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Found 72 Skills
Concurrent investigation of independent failures. Use when multiple unrelated issues need parallel resolution.
Learn how to manage conversation context in AMCP to avoid LLM API errors from exceeding context windows. This skill covers SmartCompactor strategies, token estimation, configuration, and best practices.
Multi-agent review of implementation plans. Use after creating a plan but before implementing, especially for complex or risky changes.
Orders scheduler. Reads .noodle/mise.json, writes .noodle/orders-next.json. Schedules work orders based on backlog state, plan phases, session history, and task type schedules.
Monitors context window health throughout a session and rides peak context quality for maximum output fidelity. Activates automatically after plan-interview and intent-framed-agent. Stays active through execution and hands off cleanly to simplify-and-harden and self-improvement when the wave completes naturally or exits via handoff. Use this skill whenever a multi-step agent task is underway and session continuity or context drift is a concern. Especially important for long-running tasks, complex refactors, or any work where degraded context would silently corrupt the output. Trigger even if the user doesn't say "context surfing" — if an agent task is running across multiple steps with intent and a plan already established, this skill is live.
Decomposes a spec or architecture into buildable tasks with acceptance criteria, dependencies, and implementation order for AI agents or engineers. Produces `.agents/tasks.md`. Not for clarifying unclear requirements (use discover) or designing architecture (use system-architecture). For code quality checks after building, see review-chain. For packaging and PRs, see ship.
Used when executing implementation plans with independent tasks in the current session
Web research, content extraction, and deep analysis. Multi-source parallel search with extended thinking. Supports Fabric pattern selection (242+ prompts). USE WHEN: "research X", "extract wisdom from", "analyze this content", "find info about".
Proposal-first development workflow with commit hygiene and decision authority rules. Enforces: propose before modifying, atomic commits, no force flags, warnings-as-errors. Use for any project where AI agents are primary developers and need guardrails.
Mechanize Pattern 15 — the seven-pass adversarial review protocol for academic manuscripts. Spawns 7 forked subagents in parallel (abstract, intro, methods, results, robustness, prose, citations), then synthesizes a prioritized revision checklist. Use for submission-ready or R&R-stage papers where single-pass review isn't enough.
Creates project constitution files (CLAUDE.md/AGENTS.md) that serve as always-loaded context for coding agents. Use when setting up a new project for spec-driven development, configuring agent instructions, writing CLAUDE.md or AGENTS.md, or establishing project-wide coding standards and constraints.
How to write Cavekit-quality kits that AI agents can consume effectively. Covers implementation-agnostic cavekit design, testable acceptance criteria, hierarchical structure, cross-referencing, cavekit templates, greenfield and rewrite patterns, cavekit compaction, and gap analysis. Trigger phrases: "write kits", "create kits", "cavekit this out", "define requirements for agents", "how to write kits for AI"