1,088 件 ・ 関連度順
概要と使いどころ
Decide when AutoCode acts on its own and when it must stop and ask the user. Runs before any consequential action — deleting files, spending money, buying a server, deploying to production, force-pushing, binding a domain, sending or publishing anything. Ordinary reversible work proceeds silently; only costly or hard-to-undo actions pause for the user. Powered by I-Lang v5.0 vector judgment.
日本語の概要は準備中です。原文の説明を表示しています。
Use when an agent must stand up the OpenClaw↔Cognee integration with FalkorDB as the vector + graph store. This skill is SELF-CONTAINED — it tells you to generate every file (Dockerfile, sitecustomize.py, docker-compose.yaml) from the exact contents below, build a custom Cognee image that loads the FalkorDB adapter, run Cognee + FalkorDB together, self-verify the adapter registered, and point the OpenClaw cognee plugin at it (including per-agent graphs). No other files in this directory are required.
日本語の概要は準備中です。原文の説明を表示しています。
Designs RAG retrieval for LLM grounding: chunking, BM25+vector hybrid, reranking, GraphRAG. Use when adding embeddings to SQLite FTS5 search or running held-out retrieval eval.
日本語の概要は準備中です。原文の説明を表示しています。
Audit the game for security vulnerabilities: save tampering, cheat vectors, network exploits, data exposure, and input validation gaps. Produces a prioritised security report with remediation guidance. Run before any public release or multiplayer launch.
日本語の概要は準備中です。原文の説明を表示しています。
Architecting crawl and scraping systems: distributed crawler topology, URL frontier, politeness, compliance. Architecture-only. Not for single-page scraping (Vector) or ETL pipelines (Stream).
日本語の概要は準備中です。原文の説明を表示しています。
Automating browsers via Playwright and Chrome DevTools for data collection, form interaction, screenshot capture, and network monitoring. Task completion focus (vs Voyager for E2E testing).
日本語の概要は準備中です。原文の説明を表示しています。
Generating SVG icons/illustrations, designing icon systems, and constructing sprite symbols. Use when vector assets are needed.
日本語の概要は準備中です。原文の説明を表示しています。
Implement ReasoningBank adaptive learning with AgentDB's 150x faster vector database. Includes trajectory tracking, verdict judgment, memory distillation, and pattern recognition. Use when building self-learning agents, optimizing decision-making, or implementing experience replay systems.
日本語の概要は準備中です。原文の説明を表示しています。
Initialize an AgentDB Cognitive Container (.rvf file) in the current project. Sets up storage, embedder config, and the agentdb MCP server. Use when the user is starting a new project that needs vector memory, or asks to "set up agentdb" / "init agentdb".
日本語の概要は準備中です。原文の説明を表示しています。
Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.
日本語の概要は準備中です。原文の説明を表示しています。
Hybrid search — BM25 keyword + dense vector fused with Reciprocal Rank Fusion. Use when queries have specific identifiers, code symbols, or proper nouns that pure semantic search might miss.
日本語の概要は準備中です。原文の説明を表示しています。
Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
日本語の概要は準備中です。原文の説明を表示しています。
CVSS 3.1 vector examples, executive summary template, full technical finding template, and remediation language bank for pentest reports
日本語の概要は準備中です。原文の説明を表示しています。
Audit guardrail coverage — bypass vectors, false positive rates, policy gap analysis, red-team scenarios. Use when asked to "audit our AI guardrails", "can our filters be bypassed", or "check guardrail false positives".
日本語の概要は準備中です。原文の説明を表示しています。
Expert guide for Knowledge Graphs, GraphRAG, Microsoft GraphRAG, Neo4j Text2Cypher, multi-hop relational retrieval, and hybrid vector-graph search / Panduan ahli Knowledge Graph, GraphRAG, dan pencarian relasional multi-hop.
日本語の概要は準備中です。原文の説明を表示しています。
Map design, spatial analysis, and Geographic Information Systems. Covers map projections and their distortion trade-offs, coordinate systems, thematic mapping techniques (choropleth, proportional symbol, dot density, isoline), remote sensing and satellite imagery, GIS data models (vector and raster), spatial analysis operations (overlay, buffer, interpolation, network analysis), and cartographic design principles. Use when creating maps, analyzing spatial data, selecting projections, interpreting satellite imagery, or reasoning about spatial relationships in any domain.
日本語の概要は準備中です。原文の説明を表示しています。
Distributed consensus algorithms and logical time for cloud and multi-node systems. Covers Lamport clocks, vector clocks, FLP impossibility, Paxos (basic, multi, fast), Raft, Viewstamped Replication, Byzantine fault tolerance basics, quorum reads/writes (N/R/W), leader election, and TLA+ specification style. Use when designing replicated state machines, picking a consensus protocol, reasoning about split-brain and quorum loss, or writing formal specs for distributed coordination.
日本語の概要は準備中です。原文の説明を表示しています。
Digital art tools, techniques, and workflows for art education. Covers raster and vector workflows, digital painting, photo manipulation, generative and procedural art, 3D modeling and rendering, pixel art, the relationship between traditional skills and digital execution, and ethical considerations of AI-generated imagery. Use when working with digital tools, evaluating digital art, or bridging traditional art concepts into digital practice.
日本語の概要は準備中です。原文の説明を表示しています。
Run this appropriateness check the moment you are about to integrate a retrieved long-term memory — a Grove content-addressed hit, a chroma/pgvector neighbour, a memory-consolidation digest, or a MEMORY.md line — into a response, especially anything touching private origins, Fox Companies IP, credentials, or Center Camp / consent-governed content. It answers a question intent-router never asks: not WHAT to fetch or HOW, but WHETHER a correctly-retrieved item should reach output. Relevance is not appropriateness — a perfect similarity match can still be a boundary violation. Default is FAIL-CLOSED: if in-context authorization is uncertain, the memory may inform behaviour but must not be surfaced. Backed by RBI-Eval (arxiv 2606.06055v1). Triggers on surfacing recalled sensitive memory into a response.
日本語の概要は準備中です。原文の説明を表示しています。
Linear algebra and higher-dimensional thinking. Vectors, matrices, transformations — the architecture of mathematical space.
日本語の概要は準備中です。原文の説明を表示しています。
Scholze-Clausen condensed math - condensed sets, liquid vector spaces, solid modules, 6-functor formalism, Kunneth exactness
日本語の概要は準備中です。原文の説明を表示しています。
Comprehensive Cloudflare platform skill covering Workers, Pages, storage (KV, D1, R2), AI (Workers AI, Vectorize, Agents SDK), networking (Tunnel, Spectrum), security (WAF, DDoS), and infrastructure-as-code (Terraform, Pulumi). Use for any Cloudflare development task.
日本語の概要は準備中です。原文の説明を表示しています。
Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
日本語の概要は準備中です。原文の説明を表示しています。
Audits GitHub Actions workflows for security vulnerabilities in AI agent integrations including Claude Code Action, Gemini CLI, OpenAI Codex, and GitHub AI Inference. Detects attack vectors where attacker-controlled input reaches AI agents running in CI/CD pipelines, including env var intermediary patterns, direct expression injection, dangerous sandbox configurations, and wildcard user allowlists. Use when reviewing workflow files that invoke AI coding agents, auditing CI/CD pipeline security for prompt injection risks, or evaluating agentic action configurations.
日本語の概要は準備中です。原文の説明を表示しています。