1.0.1 • Published 17h ago

@skillit/target-fastmcp

Licence

MIT

Version

1.0.1

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Size

39 kB

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Weekly

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Summary Dependency Versions

skillit

skillit — Compile-time generator of AI agent skills from your codebase.

Inline docs, CLI definitions, config schemas, and examples compile into progressively disclosed SKILL.md files that any LLM can discover. Integrated with TypeDoc, with support for conventional repo docs, and plugins for Docusaurus and VitePress provided for what code can't cover.

MCP Servers (orthogonal workflow)

skillit can also generate skills from any live MCP server, even when the server was not authored with skillit.

This is separate from the TypeDoc/docs extraction flow above: it introspects MCP tools/resources/prompts over stdio or HTTP and emits a progressive-disclosure SKILL.md. You can render:

native MCP launch instructions (mcp: frontmatter in SKILL.md, which tells MCP-capable agents how to start/connect to the server), or
CLI-proxy launch instructions for non-MCP harnesses (such as mcpc/fastmcp).

# Inspect any running or launchable MCP server and generate skills
npx skillit mcp extract \
  --command "npx -y @modelcontextprotocol/server-filesystem /tmp" \
  --out ./skills

# Optional: install non-default CLI invocation adapters
npm install --save-dev @skillit/target-mcpc @skillit/target-fastmcp

# Emit CLI-proxy invocation variants for non-MCP agents
npx skillit mcp extract \
  --command "npx -y @modelcontextprotocol/server-filesystem /tmp" \
  --invocation cli:mcpc \
  --invocation cli:fastmcp \
  --out ./skills

For server package authors, skillit mcp bundle can be run in your build to ship pre-generated skills with your MCP package.

See packages/mcp/README.md for install, extract, bundle, config-file batch mode (mcp.json / claude_desktop_config.json), and programmatic API details.

Bootstrap (recommended): `/skillit-bootstrap`

The primary way to create or improve a skill is the /skillit-bootstrap Claude Code skill (bundled with @skillit/client). It runs the deterministic generate → audit loop and lets the agent enrich your repo's source (JSDoc, README, examples, package.json) until the skill hits its grade target — you never hand-edit a SKILL.md.

# Install the bundled skill into your user skill roots (one time)
mkdir -p ~/.claude/skills ~/.copilot/skills ~/.agents/skills
cp -R node_modules/@skillit/client/skills/skillit-bootstrap ~/.claude/skills/
cp -R node_modules/@skillit/client/skills/skillit-bootstrap ~/.copilot/skills/
cp -R node_modules/@skillit/client/skills/skillit-bootstrap ~/.agents/skills/

# Then, in your agent:
/skillit-bootstrap --source cli --program ./dist/cli.js#program
/skillit-bootstrap --source typedoc

Supported sources this release: cli and typedoc. For config / mcp, use skillit refine (below); slash-command support for those lands in a later phase. The CLI commands (skillit gen, skillit audit --json, skillit refine) remain for headless/CI use.

Init: detect → install

skillit init wires a project up. It detects the project's nature and installs the matching @skillit/* package with your package manager, then points you at skillit gen to produce the skill. It does not generate or refine — those are separate, explicit commands (skillit gen, skillit refine).

# Auto-detects nature and package manager, installs the right @skillit package
npx skillit init

# Force the source kind
npx skillit init --source mcp

# Config source is built in (no install) — init just points at `skillit gen`
npx skillit init --source config --config-type ./src/config.ts#MyConfig

Detection:

Nature — commander / yargs dep → cli; @modelcontextprotocol/sdk → mcp; otherwise a plain TS library → typedoc. Override with --source.
Package — cli → @skillit/cli, mcp → @skillit/mcp, typedoc → typedoc-plugin-skillit. The config source is built into the CLI (no install).
Package manager — pnpm-lock.yaml → pnpm, yarn.lock → yarn, else npm.

If the install step fails, init prints the exact add command and stops.

Flag	Default	Description
`--source <cli\|mcp\|typedoc>`	auto	Override project-nature detection
`--config-type <file#export>`	—	Config type entry (config source)

Gen: deterministic skill generation

skillit gen (re)generates the skill from the current source — no install, no model, no network. It is the deterministic, side-effect-free generate primitive: the same source always yields the same skill. Run it after init, and again whenever you change the documented source (JSDoc, config type, README).

# Generate from the auto-detected source into skills/
npx skillit gen

# CLI source with an explicit commander program entry
npx skillit gen --source cli --program ./dist/cli.js#program

# Config source
npx skillit gen --source config --config-type ./src/config.ts#MyConfig

# Generate into a custom directory (default: skills)
npx skillit gen --out docs/skills

Flag	Default	Description
`--source <cli\|config\|mcp\|typedoc>`	auto	Source kind
`--program <file#export>`	—	Commander program entry (cli source)
`--config-type <file#export>`	—	Config type entry (config source)
`--mcp <path>`	—	MCP config path (mcp source)
`--server <name>`	—	MCP server entry (mcp source)
`--out <dir>`	`skills`	Output directory for the generated skill

Audit: score + findings as JSON

skillit audit runs the same audit + judge the refine loop uses and prints the result. With --json it emits the full AuditResult + score estimate, plus a resolved on-disk location for each improvement target — the machine-readable read-surface an agent (or CI) can act on without re-deriving anything.

