Ai Agents

iterative-retrieval is a workflow pattern for progressively refining context retrieval in agentic work. It helps subagents avoid too much or too little context, making it useful for iterative-retrieval usage, install decisions, and iterative-retrieval for Workflow Automation.

continuous-agent-loop helps agents run repeatable autonomous loops with quality gates, evals, recovery steps, and clear stop rules for reliable task completion.

autonomous-loops is a skill for designing autonomous Claude Code workflows, from simple sequential pipelines to multi-agent DAG orchestration with quality gates and handoffs.

autonomous-agent-harness turns Claude Code into a persistent, self-directing agent system with memory, scheduled runs, task dispatch, and computer use. It fits agent orchestration, recurring checks, and long-lived workflows when you need more than a one-time prompt.

Learn the agentic-engineering skill for eval-first execution, task decomposition, model routing, and safer workflow automation with regression checks.

The agent-introspection-debugging skill provides a structured self-debugging workflow for AI agent failures: capture the failure state, diagnose likely causes, apply a contained recovery step, and produce a human-readable introspection report. Use it for looping, retry-heavy, or drift-prone runs, not routine verification.

agent-harness-construction is a practical skill for improving agent harness design, including tool schemas, observation formats, error recovery, and context budgeting for stronger completion rates.

subagent-driven-development is a skill for executing implementation plans with a fresh subagent per task, then reviewing each result in two passes: spec compliance first, code quality second. It includes prompt templates for the implementer, spec reviewer, and code quality reviewer.

dispatching-parallel-agents is an Agent Orchestration skill for splitting truly independent tasks across separate agents with isolated context and coordinated results.

The evaluation-methodology skill explains PluginEval scoring for Model Evaluation, including layers, rubrics, composite scoring, badge thresholds, and practical guidance for interpreting results and improving weak dimensions.

prompt-engineering-patterns is a practical skill for production prompt design, covering install context, reusable templates, few-shot examples, structured outputs, and prompt optimization workflows for Context Engineering.

langchain-architecture is a design guide for building LangChain 1.x and LangGraph applications. Use it to choose between chains, agents, retrieval, memory, and stateful orchestration patterns before implementation.

team-composition-patterns is a decision skill for designing multi-agent teams in Claude Code Agent Teams, with sizing heuristics, subagent_type selection, and display mode guidance. Use this team-composition-patterns guide to apply preset review/debug teams, choose roles, and standardize team setup across tasks.

The parallel-feature-development skill helps teams split one feature into clear ownership groups, define shared contracts early, and choose safer merge patterns for multi-agent Git workflows. Use it to plan file ownership, dependency-aware integration, and lower-conflict parallel implementation.

task-coordination-strategies helps teams decompose complex work, map dependencies, define acceptance criteria, and coordinate parallel agent or contributor workflows with clearer ownership and fewer merge conflicts.

team-communication-protocols defines messaging rules for agent teams, covering direct vs broadcast messages, plan approval, shutdown procedures, and reusable templates for coordinated Agent Orchestration.

agentic-eval is a GitHub Copilot skill that shows how to build evaluation loops for AI outputs using reflection, rubric-based critique, and evaluator-optimizer patterns.

agent-governance is a documentation-first skill for designing AI agent guardrails, policy checks, trust rules, tool restrictions, and audit logging for tool-using and multi-agent systems.

The context-engineering skill helps you structure project context so agents follow conventions, reduce hallucinations, and stay focused. Use it when starting a session, switching tasks, or building a context-engineering guide for a codebase.

Learn what the pua skill does, how pua usage works, and what to review before install. Covers trigger logic, workflow routing, reference files, escalation paths, and setup limits for Workflow Automation.

p9 is a tech-lead style skill for Agent Orchestration that writes task prompts, coordinates P8 agents, and avoids direct coding. Use it to split project goals into scoped, executable prompts with roles, constraints, dependencies, and acceptance criteria.

ai-shaped-readiness-advisor helps product leaders assess whether their org is AI-first or AI-shaped, identify maturity gaps, and choose the next capability to build for better decision support.

agent-framework-azure-ai-py is a skill for building persistent Azure AI Foundry agents with the Microsoft Agent Framework Python SDK. It covers agent-framework-azure-ai-py install and usage, AzureAIAgentsProvider setup, threaded conversations, hosted tools, MCP integration, streaming runs, and structured outputs for agent orchestration.

do-in-steps helps an agent tackle complex tasks by splitting work into ordered subtasks, orchestrating sub-agents, and verifying each step before moving on. It is a strong fit for repository changes, multi-step refactors, migrations, and do-in-steps for Agent Orchestration when you need controlled handoff and fewer silent failures.