skill-gen — the skill that generates skills

A guided generator for portable Agent Skills. It conducts a short interview, picks the minimum viable shape, and writes a skill that runs across Claude Code, OpenAI Codex, and opencode — isolating each platform's quirks instead of hard-coding one.

Core principle. The base spec recognizes only five frontmatter fields (name, description, license, compatibility, metadata). Everything else (allowed-tools, disable-model-invocation, Codex openai.yaml, opencode permission.skill) is platform-specific and lives in a different place per runtime. A portable skill keeps its logic in the body + references/ + scripts/, and treats per-platform control as a sidecar — never a dependency.

This SKILL.md is a router. Load only the reference you need for the current step.

Workflow (follow in order)

Step 0 — Capture intent: create or improve?

First decide the operation:

• Create (new skill) → continue with Steps 1–6 below.
• Improve (an existing skill mis-fired, didn't trigger, or produced weak output) → switch

to the iteration loop in references/iteration.md: capture the examples, split working vs holdout, make the smallest delta, replay, and confirm no regression. Then run Step 5 (validate) + the description tuning in Step 4b before reporting. Skip the create-only steps.

If the conversation already contains the workflow to capture ("turn this into a skill"), extract answers from history before asking: the steps taken, tools used, corrections the user made, input/output formats observed. Only ask what you cannot infer.

Step 1 — Interview

Ask one question at a time, each with your recommended answer, and explore the codebase instead of asking when the answer is discoverable. Use the AskUserQuestion tool when it fits. Cover these decision axes (full rationale in the linked references):

1. Purpose & trigger — What should the skill let the agent do? What user phrases/contexts

should activate it? What is the expected output? (The `description` is the trigger — get real phrases the user would actually type.)

1. Single vs multiple skills — Is this one cohesive capability, or several with distinct

triggers/audiences? If triggers diverge, split. → references/archetypes-and-splitting.md

1. Target runtimes — Claude Code only? + Codex? + opencode? all three? This decides

install location(s) and which sidecars to emit. → references/platforms.md

1. Execution shape — pick the simplest adequate one → references/archetypes-and-splitting.md:

• knowledge (instructions only)
• reference-backed (heavy domain docs in references/)
• script-backed (deterministic automation in scripts/)
• orchestrator (drives an external MCP server / API)

1. Tools & safety — Read-only, or does it write/run/network? Destructive? Should it be

barred from auto-invocation? → references/security.md + references/frontmatter.md

1. Knowledge volume — Will the body exceed ~500 lines? If so, plan the

references/ split now. → references/progressive-disclosure.md

Record the chosen answers as a short spec before writing anything. If the user says "just vibe with me / skip the interview", collapse to questions 1, 3, and 5 only.

Prefer autonomy. A generated skill should act on its own and involve the user only for a decision genuinely theirs to make, or a real branch the agent can't resolve from context and sensible defaults. Most skills should lean autonomous. Only when the skill must elicit a choice (or the user is explicitly driving the decision) give it the thinking-partner style — a recommended default marked "(Recomendado)" with a one-line why, calibrated to the user's experience. → references/interaction-and-elicitation.md. (E.g. tech-discovery is intentionally interactive; a formatter or one-shot generator should just run.)

Step 1b — Research & ground the content (skip for trivial wrappers)

A skill is only as good as the knowledge curated into it — structure isn't enough. When the skill encodes domain knowledge that must be correct and current (a library/framework/API, a security/compliance domain, a methodology, anything with per-language/per-platform detail), go find the content → references/research-grounding.md:

• Ground locally first: Grep/Glob the target repo for real conventions and gotchas;

check installed versions and pin research to them; read sibling skills for house style.

• Pull current docs from context7 (version-specific) for any named library/API; prefer

primary sources (official docs, RFCs) over blogs; use WebSearch/WebFetch for recent practice.

• Work in three stages — plan → capture → consolidate — don't research straight into the

final references:

1. Plan the questions/dimensions and which source each needs.
2. Capture each result in its own file in a scratch workspace outside the skill

(<workspace>/<slug>/raw/NN-*.md), one per dimension — fan out with subagents (`Task`), one per item, each writing its own file with provenance + confidence.