# Human summary (grade + severity counts)
npx skillit audit --source cli

# Full machine-readable report
npx skillit audit --source config --config-type ./src/config.ts#MyConfig --json

Flag	Default	Description
`--source <cli\|config\|mcp\|typedoc>`	auto	Source kind
`--program <file#export>`	—	Commander program entry (cli source)
`--config-type <file#export>`	—	Config type entry (config source)
`--mcp <path>`	—	MCP config path (mcp source)
`--server <name>`	—	MCP server entry (mcp source)
`--json`	off	Emit the full audit + estimate as JSON

Refine: autonomous annotation loop

skillit refine runs an audit → draft → review loop that iteratively improves the useWhen / avoidWhen annotations in your generated skills. On each pass it asks an LLM to evaluate the current guidance, proposes improvements, and applies them — no manual editing required.

Refine is source-aware. It auto-detects the source from the installed @skillit/* package, or you can choose explicitly:

# CLI source — writes guidance back into the *Options interface JSDoc
npx skillit refine --source cli --program ./dist/cli.js#program

# MCP source (see modes below)
npx skillit refine --source mcp --mcp ./mcp.json

For the cli source, refine writes annotations into the JSDoc of your typed *Options interface (e.g. GenerateOptions), correlated to each command's flags.

Two modes depending on whether you own the server's source:

Build mode — for TypeScript MCP servers you own. refine writes annotations directly into source as _meta fields on each server.tool(...) call:

server.tool(
  'read_file',
  {
    description: 'Read a file',
    _meta: { useWhen: 'After listing a directory to inspect a specific file' }
  },
  schema,
  handler
);

# Auto-detected when @modelcontextprotocol/sdk appears in package.json
npx skillit mcp refine

# Explicit
npx skillit mcp refine --mode build

Runtime mode — for any MCP server, including ones you don't own. refine writes an overlay JSON file that extract / bundle merges at render time:

# Auto-detected when --mcp points to mcp.json / claude_desktop_config.json
npx skillit mcp refine --mcp ~/.config/claude/mcp.json

# Refine only a subset of tools
npx skillit mcp refine --mode runtime --mcp ./mcp.json --items filesystem,github

Auto-detection checks for an SDK dependency (build signal) and a runtime config path (runtime signal). When both are present the command is ambiguous — pass --mode explicitly.

Key flags:

Flag	Default	Description
`--source <cli\|mcp\|typedoc>`	auto	Refine source (auto-detected from installed package)
`--program <file#export>`	—	Commander program entry (cli source)
`--mode build\|runtime`	auto	Override auto-detection
`--mcp <path>`	—	Path to `mcp.json` or `claude_desktop_config.json`
`--source-glob <glob>`	`*/.ts`	Glob for TypeScript files to scan (build mode)
`--max-iterations <n>`	`5`	Iteration cap for the audit→draft→review loop
`--items <n>`	`5`	Work items per iteration
`--model-client <kind>`	`api`	Model backend: `api` (ANTHROPIC_API_KEY) or a CLI: `claude` / `codex` / `copilot`
`--model-cli-timeout <ms>`	`120000`	Per-call timeout for CLI model backends

CLI model backends. Instead of the Anthropic API, refine (and init) can drive the loop through an already-authenticated agent CLI — --model-client claude, codex, or copilot. The drafter/reviewer prompts are identical; only the transport changes. claude maps the drafter/reviewer split to Sonnet/Opus via --model; codex/copilot use their configured default model. Each CLI must be installed and authenticated. Note: copilot prioritizes a GH_TOKEN/GITHUB_TOKEN environment variable over its /login credential — if that token lacks the "Copilot Requests" permission, unset it so copilot uses your login. On Windows the CLIs are launched through the shell to support .cmd shims; the prompt is always piped via stdin (never passed as a command argument), so untrusted content never reaches the command line.

Why Inline?

When an agent updates your code, inline docs update atomically. There's no separate file to remember, no coordination problem, no drift. The agent edits ONE location and the truth propagates mechanically.

/**
 * Parse a configuration file.
 *
 * @useWhen
 * - Loading config from user-provided paths
 * - Dynamic config resolution at startup
 *
 * @never
 * - NEVER trust user paths without sanitization — resolves relative to cwd
 *
 * @param path Path to the config file
 * @returns Parsed and validated configuration
 */
export function loadConfig(path: string): Config { ... }

pnpm typedoc → the generated skill tells every LLM when to use this function, what to watch out for, and how to call it.

