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概要と使いどころ
AI movement decision router for chase, patrol, crowd, and bake choices on top of NavigationAgent/Server. Use when deciding node agent vs RID server, bake vs obstacle, layer masks, or retarget policy — not for engine navmesh recipes. Keywords: AI navigation, chase retarget, patrol, crowd RVO, bake vs obstacle, NavigationAgent decision tree.
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。
Bevy ECS skill for AI navigation policy. Keywords: Bevy, AI, chase, patrol, navigation, ECS.
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。
Stream-JSON chaining for multi-agent pipelines, data transformation, and sequential workflows
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。
Knowledge base from "Solving a Million-Step LLM Task with Zero Errors" (Meyerson, Paolo, Dailey, Shahrzad, Francon, Hayes, Qiu, Hodjat, Miikkulainen — arXiv:2511.09030). Use when designing long-horizon, multi-step agentic LLM pipelines that need very high reliability, applying Maximal Agentic Decomposition (MAD), first-to-ahead-by-k voting, or red-flagging error correction, estimating cost/reliability scaling laws for multi-agent systems, or studying Massively Decomposed Agentic Processes (MDAPs).
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Author and review GitHub Actions workflow YAML safely so syntactically-valid YAML can't ship a workflow that GitHub Actions refuses to run. USE FOR: editing, adding, or reviewing any file under .github/workflows/, writing run-name/name/if/env/run values that contain ${{ }} expressions, diagnosing a run that fails with 'This run likely failed because of a workflow file issue' and no jobs starting, deciding when a workflow scalar must be quoted, validating workflows with actionlint. DO NOT USE FOR: authoring application YAML unrelated to GitHub Actions, Azure Pipelines, GitLab CI, or non-workflow YAML. SCOPE: this skill covers *syntactic/structural* correctness of workflow YAML (quoting, parsing, actionlint); for *semantic and functional* workflow design (what a workflow should do, agentic-workflow behavior), see .github/agents/agentic-workflows.agent.md — the two are complementary. INVOKES: actionlint (downloaded pinned binary) plus git/grep for inspection.
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 red-teaming an agentic AI / LLM application — indirect & zero-click prompt injection, MCP tool poisoning, persistent memory poisoning, excessive-agency tool abuse, multi-turn jailbreaks, PyRIT/Garak/Promptfoo harnesses
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 attacking an AI/ML system or model — prompt injection & jailbreaks (Crescendo, Skeleton Key, Best-of-N), RAG/vector poisoning, agentic/MCP exploitation (CVE-2025-54136), ML supply-chain RCE (pickle CVE-2025-32434), model extraction / membership inference / adversarial suffixes (GCG)
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。
Orchestrate multiple AI agents for construction workflows: estimator, scheduler, document, QA and safety agents coordinated by a supervisor agent, with human checkpoints. Use when automating end-to-end project processes with agentic AI.
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.
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。
Build software with Claude Code, the AI-powered CLI coding agent. Use when a user asks to set up Claude Code, configure CLAUDE.md project files, use slash commands, manage permissions, create hooks, or optimize agentic coding workflows.
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。
Cross-chain & L2 Architect: Chain selection, rollups, bridges and cross-chain messaging with their trust assumptions.
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。
Discoverability shim for the Retail-Agentic-Commerce pre-commit analysis Codex skill. Use before commits, after significant changes, or for implementation-quality validation.
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。
Discoverability shim for the Retail-Agentic-Commerce UI Codex skill. Use when creating or modifying frontend pages, components, hooks, styling, tests, or browser behavior.
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。
Discoverability shim for the Retail-Agentic-Commerce setup Codex skill. Use when the user asks to setup, install, start, launch, or run the project stack.
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。
Discoverability shim for the Retail-Agentic-Commerce backend feature Codex skill. Use when writing or modifying Python backend code, routes, services, models, tests, or protocol logic.
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。
Discoverability shim for the Retail-Agentic-Commerce CVE remediation Codex skill. Use for CVE, BDSA, GHSA, OSV, Dependabot, Black Duck, Snyk, npm, PyPI, or package advisory remediation.
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。
React and Next.js frontend development standards for Retail-Agentic-Commerce. Use when creating or modifying UI pages, components, hooks, styling, tests, or Apps SDK widget browser behavior in src/ui or related frontend surfaces.
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。
Launch all services for the Retail-Agentic-Commerce project. Use when the user types setup, install, or asks to start, launch, or run the project stack.
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。
Smart agentic web data extraction with multi-strategy scraping (Crawl4AI v4, Firecrawl), LLM extraction loops, anti-bot bypass, and structured export / Ekstraksi data web cerdas dan agentic dengan scraping multi-strategi (Crawl4AI v4, Firecrawl), ekstraksi LLM, bypass anti-bot, dan ekspor terstruktur.
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 AI-assisted coding workflows — agentic code generation, multi-agent code swarms, self-healing CI/CD, automated PR review, spec-to-code pipelines, codebase knowledge graphs, and human-in-the-loop approval gates / Panduan ahli untuk workflow pengkodean berbasis AI — generasi kode agentic, code swarm multi-agen, CI/CD self-healing, review PR otomatis, pipeline spec-to-code, knowledge graph codebase, dan gate persetujuan human-in-the-loop.
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。
Bridge layer connecting the emerging agentic coordination protocol ecosystem (MCP, A2A, ANP, AGNTCY, AITP, LMOS, AAIF) to the plurigrid/asi skill graph. Maps protocol-level identity mechanisms (W3C DID, OAuth 2.1, IPSIE, Entra Agent ID, Agent Cards, passport.gay) into GF(3)-classified skills. The central unsolved problem — no unified identity across MCP+A2A+A2UI — is directly addressed by the bisimulation oracle composition.
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。
Design human-visible, machine-readable website layers for AI assistants, RAG, and agentic search. Use this skill when the user asks for llms.txt, LLM discovery plans, visible TLDR or direct-answer blocks, source trails, entity pages, markdown alternates, AI crawler policy, topic-to-URL maps, assistant-readable public content, or LLM-friendly architecture that must preserve canonical SEO strategy without hidden bot-only facts or duplicate story bodies.
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。
Install skills, agents, and teams from agent-almanac into any supported agentic framework using the CLI. Covers framework detection, content search, installation with dependency resolution, health auditing, and manifest-based syncing. Use when setting up a new project with agentic capabilities, installing specific skills or entire domains, targeting multiple frameworks simultaneously, or maintaining a declarative manifest of installed content.
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。
Design and build AI agents for any domain. Use when users: (1) ask to "create an agent", "build an assistant", or "design an AI system" (2) want to understand agent architecture, agentic patterns, or autonomous AI (3) need help with capabilities, subagents, planning, or skill mechanisms (4) ask about Claude Code, Cursor, or similar agent internals (5) want to build agents for business, research, creative, or operational tasks Keywords: agent, assistant, autonomous, workflow, tool use, multi-step, orchestration
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。