does Y import?", "where is Z defined?", "what's the architecture / which subsystems exist?", "what's the impact of changing this?". The graph is an AST-derived map of the repo, queried as files (no build needed — it rebuilds automatically).
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Query the local code graph (functions, classes, calls, imports) through the Deeplake mount at memory/graph/. Use when the user asks structural questions about the codebase — "what calls X?", "what does Y import?", "where is Z defined?", "what is the architecture / which subsystems exist?". The graph is an AST-derived map of the repo, queried as files (no build needed — it rebuilds automatically).
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将 CodeGraph 作为默认内置 MCP-backed 代码图谱能力接入，用于 brownfield 符号搜索、 调用链、影响面和上下文查询。输出必须回落到 `/team-*` 主链和 artifacts。
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PROACTIVELY query the code graph database to understand relationships and impact of changes. Use this skill WHEN READING any file to understand context, when searching for files, when exploring the codebase, or when you need to understand what depends on a component. This is your primary tool for understanding code structure and avoiding breaking changes.
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Decision protocol for building, debugging, and operating LangGraph-based agent systems. Activates when a coder agent is asked to design a stateful LLM workflow, add human-in-the-loop, choose a multi-agent pattern (supervisor / swarm / hierarchical), pick a checkpoint backend, or migrate a fragile chain into a durable graph. LangGraph is positioned by its maintainers as a "low-level orchestration framework for building, managing, and deploying long-running, stateful agents" — this skill encodes the *when* and *why*, not the API.
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Query patent bibliographic (catalog) information from the Nexscope patent database by patent ID or publication number. Triggered when users mention patent bibliographic info queries, patent catalog information, patent applicant queries, patent inventor queries, patent classification codes, patent abstract retrieval, patent citation analysis, patent priority claims, patent application citations, patent examiner information, patent bibliographic data, inventor lookup, applicant lookup, patent classification, patent metadata, Nexscope, or patent citations. Even if the user does not explicitly mention "bibliographic info," this skill should be triggered whenever their need involves querying detailed metadata for a specific patent by patent ID or publication number.
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Audit existing typography in a codebase — find inconsistencies, hardcoded sizes, and hierarchy gaps. Use when asked to "audit our typography", "find hardcoded font sizes", or "our type is inconsistent".
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Deterministic Software Graph analysis via Ontoly CLI and MCP. Use when: (1) Codebase architecture, dependency, route, service, module, configuration, or impact questions need graph evidence, (2) A repository should be analyzed before source search, (3) You need a persistent SoftwareGraph.json for validation, MCP, or agent workflows. Triggers: "build software graph", "trace route", "impact analysis", "architecture summary", "dependency graph", "what uses this", "Ontoly".
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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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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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Get a detailed explanation of a file, function, or module using an existing knowledge graph and its source code. It explains architectural roles, dependencies, callers, and data flow for readers who may not know the programming language.
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Explore a knowledge graph, a network of entities and relationships, from a chosen starting point. Rank relevant paths and summarize dependencies or indirect connections to help understand code structure and assess the impact of changes.
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Coordinate specialized AI agents under a lead coordinator for research, development, and review. Configure voting rules, share persistent knowledge, assign tasks, monitor progress, and pause or resume sessions through Claude Flow.
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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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