'code graph' 검색 결과

Add support for a programming language to codegraph, from connecting its parser and implementing code extraction to writing tests. Evaluate extraction quality and compare agent effort with and without codegraph across three real codebases.

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• Adding a new programming language to codegraph
• Testing extraction of functions, imports, and calls
• Comparing agent effort across three real codebases

Benchmark how CodeGraph affects an agent investigating a real codebase. Choose a version, language, repository, and execution mode, then compare cost, tool calls, file reads, and answer correctness with and without CodeGraph.

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• Evaluating a local development build
• Auditing a published CodeGraph version
• Comparing investigation cost and file reads

[omh] Outdated code index or codemap: refresh local code intelligence, summarize repo structure, and prepare task-scoped codegraph handoff context without overclaiming execution. Use when the user says: codegraph-refresh, codegraph refresh, refresh codegraph, update codegraph, codegraph stale, stale codegraph, codegraph handoff, codegraph summary.

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Connect static analysis results and human audit notes to a Trailmark code graph. Group findings by severity and compare them with input flow, impact, and privilege boundaries to prioritize investigation and understand findings in their code context.

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• Connecting Semgrep or CodeQL results to code structure
• Combining human audit notes with automated findings
• Prioritizing findings using input flow and impact

Explore code structure and history using an index of code relationships and meaning-based search. Trace calls, assess change impact, find likely bug locations, generate architecture reports, and review pull requests for risk and conflicts.

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• Tracing function calls and change impact
• Investigating code locations related to a GitHub issue
• Generating architecture reports and class diagrams

Build or refresh a knowledge graph, a map of code relationships used for code review. Choose a full rebuild or an incremental update, then report the operation's status and summary.

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• Setting up the graph for a repository's first review
• Refreshing the graph after switching branches
• Updating review data after a large refactor

Explore relationships between functions and files using a prebuilt code graph, a map of code dependencies. Trace bugs, inspect callers and execution paths, and assess the impact of refactoring or proposed changes.

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• Explore callers and dependencies in unfamiliar code
• Trace a bug across multiple files
• Assess the impact of renaming or removing code

Analyze git history to map contributors to security-sensitive files, identify reliance on too few people, and find stale areas. Export CSV and JSON results for ownership reviews, graph exploration, and comparison with CODEOWNERS.

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• Find stale security-sensitive code with few maintainers
• Check reliance on individual authentication or cryptography maintainers
• Compare CODEOWNERS with actual contribution history

GraphQL gives clients exactly the data they need - no more, no less. One endpoint, typed schema, introspection. But the flexibility that makes it powerful also makes it dangerous. Without proper controls, clients can craft queries that bring down your server. This skill covers schema design, resolvers, DataLoader for N+1 prevention, federation for microservices, and client integration with Apollo/urql. Key insight: GraphQL is a contract. The schema is the API documentation. Design it carefully. 2025 lesson: GraphQL isn't always the answer. For simple CRUD, REST is simpler. For high-performance public APIs, REST with caching wins. Use GraphQL when you have complex data relationships and diverse client needs. Use when "graphql, graphql schema, graphql resolver, apollo server, apollo client, graphql federation, dataloader, graphql codegen, graphql query, graphql mutation, graphql, api, apollo, schema, resolvers, dataloader, federation, typescript" mentioned.

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Analyze git history to map contributors to security-sensitive files, identify reliance on too few people, and find stale areas. Export CSV and JSON results for ownership reviews, graph exploration, and comparison with CODEOWNERS.

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• Find stale security-sensitive code with few maintainers
• Check reliance on individual authentication or cryptography maintainers
• Compare CODEOWNERS with actual contribution history

Automate Contentful Graphql operations through Rube MCP, a connection for agent tools. The skill discovers available actions, checks the service connection, and follows current input requirements to execute the requested workflow.

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• Finding available Contentful Graphql actions
• Running tasks after checking the connection
• Retrieving results across multiple pages

Analyze git history to map contributors to security-sensitive files and identify ownership gaps. Estimate bus factor, a measure of dependence on few people, group files that change together, and export CSV or JSON for further analysis.

