'call graph' 검색 결과

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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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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Add timed graphic cards to an existing interview, podcast, or talking-head video. Turn spoken points into titles, quotes, and data callouts, then render an MP4 in a landscape or portrait format while the original clip plays throughout.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Highlight key points in an interview
• Add data callouts to an explainer video
• Package a podcast video for a portrait canvas

Distribute edits across the appropriate branches in a Graphite stack, a chain of dependent branches. Use manual grouping when automatic absorption misplaces changes, then update commits, resolve restack conflicts, and verify the affected branches locally.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Splitting working changes across stacked branches
• Moving a fix to the branch that owns the feature
• Assigning new files that automatic absorption skips

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

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

Find API patterns and examples for coding agents with ADK, the Agent Development Kit. Covers tools, state, callbacks, multi-agent coordination, and graph workflows, with Python and Go references plus guidance on studying existing implementations.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adding tools and callbacks to an ADK agent
• Implementing state management and agent coordination
• Designing branching and parallel graph workflows

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).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Build, refresh and query a deterministic code knowledge graph to cut orientation-token cost. Triggers: knowledge graph, graphify, code graph, god nodes, orientation cost, map the codebase, what connects, callers of, blast radius.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Decision protocol for making side-effectful agent tools idempotent — so when an LLM tool call is retried (timeout, framework resume, user re-run, model duplicate emit), the second call is a no-op instead of a double-send. The load-bearing premise: the LM cannot promise it'll call exactly once; the tool must promise the second call is safe. Framework-agnostic — applies to LangGraph node bodies that re-run on resume, MCP tools, OpenAI tool-calling retries, CrewAI delegated tool invocations, and direct HTTP wrappers. Search keywords: duplicate email sent, charged twice, exactly-once, idempotency key, tool called twice, retry side effect, double-send, at-least-once delivery.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Provider-independent motion graphics direction for AI agents producing title sequences, lower thirds, explainer graphics, product UI overlays, social ads, kinetic typography, diagrams, data callouts, brand films, and video post. Use when planning, prompting, storyboarding, specifying, implementing, reviewing, or QAing motion graphics across live footage, generated video, UI capture, data visualization, brand systems, typography, accessibility, claim integrity, runtime handoff, and iteration.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Audit a dataset or access rule before it joins an identity-graph build (access rules behave like datasets in NQL here). Enumerates failure modes (hub identifiers, high-degree nodes, suspicious values, over-connected identifiers), tests hypotheses in parallel, quantifies damage by rows / edges / entities, and proposes minimal filters ranked by severity. When issues are found, returns a validated `CREATE MATERIALIZED VIEW` NQL the caller can run to produce a graph-ready clean source; if the data passes, says so and recommends it unchanged. Plans and authors the clean-view NQL; does not execute it. Use when: "audit this dataset before the graph build", "find bad edges in <source>", "check identity data quality", "recommend filters for the graph build", "quantify damage from <identifier_type>", "pre-graph DQ". (narrative-identity)

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Produce short motion graphics for animated text, numbers, charts, logos, and social overlays. Guides asset sourcing, shot design, composition, rendering, and validation, with output as an MP4 or a transparent video overlay.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Animate headlines and short text
• Show key numbers and charts in motion
• Create a short logo reveal

Create short animated graphics for titles, statistics, charts, logos, maps, or web content. The workflow sources assets when needed, designs and checks the animation, then exports an MP4 or transparent overlay after preview approval.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Animating a short title or quote
• Showing a statistic with a count-up or chart
• Creating a logo reveal

Design, build, debug, and optimize connections to external APIs. The skill covers REST, GraphQL, event notifications through webhooks, authentication, retries, request limits, and logging to help produce integrations whose failures can be traced.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing a new external API integration
• Building an API client with retries and authentication
• Investigating failed requests or missing results

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

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

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

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

Investigate bugs using a graph of code relationships. Find relevant functions, trace calls and execution paths, check recent changes, and assess what a proposed fix could affect before reading and changing the implementation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Narrowing down code related to a bug
• Tracing the execution path that triggers an issue
• Checking whether recent changes caused a bug

Turn an OpenAPI or Swagger specification into an MCP server, a bridge that lets AI assistants call an API. Covers tool generation, authentication, local testing, and deployment to mcp-use cloud.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Make an internal API callable from Claude
• Generate MCP tools from a Swagger specification
• Test API calls locally before cloud deployment

Select relevant code using Trailmark's map of function relationships, then delegate explanation, review, or edit proposals to a smaller model. Limit the supplied source, check cited evidence, and verify proposed changes before applying them.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Asking a small model to explain a function
• Tracing callers and paths from entrypoints
• Requesting edit proposals for a specific line range

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

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