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Ai agent mcp

Skill Skryx-L-A/project-kit/skills/ai-agent-mcp

Say "new project" → get a perfectly-prepared project folder. A Claude Code bootstrap kit that grills the plan to a Definition of Ready, then auto-scaffolds files, memory, project sub-agents & tooling — routing to type-specific sub-skills (website, api, data/ml, quant, SaaS, CLI, app, game-mod, research, OSS… + a 7-day build-business ultraskill).

Install
npx -y skills add Skryx-L-A/project-kit --skill ai-agent-mcp

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  • 1 stars1 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

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Build/set up an AI meta-tooling project — a Claude Code skill, a subagent, an MCP server, or an agent app — from the user's grilled answers. Project-kit sub-skill loaded by new-project routing whenever someone wants to build a skill, write a subagent, stand up an MCP server, or ship an agent/LLM app. Chains skill-creator + claude-api, picks the right Claude model, and bakes in an eval plan so triggering and tools are proven, not assumed.

SKILL.md

6.7 KB, as published. Nobody here has run it

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.

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.