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概要と使いどころ
Browser Use is an open source browser automation framework for AI agents that turns websites into controllable interfaces for multi-step tasks. It combines a Python SDK, browser orchestration, and model integrations so agents can navigate, extract data, and complete workflows in real browsers.
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Use GitHub Agentic Workflows to let an agent triage issues, inspect CI failures, or deliver scheduled repository upkeep inside GitHub Actions with explicit workflow definitions and reviewable runs. This is for bounded, repeatable repository operations, not for listing GitHub as a general coding platform.
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CI/CD pipeline and software-supply-chain exploitation: Jenkins, GitHub Actions, GitLab CI/CD, Azure DevOps, Gitea/Forgejo, self-hosted runners/agents, poisoned pipeline execution, artifact/cache abuse, dependency confusion, third-party Action trust, GitOps/registry poisoning, workload identity/OIDC, and emerging agentic CI/CD. Use this skill when the operator has access to a CI/CD system, source repository, build/deploy configuration, runner/agent, artifact/package/registry path, or software delivery trust chain.
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ALIGN — Agentic Loop Image GeneratioN. Paint a subject in the craft of a reference artwork as executable p5.js, Canvas, SVG or another mark-based medium, replay that craft's real order of work, and improve it through pixel-only cross-model review. The reference fixes the craft; the subject may be the reference's own scene (千里江山图 → p5.js) or another (清明上河图 → 武汉). Not for one-shot raster image generation.
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。
ALIGN — Agentic Loop Image GeneratioN. Paint a subject in the craft of a reference artwork as executable p5.js, Canvas, SVG or another mark-based medium, replay that craft's real order of work, and improve it through pixel-only independent Codex subagent review. The reference fixes the craft; the subject may be the reference's own scene (千里江山图 → p5.js) or another (清明上河图 → 武汉). Not for one-shot raster image generation.
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。
Policy for the cross-model council: when to hand off to an installed sibling plugin's own slash command (`/codex:*`) versus dispatching our `delegation-runner` (backend: codex|agy|…), which backend to pick, what permission posture to use, and the fan-out/cost discipline that applies regardless of backend — plus the council layer built on that substrate: which play to run, how a cast gets picked from the project roster, budget guardrails, and where project policy/commission config lives. Consult before delegating out to a peer agentic CLI, before choosing between a sibling plugin's command and our runner, before choosing a permission posture, before fanning out more than one external delegation concurrently, or before running a council play (`/…:council-run`).
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。
Monitor, cap, and recover from context accumulation in agentic systems. Covers per-cycle cost tracking, context window auditing, budget caps with enforcement policies, emergency pruning when approaching limits, and progressive disclosure integration to minimize token spend on routing. Use when running long-lived agent loops (heartbeats, polling, autonomous workflows), when context windows are growing unpredictably between cycles, when API costs spike beyond expected baselines, when designing new agentic workflows that need cost guardrails from the start, or when post-mortem analysis reveals a cost incident caused by context accumulation.
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-landing PR review. Analyzes diff against the base branch for SQL safety, LLM trust boundary violations, conditional side effects, and other structural issues. Use when asked to "review this PR", "code review", "pre-landing review", or "check my diff".
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Before committing any non-trivial change, dispatch the diff to a reviewer runtime via ATO (`ato dispatch <reviewer> --session <id>`), parse the numbered/severity-tagged findings, apply or defer each one with a recorded justification, then commit. Fights the "build passes therefore ship it" failure mode — what Garry Tan calls the AI agent complexity ratchet. Place in the v2.16 stack: this skill is the LAST gate. `ato-warroom` decides the design; `ato-mission` runs the multi-step work and produces the diff; `ato-review` checks the diff before commit. When the review is part of a Mission, dispatch the review with `--require-tools read_file,grep,git_diff,git_log` so the reviewer can walk the source itself instead of reasoning from a paraphrase (PR-1.5 tool surface). Receipts land in `execution_logs` and the Mission narrative. Fires automatically before commits touching public surface (CLI subcommands, Tauri commands, MCP tools, schema migrations, security boundaries) or whenever a diff exceeds ~50 LOC of behavior change.
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 review autonomous or semi-autonomous agent loops — systems where an agent prompts itself, runs on a schedule or event, and iterates toward a goal without a human at the keyboard. Use this whenever the user wants to build, design, harden, or audit any kind of self-running agent, agentic workflow, control loop, background worker, cron/heartbeat/webhook-driven agent, "Ralph loop", auto-PR-fixer, monitoring agent, or any long-running task that should keep going on its own until done. Trigger this even when the user says "agentic workflow", "automation that runs itself", "an agent that keeps trying until X", or "make my agent stop running away / burning tokens" without using the word "loop". Also use it to review an existing loop's stopping conditions, guardrails, verification, and escalation paths.
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。
Advanced Agentic Coding framework providing mission briefs, guardrails, and integration hints for complex tasks. This skill ensures high-quality output through disciplined automation and systematic workflows.
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Open protocol for AI agent interoperability enabling standardized communication between agents, applications, and humans across different frameworks
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MATLAB R2026a agentic workflow router. Use this whenever the user asks for MATLAB, Simulink, .m/.mlx/.slx work, toolbox-driven engineering, paper reproduction, simulation, code generation, data analysis, control, robotics, signal/image/AI workflows, or wants an end-to-end automated MATLAB task with validation. For Simulink tasks on this machine, default to a visible workflow: open MATLAB/Simulink windows first so the user can watch modeling, simulation, tuning, and plotting progress.
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Fundraising and investor operations specialist. Owns founder context, pitch narrative, investor targeting, investor communication, diligence readiness, and board-update hygiene for the Agentic Company founder.
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Run Forge's canonical verification chain (format:check + lint + typecheck + build + test). Use before claiming any task done or opening a PR.
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 Forge's canonical verification chain (format:check + lint + typecheck + build + test). Use before claiming any task complete or opening a PR.
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。
Overview of Forge's Agentic Coding Flywheel methodology — plan space, bead space, code space. Invoke this skill when you need to know which Flywheel skill or prompt applies to the current phase.
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。
Provider-agnostic patterns for LLM function calling, tool loops, and agentic workflows
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Universal prompt engineering techniques for any LLM. Use when crafting, optimizing, or reviewing prompts for AI models. Triggers on requests like "improve this prompt", "write a system prompt", "optimize my instructions", "help me prompt engineer", "audit this prompt", "review my prompt", or when building agentic systems that need structured prompts.
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 AI-integrated development environments with comprehensive coverage of Windsurf, Cursor, Antigravity, Zed, Cline, and FOSS newcomers, including intelligent usage patterns, cost optimization, and the evolution toward fully agentic development
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 AI-first interfaces that build ongoing relationships through memory, trust evolution, and collaborative planning, not just isolated screen interactions
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。
Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing.
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。
Score a repository's agentic legibility from repo-visible evidence only. Use when Codex needs to audit how easy a codebase is for coding agents to discover, bootstrap, validate, and navigate, especially for harness-engineering reviews, developer-experience audits, repo cleanup, or before/after comparisons after improving docs, tooling, or architectural constraints.
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Scan and redact PII and sensitive data (emails, phone numbers, SSNs, API keys, IP addresses, credentials, amounts, company/person names) from repository files. Includes a pre-commit hook to block commits containing PII. Use when asked to audit code for sensitive data, sanitize files before publishing, or install PII detection hooks. For application security hardening see security-engineering.
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。