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Found 11 Skills
Patterns for continuous autonomous agent loops with quality gates, evals, and recovery controls.
Agentic workflow patterns for autonomous LLM reasoning. Use when building ReAct agents, implementing reasoning loops, or creating LLMs that plan and execute multi-step tasks.
Monitor running agent loops, triage failures, clean up after completion, and decide when to intervene. Use when a loop is running and needs babysitting, when a loop just finished and needs post-merge verification, when stories are skipping/failing and need diagnosis, or when stale test artifacts need cleanup. Triggers on: 'check the loop', 'what happened with the loop', 'loop finished', 'clean up after loop', 'why did that story skip', 'monitor loop', 'nanny the loop', or any post-start loop management task. Distinct from agent-loop skill (which handles starting loops).
(Industry standard: Routing Agent / Orchestrator Pattern) Primary Use Case: Analyzing an ambiguous trigger and routing it to one of the specific specialized implementations. Routes triggers to the appropriate agent-loop pattern. Use when: assessing a task, research need, or work assignment and deciding whether to run a simple learning loop, red team review, dual-loop delegation, or parallel swarm. Manages shared closure (seal, persist, retrospective, self-improvement).
Autonomous agents are AI systems that can independently decompose goals, plan actions, execute tools, and self-correct without constant human guidance. The challenge isn't making them capable - it's making them reliable. Every extra decision multiplies failure probability. This skill covers agent loops (ReAct, Plan-Execute), goal decomposition, reflection patterns, and production reliability. Key insight: compounding error rates kill autonomous agents. A 95% success rate per step drops to 60% b
This skill should be used when a developer wants to autonomously execute all tasks under a fully-specified Epic or Feature — for example "go", "start building", "implement everything", "run the loop", "execute the feature", "build it all", "kick it off". Requires that the Epic/Feature/Task tree is fully written before starting. Chains implement → verify → PR for every task in dependency order, with targeted human-in-the-loop gates for contradictions and ambiguities.
Agent orchestration patterns for agentic loops, multi-agent coordination, alternative frameworks, and multi-scenario workflows. Use when building autonomous agent loops, coordinating multiple agents, evaluating CrewAI/AutoGen/Swarm, or orchestrating complex multi-step scenarios.
Use whenever the user mentions LLM prompt/prefix cache misses, cached_tokens=0, cache_read_input_tokens/cache_creation_input_tokens, prompt_cache_key, cache_control/cachePoint placement, stable prefixes, tool/schema stability, TTFT/prefill latency, OpenAI/Claude/Bedrock/OpenRouter routing, vLLM/SGLang KV reuse, or LLM cost/speed regressions on repeated long prompts. Use when reviewing LLM request shape changes: prompt text, message order, request builders, tools, schemas, response_format, provider API surface, model/router settings, agent loop structure, context compaction, or inference deployment. Use for speeding up agents only when prompt-cache stability, TTFT, or cache cost is central. Do not use for generic prompt writing, generic RAG design, token counting, or non-LLM performance.
Patterns and architectures for autonomous Claude Code loops — from simple sequential pipelines to RFC-driven multi-agent DAG systems.
Run a spec-driven agent loop where coding tasks live as markdown specs that move through inbox → active → archive, get implemented by Claude Code or Codex, and pass a review gate before they count as done. Use when the user mentions "loop factory", a "spec-driven loop", an "agent factory", wants repeatable/reviewable agent work, or when a repo has a factory/specs/inbox or factory/specs/active directory. Also covers installing and scaffolding the loop-factory CLI into a project.
Set up, supervise, and control a persistent multi-layer "explore → execute → escalate" agent loop on a project. Use whenever a user asks to keep an agent running on a task across sessions or days — finding bugs, polishing writing, distilling a style, watching feeds, scanning for gaps, or any task whose value grows with how many findings the agent produces. Also use when the user wants to inspect, pause, resume, stop, or send a new instruction to an already-running perpetuum task.