Ai agent mcp

ai-agent-mcp — the agent/meta-tooling builder sub-skill

What this sub-skill is for

Standing up tooling that Claude (or another agent) runs: a Claude Code skill (a folder with SKILL.md), a subagent (a .md in .claude/agents/ with frontmatter + system prompt), an MCP server that exposes tools/resources, or a small agent app built on the Anthropic SDK. This is the user's home turf — he builds skills and agents heavily — so the bar is high: correct triggering, validated tools, and evals that prove it.

Mandatory grill-questions (fold into the Definition of Ready)

Lock these before any code:

• Form factor — skill vs. subagent vs. MCP server vs. agent app? They have different shapes, runtimes, and "done" bars. If unsure, decide from how it's invoked (Claude reads a SKILL.md → skill; Claude delegates a scoped job → subagent; a tool surface other clients call → MCP; a standalone program driving the model → agent app).
• Trigger surface (skills/subagents) — what user phrasing or task shape must fire it, and what must NOT? The description line is the trigger; write it to match real intent, avoid false fires.
• Claude model — chain claude-api for current model ids: claude-opus-4-8 (most capable), claude-sonnet-4-6, claude-haiku-4-5-20251001, claude-fable-5. Default to the newest/most capable that fits cost+latency; justify the pick.
• Tools / permissions — exactly which tools the skill/subagent/MCP gets, and the least-privilege set. For MCP: which tools/resources/prompts, their input schemas, and side-effect scope.
• Eval plan — what "works" means as runnable checks (trigger-accuracy cases, golden tool calls, refusal cases). No eval plan = not Ready.
• Distribution — local-only, committed into a repo's .claude/, or published/installable?
• State & secrets — does it need state, files, or API keys? Where do keys live (never committed)?

Project sub-agents to generate (into .claude/agents/)

• skill-author (delegate-by-default) — authors/edits SKILL.md (frontmatter name+description + body) and subagent .md files to the kit's conventions; chains the skill-creator skill.
• eval-runner (delegate-by-default) — builds and runs the eval suite (trigger accuracy, tool-call goldens, variance analysis), reports pass/fail honestly, blocks "done" on red.
• mcp-tool-designer — designs MCP tool/resource schemas, names, and input validation; verifies each tool against the SDK reference and a smoke call.
• prompt-author — writes/tightens the system prompt and description line; reduces false triggers.

Tools / CLIs / MCP / skills needed

Check in environment-readiness; offer install, never auto-install:

• Node 20+ (MCP TypeScript SDK: @modelcontextprotocol/sdk) and/or Python 3.11+ (mcp, anthropic). npx @modelcontextprotocol/inspector to test an MCP server interactively.
• Anthropic SDK — npm i @anthropic-ai/sdk or pip install anthropic; ANTHROPIC_API_KEY for agent apps (ask the user to paste it; never invent).
• CHAIN these GLOBAL skills automatically: skill-creator (author skills/subagents + run/measure evals), claude-api (model ids, params, tool-use, MCP, caching — read it before touching any model id or LLM behaviour), update-config (wire skills/MCP/hooks into settings.json), code-review
  • verify before "done". Use cli-anything if the agent app needs to drive a GUI tool headlessly.
• MCP to chain: the kit's own MCP servers as live references — e.g. n8n (get_sdk_reference), claude-in-chrome / playwright, Supabase — to study real tool-surface design.

File / asset nudges (on top of the base set)

Beyond CLAUDE.md, PROJEKT_<NAME>.md, TASKS.md, DONE.md, README, .claude/:

• Skill: SKILL.md (frontmatter + body), optional references/, scripts/, assets/.
• Subagent: .claude/agents/<name>.md (frontmatter name, description, tools + system prompt).
• MCP server: src/server.ts (or server.py), tool/resource definitions, package.json / pyproject.toml, a mcp.json / install snippet, and .mcp.json registration example.
• Agent app: src/agent.*, a tool registry, a run loop, .env.example (keys, never the real .env).
• evals/ — eval cases + a runner + a results log; this is non-optional here.
• examples/ — sample invocations / transcripts showing the intended trigger and output.

Stack defaults & done-bar

Default stack: skill/subagent = plain Markdown to kit conventions. MCP server = TypeScript on the @modelcontextprotocol/sdk (Python mcp if the surrounding code is Python). Agent app = @anthropic-ai/sdk with the newest capable model (default claude-opus-4-8, drop to claude-sonnet-4-6 for cost/latency), tool-use loop, prompt caching where it pays. Evals via skill-creator's harness.

"Finished/working" means (checkable bar):

• Skill/subagent: triggers on the intended phrasing and stays quiet on near-misses (eval trigger-accuracy passes); frontmatter valid; body follows kit format.
• MCP server: starts; every tool validates against its schema and returns a correct smoke-test result in the Inspector; resources/prompts resolve; registration snippet works in a real client.
• Agent app: runs the loop end-to-end against the live API, calls tools correctly, handles errors/refusals.
• Evals pass (trigger accuracy + tool-call goldens + refusal cases) and are committed.
• code-review + verify clean; README shows install + one real example.

Guardrails

• Evals are the gate — "it triggered once for me" is not done; prove it with the suite.
• Least privilege — give a skill/subagent/MCP only the tools it needs; document why each is granted.
• Never hardcode or guess model ids/pricing/params — read claude-api; default to the newest capable model.
• Secrets never committed — .env.example only; ask the user to supply real keys.
• No emojis in any app/tool UI or output (user's standing rule) — typographic symbols only.
• Honest descriptions — the description line must reflect what the tool actually does and fires on; no over-broad triggers that hijack unrelated requests.
• Commits under the user's name only (Skryx-L-A); never add Claude as co-author.