Patterns for continuous autonomous agent loops with quality gates, evals, and recovery controls. Use when running an agent loop that must self-check, gate on evals, and recover from failures.
インストール方法を見るインストールする前に、エージェントに与えられる指示の中身を確認できます。
This is the v1.8+ canonical loop skill name. It supersedes autonomous-loops while keeping compatibility for one release.
Start
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+-- Need strict CI/PR control? -- yes --> continuous-pr
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+-- Need RFC decomposition? -- yes --> rfc-dag
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+-- Need exploratory parallel generation? -- yes --> infinite
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+-- default --> sequential
Recommended production stack:
ralphinho-rfc-pipeline)plankton-code-quality + /quality-gate)eval-harness)nanoclaw-repl)/harness-auditまだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Design, implement, and audit accessible UI to WCAG 2.2 Level AA across Web, iOS, and Android — semantic ARIA roles and labels, accessibility traits and hints, focus management, contrast, target size, and screen-reader support. Use when building or auditing UI for accessibility compliance, keyboard navigation, or screen-reader support.
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。
Full-stack diagnostic for agent and LLM applications. Audits the 12-layer agent stack for wrapper regression, memory pollution, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings with code-first fixes. Essential for developers building agent applications, autonomous loops, or any LLM-powered feature. Use when an agent or LLM feature misbehaves and the failing layer is unknown, or before shipping an agent 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。
Head-to-head comparison of coding agents (Claude Code, Aider, Codex, etc.) on custom tasks with pass rate, cost, time, and consistency metrics. Use when choosing between coding agents, or when a change to an agent setup needs measured pass rate, cost, and time rather than an impression.
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 optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates. Use when defining or revising an agent's tool set, action space, or observation format.
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。
Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports. Use when an agent run fails and you need a reproducible diagnosis instead of a retry.
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。
Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports. Use when an agent run fails and you need a reproducible diagnosis instead of a retry.
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。