1. Consolidate the workspace into one reference file per domain (the Sentry pattern),

verifying non-obvious claims against a second source first. The raw workspace doesn't ship.

• Degrade gracefully if subagents/web/context7 aren't available (run inline; flag confidence).

Skip this step for a thin procedural wrapper with no external knowledge. Output: a short research log (findings + sources + gaps) that feeds the authoring step.

Step 2 — Decide layout

From the interview, fix:

• the directory name (= name, 1–64 chars, lowercase, single-hyphen separators);
• the install path(s) per target runtime (see references/portability.md §placement);
• the bundled resources (references/, scripts/, assets/) the shape requires.

Default to .claude/skills/<name>/ when targeting Claude Code and opencode — opencode reads .claude/skills/ natively, so one copy covers both. Add a .codex/skills/<name>/ copy (or a config.toml entry) only when Codex is a target. Use .agents/skills/<name>/ when the user wants the broadest cross-client convention.

Step 3 — Write the portable core

Start from assets/templates/SKILL.md.tpl. Rules:

• Frontmatter carries the five standard fields + (only if Claude is a target)

allowed-tools. Unknown fields are harmless in opencode/Codex (ignored) but keep them minimal. → references/frontmatter.md

• Body in imperative voice, trigger-rich, explaining the why — not heavy-handed MUSTs.
• No `!`cmd`` load-time execution and no `${CLAUDE_SKILL_DIR}` unless the skill is

Claude-only — those don't expand in Codex/opencode. Instead, instruct the agent to run the command as a workflow step ("run ls -la and read the output").

• Keep SKILL.md under ~500 lines; route heavy material into references/ with explicit

"open when…" pointers. → references/progressive-disclosure.md

• Use checklists, tables, and template anchoring (embed exact output skeletons) over

prose.

Step 4 — Emit per-platform sidecars (only for chosen targets)

• Claude Code — allowed-tools (and optionally disable-model-invocation: true) in the

frontmatter. Use the scoped syntax Bash(git add *) for least privilege.

• Codex — write agents/openai.yaml from assets/templates/openai.yaml.tpl

(dependencies, allow_implicit_invocation). Tool/dep control lives here, not in frontmatter.

• opencode — it ignores allowed-tools; permission is set in the user's opencode.json

under permission.skill. Don't write to their config — instead, if the skill needs restriction, emit a short PORTABILITY.md telling them the exact opencode.json snippet.

• When more than one runtime is targeted, always emit PORTABILITY.md listing what to

configure where (template in assets/templates/). → references/portability.md

Step 4b — Optimize the trigger description

The description is what decides whether the skill fires. Tune it before validating → references/description-optimization.md: draft realistic trigger phrasing, build should-trigger / should-not-trigger query sets (the near-misses matter most), check the description against both, and edit until false positives/negatives are acceptable. Keep it generic across runtimes unless the skill is intentionally provider-specific.

Step 5 — Validate

Run the bundled checker, then a security pass:

python3 scripts/validate_skill.py <path-to-generated-skill>

It checks: SKILL.md exists, valid YAML, name/description present and within limits, name matches the directory, and flags Claude-only constructs (!cmd`, ${CLAUDE_SKILL_DIR}) when the skill is declared cross-platform. Then apply references/security.md (least privilege, no secret-prompting, review any scripts/). If a skill-scanner` skill is available, run it too.

Step 6 — Report

Return:

1. Summary — what was generated and for which runtimes.
2. Files — tree of created paths.
3. Per-platform setup — the exact steps the user must do in each target (e.g. Codex

config.toml entry, opencode permission snippet).

1. Validation results — checker output + any open gaps.

Quick decision shortcuts

• One runtime, Claude Code, read-only knowledge skill → single SKILL.md, 5 fields +

allowed-tools: Read Grep Glob, no references. Done in one file.

• All three runtimes → portable body, no load-time exec, allowed-tools for Claude,

openai.yaml for Codex, PORTABILITY.md for opencode permissions.

• Heavy domain knowledge → lean router SKILL.md + references/<topic>.md, each with an

"open when…" entry.

• Repetitive deterministic step → bundle a scripts/ helper so every future run reuses

it instead of reinventing.