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Found 82 Skills
A 10-step methodology for building software with AI collaboration - from north star through automated Ralph loop execution with zero human-in-the-loop code writing
Define the design rules (Skill Laws) that all Skills must follow, including core principles such as AI-first, human-centric, and ready-to-use. When to use: When users create a new Skill, optimize an existing Skill, ask about Skill design specifications, or need to evaluate Skill quality.
AI agent workflow with interview-driven planning and team-based execution. Use /design to start planning, /work to execute.
Clear conversation context while preserving knowledge via context marker. Use when user says "clear context", "start fresh", "done with this task", or when approaching token limits.
Extract standalone snippets from newsletters or blog posts and route to social platforms. Posts suggestions to
Ensure AI agents work in an isolated Git worktree to prevent changes to the main working directory. Use when AI is about to make its first code modification in a session, or when the user requests isolated/safe editing. Triggers include starting to edit files, implementing features, or fixing bugs.
File-based knowledge persistence patterns: when to store discoveries, when to recall past solutions, and how to organize project memory. Activate when starting tasks, encountering errors, making decisions, or when context may be lost between sessions.
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.