“code graph”的搜索结果

Structure and revise investment research into readable Markdown reports for Codex. Explain business economics, competitive advantages, opposing arguments, and valuation, then connect the evidence to price bands and actions for prospective investors and existing holders.

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• Restructure a lengthy investment research report
• Explain how a company earns money and sustains its advantages
• Strengthen a report's bear case and observable risk indicators

Uses a known or suspected vulnerability to find related code through Trailmark's graph of calls, types, and implementations. Produces ranked review targets with reasons and limitations for follow-up security analysis.

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• Finding variants of a known vulnerability
• Prioritizing code that shares sensitive operations
• Preparing candidates for Semgrep or CodeQL review

Compare code structure across two commits, tags, or directories to identify security-relevant changes. Produces a report on new attack paths, privilege boundaries, complexity, and how widely a change could affect other functions.

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• Compare code structure before a release
• Investigate new attack paths after code changes
• Track untrusted data flows across a refactor

Create Mermaid diagrams from code relationships parsed by Trailmark. Visualize function calls, class inheritance, module dependencies, class members, complexity hotspots, and paths from inputs to sensitive functions, with adjustable focus, depth, and layout.

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• Mapping function calls around a selected function
• Visualizing class inheritance and members
• Drawing module dependency maps

Checks cryptographic code against known attacks and edge cases using Project Wycheproof test vectors, predefined inputs and expected results. Helps load cases, interpret failures, compare implementations, and integrate checks into automated testing.

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• Testing cryptographic code against known attacks
• Comparing results across cryptographic libraries
• Investigating rejected or incorrectly accepted signatures

Review pull requests to code-graph-rag, a multi-language code graph parser. Focuses on graph-edge correctness in language parsers, cross-language consistency, and test fixtures that actually exercise the fix.

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Implements GraphQL APIs in Golang using gqlgen or graphql-go. Apply when building GraphQL servers, designing schemas, writing resolvers, handling subscriptions, or integrating GraphQL with existing Go HTTP services. Also apply when the codebase imports `github.com/99designs/gqlgen` or `github.com/graph-gophers/graphql-go`.

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Detect GraphQL injection vulnerabilities in a codebase using a three-phase approach: recon (confirm GraphQL usage and find unsafe operation document assembly sites), batched verify (trace user input to those sites in parallel subagents, up to 3 candidate sites each), and merge (consolidate batch results). Requires sast/architecture.md (run sast-analysis first). Outputs findings to sast/graphql-results.md. If no GraphQL technology is found in Phase 1, later phases are skipped. Use when asked to find GraphQL injection, unsafe GraphQL document construction, or operation string injection bugs.

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Render videos from React/Remotion component code via inference.sh. Pass TSX code, get MP4. Supports all Remotion APIs: useCurrentFrame, useVideoConfig, spring, interpolate, AbsoluteFill, Sequence. Configurable resolution, FPS, duration, codec. Use for: programmatic video generation, animated graphics, motion design, data-driven videos, React animations to video. Triggers: remotion, render video from code, tsx to video, react video, programmatic video, remotion render, code to video, animated video, motion graphics code, react animation video

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Use code-review-graph for local-first CLI/MCP code knowledge graphs, graph-backed code review, structural search, and repository-analysis integrations.

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Reviews LangGraph code for bugs, anti-patterns, and improvements. Use when reviewing code that uses StateGraph, nodes, edges, checkpointing, or other LangGraph features. Catches common mistakes in state management, graph structure, and async patterns.

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Use when the user asks a question about how things in the codebase or wiki relate — what connects two things, how something works end to end, what depends on what, or what a concept means in this repo. Answers from the knowledge graph in graphify-out/graph.json, with source-location citations. Trigger on "/graph-query", "ask the graph", "what connects X to Y", "trace how X works", "explain <concept> from the graph", "shortest path between".

