'call graph' 검색 결과

Japanese-inspired canvas and graphic design skill for HTML5 Canvas and SVG, covering traditional aesthetics (ma, wabi-sabi, yugen), calligraphy, washi textures, traditional patterns, and seasonal motifs. Triggers on requests for 和風デザイン, Canvas描画, SVGイラスト, 日本画風, Japanese-inspired graphics.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

MANDATORY prerequisite — load this skill BEFORE every `generate_diagram` tool call. NEVER call `generate_diagram` directly without loading this skill first. Trigger whenever the user asks to create, generate, draw, render, sketch, or build a diagram — flowchart, architecture diagram, sequence diagram, ERD or entity-relationship diagram, state diagram or state machine, gantt chart, or timeline. Also trigger when the user mentions Mermaid syntax or wants a system architecture, decision tree, dependency graph, API call flow, auth handshake, schema, or pipeline visualized in FigJam. Routes to type-specific guidance, sets universal Mermaid constraints, and tells you when to use a different diagram type or skip the tool entirely (mindmaps, pie charts, class diagrams, etc.).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Data structures and algorithms for AI agent episodic memory. Covers vector stores (HNSW, IVF, PQ), temporal indexing, knowledge graphs with triple stores, hierarchical summarization, forgetting curves, working/long-term/ procedural memory, and memory consolidation. Deep analysis of MemGPT/Letta, Zep/Graphiti, Mem0, and the Stanford generative agents memory architecture. Teaches the CS fundamentals behind how agents remember, retrieve, and forget. Activate on: "agent memory", "episodic memory", "vector search algorithm", "HNSW", "memory retrieval", "forgetting curve", "knowledge graph memory", "MemGPT", "Letta", "Zep", "Mem0", "memory consolidation", "temporal retrieval", "agent long-term memory", "memory layer". NOT for: conversation protocol design (use agent-conversation-protocols), agent infrastructure selection (use agentic-infrastructure-2026), building RAG pipelines (use ai-engineer).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

기존 사이트의 소스와 브라우저 동작을 조사해 모양과 조작을 로컬에서 재현합니다. 자료 수집부터 구현, 원본과의 비교 검증, 콘텐츠 교체까지 진행합니다.

• 기존 사이트를 로컬에서 재현하며 학습
• WebGL과 Canvas 효과 조사
• 재현한 사이트와 원본 비교

공개 추적 데이터에서 지역 내 군용기의 위치, 고도, 기종, 운용 조직을 조회합니다. 지역·조직·기종으로 필터링하고 탐지된 활동 그룹도 확인합니다.

• 지역 내 추적된 군용기 확인
• 운용 조직별 항공기 필터링
• 공중급유기와 정찰기 활동 확인

Audit geographic meta tags on region-specific pages, checking region codes, coordinate formats, and consistency with the intended audience. Use the findings to correct supplementary location signals while keeping language targeting and structured business data as primary approaches.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Auditing geographic tags on regional pages
• Checking region codes and coordinate consistency
• Reviewing supplementary location signals for Bing

Store conversation history and project material locally, then search by meaning and retrieve original text. Organize memories by person or project, track dated relationships and facts, and keep diary entries that connect work across sessions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Import conversation history or project files
• Search past discussions by meaning
• Organize records by person, project, and topic

Generate, compose, and edit product images, posters, illustrations, and social graphics through the paid Beatra service. The workflow covers reviewing upload files and estimated costs before each paid call, then checking results and actual charges.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Creating product images
• Designing posters and social graphics
• Editing reference images and making photo variations

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

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

Investigate CUDA crashes in SGLang using logs and inputs saved before execution. Inspect tensor shapes, data types, and numerical anomalies, with guidance for separate process logs and additional CUDA debugging tools.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating illegal memory access and device-side assertions
• Tracing NaN or Inf values through kernel calls
• Saving inputs before a CUDA crash

Send signed messages between AI agents, check inboxes, and reply to requests. Supports file attachments, message types, and priorities, with basic messaging running locally and separate identities for each agent.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Sending work requests to another agent
• Checking an agent inbox and replying
• Handing off context with attached files

Add persistent memory to NanoClaw agents using the default Claude provider. With mnemon installed in the container, agents retrieve relevant context before responding and save new insights after each turn, retaining memory across container restarts.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Recalling earlier conversations in a new session
• Keeping agent memory across container restarts
• Checking memory hooks and storage settings

