Browse reusable Agent Skills, each with a clear purpose and practical guidance.
The trust layer for agent-to-agent commerce — natural-language mandates, delegated permissions (ERC-7710), x402 payments, escrow, and dispute resolution as one open, catch-all skill. Use for agent mandates, delegated permissions, GenLayer supervision, revocation, x402 payments, escrow, verification, dispute resolution, and agent-to-agent commerce.
Detect local hardware (RAM, CPU, GPU/VRAM) and recommend the best-fit local LLM models with optimal quantization, speed estimates, and fit scoring.
Create, update, validate, or reorganize repo-local Agent Skills under .agents/skills, including SKILL.md frontmatter, first-level references, compatibility symlinks, and validation scripts. Use only for skill-tree maintenance, skill taxonomy changes, or agent-guidance validation work.
Internal guidance for composing Codex and GPT-5.4 prompts for coding, review, diagnosis, and research tasks inside the Codex Claude Code plugin
Troubleshoot and configure Reasonix capabilities: Skills (project/custom/global/builtin priority, discovery dirs), Commands (override order, /dir:file naming), Hooks (11 events, project trust, matchers, timeouts), MCP (reasonix.toml + .mcp.json + plugin packages, auto_start), plugin packages (native/Codex/Claude manifests), and AGENTS.md / instruction docs. Use when the user asks how to configure, debug missing skills/commands/hooks/MCP/plugins, or diagnose capability loading.
Authoring playbook for building agents that automate recurring internal tasks — running scheduled workflows, syncing data between systems, posting notifications, processing inbound events, or executing operational runbooks. Use this when the user wants an agent that runs on a schedule, reacts to events, automates a process, syncs between tools, or handles ops/internal infrastructure.
Analyzes your local Copilot CLI sessions for dotnet/maui to drive iterative improvements to the PR-review agent (and other agents, skills, and instruction files). Runs a select → extract → score → judge → cluster → propose → emit-eval loop: a deterministic core ranks your worst / most-expensive sessions, then the agent rubric-tags recurring failure modes, proposes concrete repo edits, and emits a vally guard-eval per failure mode so each one becomes a regression test. Triggers on: "analyze my recent maui sessions", "what's making my agent runs expensive", "find failure modes in my Copilot sessions", "turn my session failures into guard evals". LOCAL-ONLY — never uploads, shares, or posts transcripts. Do NOT use for: reviewing a single PR (use pr-review), running tests, or analyzing a GitHub issue.
Analyzes a completed PR to extract lessons learned from agent behavior. Use after any PR with agent involvement - whether the agent failed, succeeded slowly, or succeeded quickly. Identifies patterns to reinforce or fix, and generates actionable recommendations for instruction files, skills, and documentation.