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Init ai

Skill Uzu83/ai-agent-skills/skills/init-ai

Audits an existing repository and fills only the gaps that AI coding agents need, using minimal diffs — a thin CLAUDE.md command center, ARCHITECTURE and CONVENTIONS docs, CI, lint/format, .env.example, and a single verify entrypoint. It is opinionated and minimal: it states what it deliberately does NOT add and why, never overwrites a file without explicit confirmation, derives commands only from tools that are actually present (never fabricated), and adapts to the detected stack and AI tools in use. Suitable for onboarding an existing repository to Claude Code or other AI coding agents, or when a repo lacks AI-ready scaffolding.From its SKILL.md

Install
npx -y skills add Uzu83/ai-agent-skills --skill init-ai

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One thing to look at

  • 0 stars0 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 file declares

Copied from the file, not written here

The file declares its own license as MIT. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.

SKILL.md

5.7 KB, ~1.3k tokens by cl100k_base, as published. Nobody here has run it

init-ai

/init-ai audits an existing repository and fills only the gaps that AI coding agents need to work well — with the smallest possible diff. It is the opposite of a template generator.

Why this exists (the one idea)

create-* scaffolders emit a static tree that rots from the moment it is generated. /init analyzes the repo but stops at a one-shot CLAUDE.md. /init-ai instead audits the real repository and adds only what is missing, as a minimal diff over the files that already exist, and across a wider scope (CLAUDE.md + architecture/conventions docs + CI + lint/format + verify). The differences that matter: it audits rather than templatizes, it never overwrites without confirmation, it derives everything from what is actually present, and it makes an explicit call about what not to add.

That last point is the discipline. The most common failure of "make your repo AI-ready" tooling is adding too much — CHANGELOG, CONTRIBUTING, SECURITY.md, ADRs, issue templates, CodeQL — which is noise for a solo or small repo and dilutes the agent's context. /init-ai adds only the high-value core and tells you what it is NOT adding, and why (references/checklist.md).

Non-negotiable principles

  1. Audit, never templatize. Never clobber existing files. If a target path already exists, default to not overwriting it (append, add an adjacent file, or skip) and surface it in the plan. Overwriting or deleting always requires explicit confirmation.
  2. Opinionated minimalism (anti–cargo-cult). Add only the high-value core. Everything else is opt-in — only when the user explicitly asks for it.
  3. Never fabricate. Derive every command, key, and fact from what is actually present (declared scripts, installed dev-deps, config files, real .env* keys). Omit any step whose tool is absent. If something can't be determined, ask — do not invent a toolchain or a value.
  4. CLAUDE.md is a thin command center. Repo map, invariants, and links to docs/ — not full detail. If a parent/org CLAUDE.md exists, inherit and link rather than duplicate (rules in references/checklist.md).
  5. Optimize for the real toolchain and the AI tools actually in use. Detect the stack; generate AGENTS.md only if Codex is used, copilot-instructions only if Copilot is used. Never create files for tools the repo doesn't use.
  6. Compose existing review assets; do not reinvent. The verify path calls the repo's own lint/typecheck/test/build and any existing /code-review or reviewer subagent — it does not build a new checker.

Procedure

Run these steps in order. Stop where noted and wait for the user.

1. Audit (read-only)

Scan with Glob/Read — modify nothing. Inspect presence/absence of: manifest · lockfile · tsconfig · .github/workflows/* (CI) · lint/format config · test config · docs/ · .env* (read key names only, never values) · .env.example · CLAUDE.md (and any parent) · README · AGENTS.md · deno.json. Detect the stack and the AI tools in use. Full table and detection/priority rules: references/checklist.md. If the stack or target is ambiguous (polyglot/monorepo), do not guess — note it for step 2.

2. Judge

List the gaps. Present both "what to add" and "what we deliberately do NOT add, and why". If the repo is too sparse to describe a section truthfully, say so here rather than inventing content. Then wait for the user to choose how to proceed.

3. Plan → confirm

Show the minimal-diff plan. List every file that already exists and would be touched as a single overwrite list, and ask for confirmation once (the user may exclude items by number). In a non-interactive context, skip destructive changes and log them.

4. Apply (minimal diff, respect what exists)

Add only what is missing, from the high-value core (shapes in references/templates.md):

  • Thin CLAUDE.md — repo map + invariants + links. Inherit a parent if present.
  • docs/ARCHITECTURE.md and docs/CONVENTIONS.md.
  • CI (if absent) — a GitHub Actions workflow running the verify chain, built only from steps whose tools are present (per checklist.md).
  • lint + format (if absent) — the stack's standard minimal config.
  • .env.example — built from real .env* key names; values never copied.

5. Verify entrypoint

Provide one verify entrypoint that chains the stack's checks: a verify script in package.json (JS) or a verify target in a Makefile (non-JS). The exact commands per stack live in references/checklist.md.

What this deliberately does NOT add

See references/checklist.md § "Opt-in only" — these are added only on explicit request, because in a small repo they dilute agent context.

References

  • references/checklist.md — audit table, stack detection + verify commands, inheritance & priority rules, opt-in list.
  • references/templates.md — the CLAUDE.md / docs / CI / .env.example shapes.

What ships with it: 2 files

7.3 KB alongside SKILL.md

references/

Gives 0 of the 12 instructions most quality gates skills give in ~1.3k tokens

Counted across 1,195 of the 2,094 authors here whose files we hold, read 2026-08-07

  • Read the output and check the exit codein 54 of 1195, across 14 files
  • Verify requirements using a line-by-line checklistin 53 of 1195, across 12 files
  • Identify the verification command proving the claimin 51 of 1195, across 12 files
  • Run the full verification commandin 50 of 1195, across 11 files
  • Verify output confirms the claimin 49 of 1195, across 12 files
  • Check version control diff after agent delegationin 46 of 1195, across 6 files
  • State claim with evidencein 44 of 1195, across 4 files
  • Run the test suitein 33 of 1195, across 26 files
  • Keep state in memory by defaultin 27 of 1195, across 6 files
  • Make prototype runnable with one commandin 26 of 1195, across 5 files
  • Produce a verification reportin 25 of 1195, across 14 files
  • Detect the package manager from lockfilesin 24 of 1195, across 5 files

Said here and by no other author read

  • audit the repository without modifying anything
  • list gaps and deliberate omissions
  • show the minimal-diff plan before execution
  • skip destructive changes in non-interactive contexts
  • derive all commands from tools actually present
  • build a thin CLAUDE.md as a command center

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

Keep looking

Skills are one crate of 326,835. 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.