Create a manufacturing drawing PDF from a part’s 3D geometry, with projected views, measured dimensions, hole callouts, and a title block. Rerun the drawing script after model changes to update the sheet.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Prepare a dimensioned drawing for manufacturing
• Document hole sizes and machining requirements
• Update drawings after changing a part model

Turn completed research, analysis, reports, or plans from the current session into a self-contained HTML artifact. A renderer controls layout, typography, and color, using structured content and a choice of two visual styles.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Turn research findings into an HTML artifact
• Format a cost analysis with tables and callouts
• Present investigation findings as a designed document

Analyze code structure with Trailmark to identify complex functions, trace input flow, assess impact across connected code, and locate privilege boundaries. The resulting JSON provides detailed evidence for deciding where to focus a code audit.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Prioritizing code audit targets
• Tracing input flow and assessing impact
• Inspecting unresolved calls and type references

Evaluate how well existing tests catch code changes, then combine the results with function relationships. Get a report separating harmless findings, missing unit tests, and targets for fuzzing, which tests many generated inputs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Triage code changes that existing tests failed to catch
• Prioritize missing unit tests
• Find functions suited to fuzzing

Batch retouch portrait photos with Adobe tools, correcting alignment and tone and adding optional presets, background blur, or cropping. Review a sample before processing the full set, then receive previews and download links.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Batch retouching wedding portraits
• Polishing a folder of event photos
• Applying a consistent look to headshots

Build JavaScript or TypeScript apps that interact with NEAR. This skill guides token transfers, smart contract calls, account and key management, transaction signing, and message authentication through near-api-js v7 examples.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adding NEAR and token transfers to an app
• Reading and updating smart contract data
• Managing account keys and access permissions

Review database access built with Entity Framework Core to find slow queries, excessive database calls, memory costs, and correctness issues. It traces application code and uses available SQL logs or execution plans to support targeted fixes.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing slow EF Core queries and excessive database calls
• Investigating memory use in imports and background jobs
• Checking search filters, pagination, and index coverage

Find and improve slow React components while preserving interface behavior. It targets unnecessary renders, costly calculations, and long lists, then compares render counts and timings with baseline measurements using React DevTools Profiler.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating sluggish input and screen updates
• Reducing repeated renders caused by timers
• Improving long-list scrolling

Explores an existing codebase at quick, deep, or architecture-focused levels. Summarizes structure, function calls, and dependencies, then produces documentation or a handoff for implementation without changing the code.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Getting oriented in an unfamiliar codebase
• Understanding how a specific feature works
• Mapping layers and circular dependencies

Map the load-bearing files, symbols, callers, and dependencies of an unfamiliar codebase before making changes. Use for architecture orientation, impact analysis, what-would-break questions, or finding callers. Uses CodeGraph when available and a dependency-free builtin fallback.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use to extend an existing agent project with memory, app integration, VPC, multi-agent, migration, model, browser, code interpreter, payments, or resource removal. Triggers: "add memory", "remember across sessions", "call agent from app", "invoke agent from code", "agent auth", "streaming", "VPC", "VPC connectivity", "can't reach from VPC", "multi-agent", "A2A", "A2A auth", "orchestrator not delegating", "specialist not called", "migrate Bedrock Agent", "migration issue", "change model", "browser tool", "code interpreter", "delete agent", "tear down", "agentcore remove", "cross-account memory", "add payments capability to my agent", "wire payments plugin", "integrate x402 payments with the agent I'm building", "add MPP payments", "Machine Payments Protocol". External APIs via Gateway: use agents-connect. New project: use agents-get-started. CLI/dev-server errors: use agents-debug. Runtime x402/MPP payments: use agents-pay. Migration-specific Strands vs LangGraph routes here.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Extract, suggest, and sync tags and categories for blog posts across all major CMS platforms. Supports WordPress REST API, Shopify GraphQL, Ghost Content API, Strapi REST/GraphQL, and Sanity GROQ. Generates tag suggestions from content analysis (keyword frequency, heading extraction, semantic grouping), enforces minimum post-count thresholds to prevent thin tag archives, and syncs taxonomy via authenticated API calls. Use when user says "tags", "categories", "taxonomy", "tag suggestions", "sync tags", "WordPress tags", "Shopify tags".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.