Skillit bootstrap
Skill pradeepmouli/skillit/packages/client/skills/skillit-bootstrap
Generate AI agent skills (SKILL.md) from TypeScript API docs — TypeDoc plugin + CLI + Docusaurus/VitePress
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Bootstrap an AI-agent skill from a TypeScript codebase by running the deterministic skillit generate/audit loop and enriching repo source (JSDoc, README, config-type properties, MCP tool annotations, examples, package.json) until the skill reaches its grade target. Use for cli, typedoc, config, or mcp (build-mode) projects; never edit SKILL.md/references directly.
SKILL.md
6.5 KB, as published. Nobody here has run it
skillit-bootstrap
Bootstrap a high-quality AI-agent skill from a TypeScript codebase. You run the deterministic skillit pipeline, read its machine-readable findings, and enrich the repo source (JSDoc, README, examples, package.json) until the generated skill reaches its grade target. skillit owns the skill output; you own the inputs.
The one hard rule
Never create or edit any SKILL.md or references/*.md for the target
project. Those are pure outputs of skillit gen — regenerated every pass. You
edit only repo source surfaces. If you find yourself about to write a
SKILL.md, stop: the fix belongs in the source the skill is generated from.
When to use
- A
cli(Commander),typedoc(TypeScript library),config(a TypeScript config type), ormcp(an MCP server whose TS source you own — "build mode") project that needs a generated agent skill, or whose skill scores below its grade target. - Run after the project is set up with the right
@skillit/*package (see step 1). - Third-party MCP servers you cannot edit are out of scope here: with no
editable source, the skill is produced from an overlay ("runtime mode") via
skillit refine. This loop targets the build-mode (own-source) path. Seereferences/surface-routing.md.
Inputs
/skillit-bootstrap [--source cli|typedoc|config|mcp] [--program <file#export>]
[--config-type <file#export>] [--mcp <path>] [--server <name>]
[--out <dir>] [--grade A|B|C] [--max-iterations <n>]
[--ground <glob>...]
--source— override detection (cli,typedoc,config, ormcp).--program— Commander program entry for the cli source (./dist/cli.js#program).--config-type— config type entry for the config source (./src/config.ts#MyConfig).--mcp— path tomcp.json/ MCP config file (mcp source).--server— MCP server entry to select when the config lists several (mcp source).--out— skill output dir (defaultskills).--grade— override the kind-aware target (below).--max-iterations— hard cap on enrich/regenerate passes (default 5).--ground <glob>— consumer/implementation code you MUST read before writing any runtime-behavior pitfall, so your claims reflect real behavior, not guesses.
The loop
- Set up once. Determine the source kind (honor
--source, else infer:commander/yargsdep → cli;@modelcontextprotocol/sdkdep → mcp; otherwise a TS library → typedoc).configis never auto-detected — select it explicitly with--config-type <file#export>. Each kind has its own selector: cli →--program, config →--config-type, mcp →--mcp(+ optional--server); typedoc needs none. If the project has no@skillit/*package installed yet, runskillit init --source <kind>once (it installs + wires only; it does not generate). - Generate. Run
skillit gen --source <kind> <selector> [--out …](the selector is the kind's from step 1). This deterministically produces the skill from current source. Never hand-edit its output. (For mcp,genspins up the server to introspect it, so the source skill is a function of a deterministic server.) - Audit. Run
skillit audit --source <kind> <selector> --jsonand read the JSON:estimate.grade,estimate.dimensions(D1–D8), andimprovements[]. Each improvement carriessuggestion,dimension,targets: [{file, name, kind}], and (when resolvable)resolvedLocations[]pointing at the exact file + declaration to edit. These targets are your work queue. - Enrich the source. For each addressable finding, open the cited source
file and write the missing content on the named symbol/section/key, choosing
the surface the suggestion names. The surface → audit-code map and the
edit-vs-create guidance are in
references/surface-routing.md. Before writing any runtime-behavior pitfall, read the relevant implementation (--groundglobs) — do not invent semantics from a type signature.- Prefer the
upsertJsDocTag/upsertPropertyJsDocTaghelpers (exported from@skillit/core) for JSDoc-tag writeback rather than free-hand splicing — they handle*/escaping and multi-line prefixing. - You may also create a new parseable artifact when that is the better
home (a
docs/<guide>.md, anexamples/<name>.ts, a missing README section) — but only of a type an existing parser already consumes, and never aSKILL.md.
- Prefer the
- Regenerate + re-audit. Re-run step 2 then step 3. Compare the new
estimateto the previous pass. - Decide convergence (your judgment, using these signals):
- Pass —
estimate.grade≥ the target. Default target is kind-aware: typedoc/library → A (every export is introspectable); cli adapter-model → B, config → B, mcp → B — these surfaces structurally cap below A (a cli command tree isn't enumerated per-symbol; a config type has no functions/params, so per-option routing + one example file is its ceiling; mcp reaches A only if every tool handler carries full JSDoc).--gradeoverrides. - Plateau — the score did not rise AND every remaining finding targets a symbol that genuinely has nothing more to truthfully say. You can see the source, so distinguish "legitimately complete" from "stuck re-drafting" — stop on the former.
- Cap — never exceed
--max-iterations(default 5).
- Pass —
- Report. Print the final grade, the D1–D8 breakdown, the list of source files you enriched, and any remaining findings you judged un-addressable with a one-line rationale each. This is the human review surface; the user commits the result.
After convergence
Tell the user to review the enriched source diffs and the regenerated skill,
then commit. Remind them the skill is reproducible: skillit gen on the same
source yields byte-identical output, so the source diff is the real change.