skills-collections

Build automated evaluation suites for AI agents using golden datasets, rubrics, and regression gates. Use when shipping agent features, validating prompt changes, or gating deployments on quality.

Evaluate agent behavior with versioned cases and explicit verifiers. Use when comparing agent or prompt changes, reproducing failures, or running agent regression tests.

Use when summarizing agent evaluations where autonomous, assisted, failed, timed-out, or invalid outcomes must remain distinct and comparable.

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

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

Manage multiple local CLI agents via tmux sessions (start/stop/monitor/assign) in an explicitly authorized workspace.

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

Rules for when an agent should recall from long-term memory before acting and when it should save decisions, corrections and failures afterwards. Works with any memory backend.

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

Design tenant-scoped memory, retrieval and retention for agents. Use when planning RAG, conversation history, vector stores or remembering across sessions.

Instrument AI agents with tracing, token metrics, latency, and cost visibility. Use for reliability and debugging.

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

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

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

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

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

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

Find animation and video references on 1human, consult a human-agent shared library, and contribute public creations only with prior owner permission.

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

Main agent orchestrator that coordinates a specialized squad of agents