1,682 件 ・ 関連度順
概要と使いどころ
Audit GitHub Actions that run AI agents for prompt injection, unsafe interpolation, sandbox gaps, and permissive actor rules. Use for agentic CI workflows, not general application code review.
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。
Write reliable prompts for Agentica/REPL agents that avoid LLM instruction ambiguity
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。
Reference guide for Agentica multi-agent infrastructure APIs
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。
Guide for integrating Agentica SDK with Claude Code CLI proxy
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。
Agentic Workflow Pattern
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。
Agentica server + Claude proxy setup - architecture, startup sequence, debugging
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。
Spawn Agentica multi-agent patterns
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。
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。
AI Agent Harness 设计模式 — 记忆、权限、上下文工程、委派、技能、 Hook、Bootstrap。中文版。
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。
Harness patterns for coding agents — memory, permissions, context engineering, delegation, skills, hooks, bootstrap.
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。
Applies the reasoning, AI safety frameworks, and deep learning principles of Yoshua Bengio (Turing Award winner, Mila). Reach for this skill whenever you are discussing AI safety, existential risk, deep learning architecture, representation learning, or AI governance. Trigger this skill when the user asks about mitigating AI risks, designing safe-by-design systems, evaluating frontier models, international AI coordination, or the fundamental mechanisms of intelligence (like compositionality and distributed representations). Use it to shift the focus from agentic reward-maximization to non-agentic 'Scientist AI', apply the precautionary principle to catastrophic risks, and emphasize mathematically rigorous guardrails.
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。
Kaggle-first end-to-end competition workflow for scored submissions. Use when Codex must run Kaggle or competitive ML workflows through scored submission, including code competitions, validation, metrics, policy-safe public notebook/discussion intel, tabular/text/image modeling, tuning, ensembling/stacking, proactive multi-notebook architectures, producer notebooks that train models and export private Kaggle artifact datasets, downstream consumer notebooks, Kaggle GPU offload, kagglehub access, hidden-test debugging, and public score retrieval.
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。
Interview an instructor and generate a complete MATLAB and Simulink enabled course in IMS Common Cartridge format. Use when the user asks to create a MATLAB course, Simulink course, MATLAB and Simulink curriculum, MATLAB Course Designer-ready course shell, Common Cartridge course package, .imscc export, MATLAB Exercises with validated .m files, or Simulink Exercises with starter and solution model files. Coordinates IDStack, MATLAB Agentic Toolkit, Simulink Agentic Toolkit, matlab-create-course-activity, simulink-create-course-activity, and the existing matlab-generate-grader-assessments skill.
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。
Goal-driven E2E testing where a browser agent (Playwright MCP / computer-use) reads a natural-language goal and explores the app via the accessibility tree to assert outcomes — no pre-written script. Covers when intent-driven beats scripted, making agent runs deterministic (pinned model, temperature 0, seeded data, bounded steps, explicit success assertion, snapshot-not-pixel), cost/latency control, the accessibility-tree-first interaction model, CI gating, and graduating a stable run into a scripted Playwright test. Use when: "agentic browser test," "goal-driven browser test," "let an agent explore the app," "natural-language E2E," "browser agent smoke test," "Playwright MCP test." Not for: Writing/maintaining deterministic scripted Playwright tests — that is playwright-automation. Testing your product's OWN LLM features — that is ai-system-testing. Related: playwright-automation, ai-system-testing, exploratory-testing, test-reliability, qa-project-context.
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 execute structured exploratory testing sessions. Covers Session-Based Test Management (SBTM), charter writing, heuristic-based exploration (HICCUPS, FEW HICCUPS), bug discovery patterns, note-taking templates, and conversion of findings to automated tests. Use when: "exploratory testing," "SBTM," "manual testing," "bug hunting," "test charter," "heuristic testing." Not for: an AI browser agent autonomously exploring the app from a natural-language goal — use agentic-browser-testing. Not for: testing your product's own AI/LLM features — use ai-system-testing. Related: test-planning, ai-bug-triage, risk-based-testing, agentic-browser-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。
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。
Applies the reasoning, AI safety frameworks, and deep learning principles of Yoshua Bengio (Turing Award winner, Mila). Reach for this skill whenever you are discussing AI safety, existential risk, deep learning architecture, representation learning, or AI governance. Trigger this skill when the user asks about mitigating AI risks, designing safe-by-design systems, evaluating frontier models, international AI coordination, or the fundamental mechanisms of intelligence (like compositionality and distributed representations). Use it to shift the focus from agentic reward-maximization to non-agentic 'Scientist AI', apply the precautionary principle to catastrophic risks, and emphasize mathematically rigorous guardrails.
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。
Sub-skill: Implement ARD (Agentic Resource Discovery). Publish /.well-known/ai-catalog.json so agents can discover MCP servers, A2A agents, skills and API tools (agenticresourcediscovery.org / ai-catalog).
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。
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。
Pre-commit implementation analysis for Retail-Agentic-Commerce. Use before commits, after significant changes, or when validating Apps SDK tools, mock data, MCP communication, widget isolation, or implementation quality.
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 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。
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。