Configuration (new approach)

skillit now centralizes routing in skillit.config.ts (scaffolded by skillit init):

import { defineConfig } from '@skillit/client';

export default defineConfig({
  skillDir: 'skills',
  plugins: {
    cli: { skillDir: 'skills/cli' },
    typedoc: { maxTokens: 4000 }
  }
});

Use skillDir for global output defaults and plugins.<source> overrides for source-specific output/token behavior (cli, config, mcp, typedoc).

Plugin/content-source routing matrix

Plugin surface	Primary content source(s)	Additional content source(s)
`@skillit/core`	Shared renderer + writer + token budgeting	README/docs/examples scanners
`@skillit/cli`	Commander/yargs command trees + flags	Typed option-interface JSDoc correlation
`@skillit/typedoc` / `typedoc-plugin-skillit`	TypeScript API + JSDoc tags	package.json metadata + optional docs/examples
`@skillit/vitepress`	VitePress sidebar/doc pages	Merged with typedoc/core output in one skill
`@skillit/docusaurus`	Docusaurus docs + `_category_.json`	Merged with typedoc/core output in one skill
`@skillit/mcp` (bundled)	MCP server introspection + invocation adapters	Package build metadata (`skillit.mcp`)
`@skillit/mcp` (runtime)	MCP runtime config (`mcp.json` / `claude_desktop_config.json`)	Runtime overlay JSON from refine

Use-case routing matrix

Use case	Primary plugin/source	Additional source(s)
Monorepo / multi-use-case routing	`typedoc` + `skillit.config.ts` plugin overrides	`core` docs/examples scanning
CLI tool	`cli` (`skillit gen --source cli`)	`core` + typed options JSDoc
API/library	`typedoc` (`typedoc-plugin-skillit`)	`core`, optionally `vitepress`/`docusaurus`
MCP server (owned, build mode)	`mcp` (`skillit gen/audit/refine --source mcp`)	`target-mcp-protocol` / `target-mcpc` / `target-fastmcp`
MCP server (third-party, runtime mode)	`mcp` runtime (`--mcp <config>`)	Runtime overlay from `skillit refine`

Package readers

For package-level details and options, use the package READMEs directly:

Examples

See the examples/ directory for runnable scripts:

basic-skill-generation.ts — generate a SKILL.md from an ExtractedSkill object
audit-and-fix.ts — run the documentation audit and print results
cli-extraction.ts — extract skills from a commander program
docs-scanning.ts — include prose docs alongside API skills

Case Study: PixiJS (47K stars)

We forked PixiJS and bootstrapped skillit to measure the before/after impact on generated skill quality, scored against the skill-judge rubric (120 points, 8 dimensions).

Results

Phase	Score	Grade	What Changed	Agent Cost
Install + generate	84/120	B-	`npm install typedoc-plugin-skillit && pnpm typedoc`	0 tokens
After JSDoc conventions	113/120	A	`@useWhen`/`@never` on 7 key classes (110 lines of JSDoc)	~80K tokens

B- → A with 110 lines of JSDoc annotations. The generator handles structure, progressive disclosure, config detection, and reference splitting automatically. The annotations add the expert knowledge — when to use each class, what to never do, and why.

What the agent wrote (110 lines, ~80K tokens)

// src/scene/sprite/Sprite.ts — added to existing JSDoc
/**
 * @useWhen
 * - Displaying images, texture regions, or sprite sheets
 * - You need fast batched rendering of many images
 * @avoidWhen
 * - Drawing dynamic shapes — use Graphics instead
 * - Rendering text — use Text or BitmapText
 * @never
 * - NEVER create Sprites from unloaded textures — always Assets.load() first
 * - NEVER use Sprite.from() in hot loops — it creates new textures each call
 */

Similar annotations on Application, Container, Graphics, Text, Assets, and AbstractRenderer. The @packageDocumentation block added 6 NEVER rules covering v8 migration pitfalls.

Generated output (224 reference files)

skills/pixi-js/
  SKILL.md (343 lines)
  references/
    classes/
      scene/                    # Per-class files + index.md
        container.md, sprite.md, graphics.md, ...
      rendering/                # 80+ renderer system classes
        abstractrenderer.md, webglrenderer.md, ...
      text/, assets/, events/, filters/, maths/, ...
    functions.md, types.md, config.md, variables.md
    architecture.md, scene-graph.md, render-loop.md
    performance-tips.md, garbage-collection.md
    v5-migration-guide.md ... v8-migration-guide.md

Fork: pradeepmouli/pixijs

Ecosystem

agentskills.io — the SKILL.md specification
skills.sh — skill registry and CLI (npx skills add)
llmstxt.org — the llms.txt specification

License

MIT

Keywords

agent-skills ai fastmcp mcp model-context-protocol skillit