“call graph”的搜索结果

Apply IRQL graph functions to KQL or IRQL query results for Kusto Explorer visualization. Generates Lift_To_Graph mappings and composes Graph_Render_View, Graph_Fold_By_Property, Extract_Node_*, Enrich_Node_*, and Enrich_Graph_* calls. Accepts a supplied query or limited basic natural-language source request; it is not a general natural-language-to-KQL/IRQL skill. WHEN: Lift_To_Graph, Graph_Render_View, Graph_Fold_By_Property, IRQL graph enrichment, graph mapping for existing query results, icon-decorated graph, fold graph nodes. Use azure-kusto-graph for native make-graph analysis, graph-match, shortest paths, components, or persistent graphs.

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Apply IRQL graph functions to KQL or IRQL query results for Kusto Explorer visualization. Generates Lift_To_Graph mappings and composes Graph_Render_View, Graph_Fold_By_Property, Extract_Node_*, Enrich_Node_*, and Enrich_Graph_* calls. Accepts a supplied query or limited basic natural-language source request; it is not a general natural-language-to-KQL/IRQL skill. WHEN: Lift_To_Graph, Graph_Render_View, Graph_Fold_By_Property, IRQL graph enrichment, graph mapping for existing query results, icon-decorated graph, fold graph nodes. Use azure-kusto-graph for native make-graph analysis, graph-match, shortest paths, components, or persistent graphs.

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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

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

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

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

Design GraphQL APIs with clear data types, data-fetching functions called resolvers, and real-time updates. Address repeated database calls, pagination, structured errors, and expensive queries while planning schema changes that preserve compatibility.

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• Designing schemas around business concepts
• Reducing repeated database calls with DataLoader
• Planning real-time subscriptions over WebSocket

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

Integrate ManagedCode.Orleans.Graph into an Orleans-based .NET application for grain-call policy enforcement, deadlock detection, live-call telemetry, and Mermaid graph diagnostics. USE FOR: ManagedCode.Orleans.Graph integration; allowed grain transitions; Orleans call filters; live policy graphs; reviewing Orleans call-cycle risk. DO NOT USE FOR: generic graph data modeling or traversal unrelated to Orleans calls. INVOKES: inspect the repository context, edit targeted files, and run relevant build, test, lint, or validation commands when changes are made.

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Run Neo4j Graph Analytics algorithms (PageRank, Louvain, WCC, Dijkstra, KNN, Node2Vec, FastRP, GraphSAGE) directly inside Snowflake without moving data. Use when running graph algorithms against Snowflake tables via the Neo4j Snowflake Native App ("GDS Snowflake", "graph algorithms in Snowflake", "Neo4j Graph Analytics"). Covers the explore → prepare projection views → project-compute-write flow, the strict view/column type rules the graph engine requires, exact SQL CALL syntax, and privilege setup in both modes — app-identity grants and execute-as-user / per-user PAT auth (programmatic access token, set_enable_custom_credentials, register_user_role, caller grants). Does NOT cover Cypher or Neo4j DBMS queries — use neo4j-cypher-skill. Does NOT cover Aura Graph Analytics — use neo4j-aura-graph-analytics-skill. Does NOT cover self-managed GDS — use neo4j-gds-skill.

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Query an indexed code graph, a database of code relationships, to find callers, dependencies, model associations, and call paths. Use the results to understand which components may need attention before changing a function or model.

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• Find callers before changing a function
• Find serializers affected by model changes
• Trace call paths between functions

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

Work through unresolved review comments across dependent PRs managed with Graphite. Review branches from the base upward, propose fixes or replies for approval, test changes, and commit each branch locally while preserving stack dependencies.

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• Review unresolved comments across a Graphite stack
• Fix review findings with regression tests
• Explain findings already addressed in a later PR

Assess API business workflows for flaws that automated scanners can miss. The skill examines skipped steps, simultaneous transactions, authentication tokens, GraphQL, and webhooks, helping testers document weaknesses and their financial or operational impact during authorized assessments.

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• Reviewing order and payment workflow integrity
• Testing concurrent coupon redemption or transfers
• Assessing JWT validation and GraphQL controls

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

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Fetch execution traces, records of an agent’s actions, from LangSmith Studio to debug LangChain and LangGraph agents. Inspect errors, tool calls, memory operations, execution times, and token usage, or export records for further review.

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• Investigating an agent failure
• Checking tool selection and execution order
• Investigating memory storage and recall

Fetch execution traces, records of an agent’s actions, from LangSmith Studio to debug LangChain and LangGraph agents. Inspect errors, tool calls, memory operations, execution times, and token usage, or export records for further review.

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• Investigating an agent failure
• Checking tool selection and execution order
• Investigating memory storage and recall

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

Design AI applications with LangChain 1.x and LangGraph, libraries for coordinating language models and workflows. Build agents that use external tools, manage conversation history and saved state, process documents, and support tracing and testing.

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• Build an AI agent that calls external tools
• Manage conversation history and saved execution state
• Create document processing and retrieval workflows

Integrate Microsoft Graph SDK into .NET, TypeScript/JavaScript, or Python applications to access Microsoft 365 data. Covers authentication, permissions, paginated results, incremental synchronization, change notifications, and retries when requests hit service limits.

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• Building apps that access Microsoft 365 mail and calendars
• Integrating Teams, OneDrive, and SharePoint data
• Synchronizing only data changed since the previous run

Query the local AST-derived code graph (functions, classes, calls, imports) for structural codebase questions — what calls X, what does Y import, where is Z defined, blast radius of a change. The graph rebuilds automatically after each agent turn; use hivemind_graph_search and hivemind_graph_neighborhood tools (no manual build step).

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Use CALL-E from Codex through the calle CLI. Use for CALL-E setup checks, authentication recovery, phone call planning, planned call execution, and call status checks.

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Build and analyze pangenomes for prokaryotes (Panaroo, PPanGGOLiN, PEPPAN, GET_HOMOLOGUES, anvi'o pangenomics) and eukaryotes (Minigraph-Cactus, PGGB, vg pangenome graphs). Implement Tettelin core/accessory/cloud genome decomposition (Tettelin 2005), Heap's law open/closed pangenome modeling, gene presence/absence GWAS (Scoary, pyseer), pangenome graph variant calling (vg, PanGenie), and structural-variation graph indexing. Use when assembling species- or genus-level pan-gene catalogs, separating core from accessory/shell/cloud genes, testing gene-content associations with phenotypes, building pangenome graphs from haplotype-resolved assemblies, calling SVs from pangenome graphs, or selecting between bacterial-pangenome and eukaryotic-pangenome workflows.

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