Explore Agent Skills

Orchestrate autonomous AI development pipelines through your Kanban board (Asana, GitHub Projects, Linear). Manages multi-worker Claude Code dispatch, deterministic quality gates, adversarial review, per-task cost tracking, and crash-proof pipeline execution.

Tools are how AI agents interact with the world. A well-designed tool is the difference between an agent that works and one that hallucinates, fails silently, or costs 10x more tokens than necessary. This skill covers tool design from schema to error handling.

Translates user intent into a precise, unambiguous specification and requirements.

Proves the system works by writing and executing comprehensive test suites.

Cleans up and improves existing code without changing behavior.

Produces clean, functional code that matches the architecture and checklists.

Reviews code for objective correctness, security, and reliability.

Handles containerization, CI/CD pipelines, and deployment setup.

Designs the data model, API contracts, and structural foundation of the system.

Turns requirements into a precise, dependency-aware implementation plan.

Main agent orchestrator that coordinates a specialized squad of agents

Schedule AI agent runs with cron, loops, or external clocks while avoiding unsafe tight autonomous timers.

Triage failed Agent QA runs with MCP evidence, artifacts, logs, fixed failure categories, confidence, and actionable next steps.

Debug, patch, and verify failed Agent QA runs from MCP evidence, artifacts, logs, and local code without hiding product or infrastructure defects.

Create, edit, validate, and run Agent QA tests, suites, and hooks through MCP or CLI while preserving canonical IDs and schema contracts.

Meta-skill que orquestra todos os agentes do ecossistema. Scan automatico de skills, match por capacidades, coordenacao de workflows multi-skill e registry management.

Optimize multi-agent systems with coordinated profiling, workload distribution, and cost-aware orchestration. Use when improving agent performance, throughput, or reliability.

Systematic improvement of existing agents through performance analysis, prompt engineering, and continuous iteration.

A hybrid memory system that provides persistent, searchable knowledge management for AI agents.

Memory is the cornerstone of intelligent agents. Without it, every interaction starts from zero. This skill covers the architecture of agent memory: short-term (context window), long-term (vector stores), and the cognitive architectures that organize them.

A hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions).

Manage multiple local CLI agents via tmux sessions (start/stop/monitor/assign) with cron-friendly scheduling.

Use when an agent workflow needs deterministic recovery evidence for sandbox, MCP/tool, worker, checkpoint, memory, or orchestration failures.

Build persistent agents on Azure AI Foundry using the Microsoft Agent Framework Python SDK.