Loading...
Loading...
Found 14 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).
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.
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.
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.
Create and maintain gjkim_instruction.md, the root document for a loop-engineering effort. The document holds only the minimum requirements and confirmed decisions, and deliberately leaves everything else open so that later iterations are not locked into early guesses. Use whenever the user mentions gjkim_instruction.md, a root document or root doc for a loop, loop engineering, starting a long-running agent loop on a goal, or asks to record a requirement or a confirmed decision for such an effort. Also use at the start of a loop iteration to check what is already decided versus still open.
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.
Find, compare, adapt, and design repeatable AI-agent loops with explicit triggers, actions, verification, stopping conditions, guardrails, and handoffs. Use when a user asks for a loop, recurring agent workflow, automation cadence, iterative improvement process, an existing Loop Library recommendation, or help turning an outcome into a bounded copy-ready loop through a short question-led design session.
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