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• Finding sensitive code with stale, concentrated ownership
• Checking reliance on one person for authentication code
• Comparing CODEOWNERS with contribution history

Codebase'i knowledge graph olarak analiz et. Dependency, call graph, hotspot analizi.

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Analyze indexed codebases via graph database (neug) and vector index (zvec). Covers call graphs, dependencies, dead code, hotspots, module coupling, architecture reports, semantic search, impact analysis, bug root cause from GitHub issues, class diagrams (UML), and PR review (risk scoring, conflict detection, auto-merge candidates, labeling). Also covers creating, inspecting, and repairing a CodeScope index. Use for: code structure, who calls what, why something changed, similar functions, module boundaries, bug tracing, class relationships, PR risk/conflicts, or any question benefiting from a code knowledge graph. Applies when a `.codegraph` index exists in the workspace, or when the user wants to create one.

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Diagnose REST and GraphQL API failures by checking connectivity, authentication, request formats, and returned data. Prepare reproducible commands, identify likely causes, and build regression tests that check whether a fix keeps working.

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• Investigating API authentication failures
• Reproducing requests that work in Postman but fail in code
• Checking pagination and rate limits

Explore an unfamiliar codebase using a graph of code relationships. Inspect its structure, find functions and classes, trace calls and imports, follow execution paths, and locate large functions for closer investigation.

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• Understanding the structure of an unfamiliar codebase
• Finding functions and classes by name or keyword
• Tracing calls, imports, and execution paths

Style materials to match the OpenAI and Codex look using specified colors and fonts. Apply distinct formatting to headings, body text, code, and shapes, with fallback fonts when the preferred ones are unavailable.

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• Match material colors to the OpenAI and Codex look
• Apply consistent fonts to headings and body text
• Style code snippets with a monospace font

Build a graph of functions and calls across multiple programming languages to investigate entry points, paths to sensitive operations, and potential impact. Use the results to prioritize security reviews and connect external findings with source code.

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• Tracing call paths from entry points to sensitive functions
• Prioritizing code for a security audit
• Connecting calls across language and RPC boundaries

Use when the user wants to build, refresh, or visualize the repo's knowledge graph — code structure plus the committed wiki/ — via graphify. Produces an interactive graph + a GRAPH_REPORT.md of God Nodes, surprising connections, and suggested questions. Trigger on "/graph", "build the knowledge graph", "graph the repo", "map the codebase", "refresh the graph", "visualize the second brain".

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Use this skill when the user wants to turn code graph analysis into tracked GitHub issues — "audit the architecture", "find god nodes", "triage architectural debt", "run graphify and log issues", "what's over-coupled in this codebase", "create issues from graphify findings", "check for structural problems", "scan for architectural smells", or any request that combines codebase topology analysis with issue tracking. This skill runs graphify to build a dependency graph, parses GRAPH_REPORT.md for god nodes and structural patterns, converts findings into GitHub issue payloads, and optionally pushes them to the repo. It bridges static analysis and project management in a single automated pass. Requires graphify and gh CLI.

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Extract business areas, workflows, and process steps from a codebase and display them as an interactive horizontal flow graph. Use an existing knowledge graph, a map of project components, or run a lightweight scan of project files.

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• Understanding business workflows from code
• Mapping business areas and process steps
• Creating a business view from an existing knowledge graph

Set up rules that allow or block Claude Code tool calls, and record executed tool names in digitally signed receipts. The skill also explains offline verification and the limits of what those records prove.

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• Restricting which Claude Code tools may run
• Recording tool names in digitally signed receipts
• Verifying receipts offline with a public key

Design REST and GraphQL APIs by clarifying who will use them and how. Define data structures, errors, authentication, pagination, and versioning, then review examples to make the interface consistent and easier to use.

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• Designing a new REST or GraphQL API
• Reviewing API specifications before implementation
• Defining shared API design standards for a team

Build AI agents with LangGraph by connecting processing steps into a graph. Define shared state, conditional paths, tool calls, saved checkpoints, and human review points to create workflows that retain information as they run.

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• Building an agent that calls tools in a loop
• Sharing accumulated findings between multiple agents
• Routing requests to different processing paths