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Cold-start brownfield reconnaissance — reconstruct the WHOLE game design from an existing codebase when there is no GDD yet. Optionally accepts a free-text game description, notes, or reference links to guide and enrich the scan. Use when the user wants to build the game design from the code, e.g. "create a GDD from this codebase", "reconstruct the design from our code — it's a roguelike deck-builder", "build a design doc from the existing project, here are some refs". Scans the project (engine auto-detected), extracts a system roster + dependency graph and content-type schemas / resources / entities into one passive .unikit/gamedesign/RECON.md, tags every fact provenance:extracted-from-code, and writes a mandatory Intent Gap for what code cannot know (pillars, fantasy, the "why"). Writes nothing else and calls no skill. STRICTLY whole-project cold-start — if a GDD already exists and the user wants a specific part investigated, use /unikit-gd-explore (code lens); to author the GDD use /unikit-gd-spec.

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Professional typography rules for UI design, web applications, software interfaces, and all screen-based text. Enforces timeless typographic correctness that LLMs consistently get wrong: proper quote marks, dashes, spacing, hierarchy, and layout. ENFORCEMENT MODE: When generating ANY HTML, CSS, React, JSX, or UI code containing visible text, auto-apply every rule in this skill silently — do not ask, do not explain, just produce correct typography. AUDIT MODE: When reviewing or improving existing interfaces or legacy code, flag violations and provide fixes. Trigger on: any HTML/CSS/React artifact creation, "build a landing page", "create a component", "design a UI", "fix the typography", "make this look professional", "review this layout", web design, presentation design, dashboard creation, document generation, or any task producing visible text for humans. Even if the user doesn't mention typography, apply these rules whenever generating UI output.

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Semantic search, grep, and Q&A across codebases and documentation indexed in CodeAlive. Use when the user mentions "CodeAlive", asks to list or get data sources, list indexed repositories, search code or docs across remote repos, fetch artifact content, or trace call graphs across repositories.

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GraphDB構築エージェント - ユビキタス言語とコード解析結果からRyuGraphデータベースを構築。/build-graph [対象パス] で呼び出し。

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GraphDB探索エージェント - RyuGraphデータベースを自然言語またはCypherで探索し、関連するコードや仕様書を返却。/query-graph [クエリ] で呼び出し。

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GraphDB可視化エージェント - RyuGraphデータベースの内容をMermaid/DOT/HTML形式で可視化。/visualize-graph [出力パス] で呼び出し。

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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. Uses Codex CLI as the search driver, but Codex calls a bundled deterministic PubMed tool (scripts/pubmed_search.py) instead of writing its own E-utilities code — bibliography is fetched live from PubMed so PMIDs are never fabricated. 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", "refs.csv", "codex-refs", "/codex-refs".

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Collect design tokens—reusable values for colors, typography, spacing, corners, and shadows—from source code, screenshots, or Figma exports. Organize them in a JSON file with source and usage references for later mapping to a design system.

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• Collecting design values scattered across source files
• Preparing Figma design data for migration
• Extracting design settings from screenshots

Analyze project code, configuration, and documentation to build a graph for interactive architecture exploration. It maps components and dependencies, supports Japanese descriptions, and can update the analysis based on changes since the previous analyzed commit.

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• Understand the structure of an unfamiliar project
• Explore dependencies between components
• Generate architecture descriptions in Japanese

Extract classes, functions, modules, and their relationships from source code or documentation into AgentDB. Build a knowledge graph, a map of connected entities, to understand code structure, dependencies, and potential change impacts.

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• Understanding a codebase's structure
• Investigating dependencies between modules
• Assessing the impact of code changes

Set up human approval for Claude Code actions such as PR reviews, merges, and CI changes. Configure Cedar authorization rules and signed records of allowed actions that can be checked offline with a public key.

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• Requiring approval before PR reviews and merges
• Gating issue actions and release publishing
• Requiring approval for CI changes and protected branch pushes

Turn documents, notes, research, or code into a knowledge graph that maps identifiable things and their relationships. Create or update entity files and typed connections, with source evidence attached for inspection and later visualization.

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• Map people and organizations across documents
• Organize research with traceable evidence
• Map relationships between software modules