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Found 82 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.
Symphony turns project work into isolated, autonomous implementation runs, allowing teams to manage work instead of supervising coding agents.
CrewAI task design and configuration. Use when creating, configuring, or debugging crewAI tasks — writing descriptions and expected_output, setting up task dependencies with context, configuring output formats (output_pydantic, output_json, output_file), using guardrails for validation, enabling human_input, async execution, markdown formatting, or debugging task execution issues.
Use when any Maestro command is invoked — provides foundational workflow design principles across prompt engineering, context management, tool orchestration, agent architecture, feedback loops, knowledge systems, and guardrails.
Research how to implement a phase (standalone - usually use COMMAND PREFIX plan-phase instead)
Set up Jetty for the first time. Guides the user through account creation, API key configuration, and introduces runbooks — human-readable markdown files that tell an agent how to accomplish multi-step tasks with measurable outcomes. Use this skill whenever the user wants to set up, configure, or get started with Jetty — including 'set up jetty', 'configure jetty', 'jetty setup', 'get started with jetty', 'install jetty', 'connect to jetty', 'jetty onboarding', 'I am new to jetty', 'how do I start with jetty', or even just 'jetty' if they do not appear to have a token yet. Also trigger if the user mentions needing an API key for Jetty or storing their OpenAI/Gemini key in Jetty.
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