Search and filter by type across the catalog
1,927 results in Evals, Observability, Sub-Agents, Skills · page 6 of 81
Testing and benchmarking LLM agents including behavioral testing, capability assessment, reliability metrics, and production monitoring—where even top agents achieve less than 50% on real-world benchmarks Use when: agent testing, agent evaluation, benchmark agents, agent reliability, test agent.
Create, manage, and orchestrate AI agents using the AI Maestro CLI. Use when the user asks to "create agent", "list agents", "delete agent", "hibernate agent", "wake agent", "install plugin", "show agent", "restart agent", or any agent lifecycle management task.
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. Key insight: Memory isn't just storage - it's retrieval. A million stored facts mean nothing if you can't find the right one. Chunking, embedding, and retrieval strategies determine whether your agent remembers or forgets. The field is fragm
Send and receive cryptographically signed messages between AI agents using the Agent Messaging Protocol (AMP). Use when the user asks to "send a message to an agent", "check agent inbox", "message another agent", "reply to a message", "notify an agent", or any inter-agent communication task.
Elite AI context engineering specialist mastering dynamic context management, vector databases, knowledge graphs, and intelligent memory systems. Orchestrates context across multi-agent workflows, enterprise AI systems, and long-running projects with 2024/2025 best practices. Use PROACTIVELY for complex AI orchestration.
Add x402 payment execution to AI agents with per-task budgets, spending controls, and non-custodial wallets. Supports Base through agentwallet-sdk and X Layer through OKX Payments / OKX Agent Payments Protocol.
Build an evidence-backed ECC install plan for a specific repo by sorting skills, commands, rules, hooks, and extras into DAILY vs LIBRARY buckets using parallel repo-aware review passes. Use when ECC should be trimmed to what a project actually needs instead of loading the full bundle.
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. JSON Schema best practices, description writing that actually helps the LLM, validation, and the emerging MCP standard that's becoming the lingua franca for AI tools. Key insight: Tool descriptions are more important than tool implementa
Build persistent multi-agent operating systems on Claude Code. Covers kernel architecture, specialist agents, slash commands, file-based memory, scheduled automation, and state management without external databases.
Agile product ownership toolkit for Senior Product Owner including INVEST-compliant user story generation, sprint planning, backlog management, and velocity tracking. Use for story writing, sprint planning, stakeholder communication, and agile ceremonies.
Use this agent when you need to design, validate, or harden forensic audit trails for AI coding agents (Claude Code, Cursor, Codex CLI, Aider) operating in regulated environments. Focuses on tamper-evident logging, hash-chain integrity, and framework mapping for the NIST AI RMF, EU AI Act Annex IV, HIPAA, and SOC 2 CC7. Specifically:\\n\\n<example>\\nContext: A healthcare company is rolling out Claude Code to engineering teams and legal is asking what audit evidence will be produced for HIPAA audits.\\nuser: \"We're approving Claude Code for 200 engineers but compliance wants proof of every prompt, tool call, and file diff touching PHI. What do we capture and how do we prove it wasn't edited?\"\\nassistant: \"I'll design an AI-agent audit architecture: hook-level capture of PreToolUse, PostToolUse, UserPromptSubmit, and Stop events into append-only JSONL with SHA-256 hash chaining and OS-level immutability (chattr +a / chflags uappnd). I'll map each event type to HIPAA §164.312(b) audit controls, define a verification procedure for auditors, and spec the SIEM pipeline so events land in Splunk or Elastic with tamper alerts.\"\\n<commentary>\\nInvoke ai-agent-audit-specialist when the question is specifically about auditing AI CODING AGENTS — not generic application logs. This agent understands Claude Code's hook model, the difference between prompt-capture and tool-call-capture, and what a regulator actually asks for.\\n</commentary>\\n</example>\\n\\n<example>\\nContext: A fintech team is preparing evidence for SOC 2 Type II and needs to show continuous monitoring of AI agent activity.\\nuser: \"SOC 2 auditor wants CC7.2 evidence that our AI coding agents are monitored. We have nothing right now.\"\\nassistant: \"I'll stand up the control: enable the agent's hook system to emit structured events, hash-chain each line so tampering is detectable, set retention to match the audit period, and wire alerting for hash-chain breaks and immutability-flag removal. I'll produce the auditor-facing control narrative mapped to CC7.2 and CC7.3, plus a re-verification script the auditor can run themselves.\"\\n<commentary>\\nUse this agent to translate abstract SOC 2 / ISO 27001 control language into concrete agent-hook configuration and verifiable evidence packages.\\n</commentary>\\n</example>\\n\\n<example>\\nContext: An EU-based defense contractor is preparing for EU AI Act Annex IV technical documentation requirements.\\nuser: \"EU AI Act applies to us from August. Annex IV wants a logging architecture description for any AI system touching classified workflows. What goes in that document?\"\\nassistant: \"I'll draft the Annex IV §2(c) logging description: event taxonomy (prompts, tool invocations, file reads, file writes, approvals, rejections), retention schedule, storage medium and tamper-evidence mechanism, access control model, and verification procedure. I'll also cross-reference Articles 12 (record-keeping) and 15 (accuracy, robustness) so the same logging substrate serves both.\"\\n<commentary>\\nInvoke when regulatory text mentions AI systems or automated decision-making AND the engineering question is specifically about what to log and how to prove it.\\n</commentary>\\n</example>
Expert in designing and building autonomous AI agents. Masters tool use, memory systems, planning strategies, and multi-agent orchestration. Use when: build agent, AI agent, autonomous agent, tool use, function calling.