ai-scaffold

name	ai-scaffold
description	Creates new ai-engineering skills or agents end-to-end: scaffold, TDD pressure-test, optimize description, register in manifest, sync mirrors. Trigger for 'create a new skill', 'add a slash command', 'the framework needs a capability for', 'build a new agent', 'scaffold a skill for'. Not for evolving existing skills; use /ai-skill-improve instead. Not for description-only optimization; use /ai-prompt-tune instead.
effort	mid
argument-hint	skill [name]|agent [name]
tags	["meta","framework","creation"]
model_tier	sonnet
mirror_family	antigravity-skills
generated_by	ai-eng sync
canonical_source	.claude/skills/ai-scaffold/SKILL.md
edit_policy	generated-do-not-edit

Scaffold

Create new skills and agents for the ai-engineering framework. Owns the ai-engineering context layer (governance, manifest registration, IDE mirrors, pain sources); delegates skill drafting, TDD pressure-testing, eval pipeline, and description optimization to Anthropic's skill-creator.

Registration Checklist — [NAME]

Write at top; check off regardless of skill vs agent:

• No overlap with existing skills (checked manifest.yml registry)
• File at correct path (.agents/skills/ai-<name>/SKILL.md or .agents/agents/ai-<name>.md)
• Frontmatter has name, description, argument-hint
• Description CSO-optimized (triggering conditions, not summary)
• IDE-compatibility fields set if needed (copilot_compatible, disable-model-invocation)
• Registered in .ai-engineering/manifest.yml (skills.registry or agents.names + total)
• Mirror sync run: python scripts/sync_command_mirrors.py
• Tests pass: source .venv/bin/activate && python -m pytest tests/unit/ -q
• Pain sources consulted (decision-store, LESSONS.md) for constraints

Workflow

• Principles applied: §10.6 SDD (every new capability traces to a documented gap — no scaffold without one); §10.4 DRY (a capability lives in exactly one skill; overlap routes to /ai-skill-improve).

Mode: skill

1. Context (owned here) — follow handlers/create-skill.md:
   • Check overlap: read manifest.yml skill registry; if the capability is covered, STOP and route to /ai-skill-improve.
   • Load pain sources: decision-store.json, LESSONS.md, observations.yml (e.g. DEC-003 plan/execute split, similar-skill failures, instinct sequences to optimize).
   • Determine IDE compatibility (see table below).
2. Delegate to skill-creator — invoke Anthropic skill-creator with the context block below. It owns drafting, TDD pressure-testing, the eval pipeline (grader/analyzer/benchmark/HTML viewer), description optimization, and iteration.
3. Verify return — SKILL.md follows ai-engineering conventions (Step 0 context loading, output contract); frontmatter has all required fields; description is CSO-optimized.
4. Register + sync (owned here) — walk the Registration Checklist and handlers/validate.md. Manifest entry: ai-<name>: { type: <type>, tags: [<tags>] }; bump skills.total. Run python scripts/sync_command_mirrors.py; run unit tests; update README skill counts if they changed.

skill-creator context block:

Create a new skill called "ai-<name>" for the ai-engineering framework.
- Skills live in .agents/skills/ai-<name>/SKILL.md
- Frontmatter: name, description (CSO-optimized), effort, argument-hint, tags
- description is the primary triggering mechanism — describe WHEN to use, not WHAT it does
- Pain sources found: [lessons, decisions, instinct patterns from Step 1]
The skill should: [pass the user's requirements]
Format reference: .agents/skills/ai-security/SKILL.md or .agents/skills/ai-review/SKILL.md

Mode: agent

Agents skip skill-creator (they are not skills). Follow handlers/create-agent.md:

1. Define mandate — singular responsibility (one thing).
2. Load pain sources — as skill Step 1; check decision-store for agent-architecture constraints (e.g. DEC-019).
3. Scaffold .agents/agents/ai-<name>.md — frontmatter (CSO description, model, tools whitelist, dispatch-source comment) plus body: Identity (role/experience/specialization), Mandate (owns/does-not-own), Capabilities (read-only/read-write/paths), Behavior (modes/procedures), Output Contract, Boundaries (limits/escalation), Self-challenge protocol.
4. Register in manifest.yml agents section (names array + total count).
5. Create matching skill — if a /ai-<name> entry point is needed, scaffold via /ai-scaffold skill <name>.
6. Sync + test — as skill Step 4.

CSO Description Patterns

The description field is the skill's search ranking — describe triggering conditions, not functionality.

Bad (summary)	Good (CSO trigger)
"Generates standup notes"	"Use when preparing daily standup notes or summarizing recent PR activity"
"Sprint planning tool"	"Use when planning a new sprint or running a retrospective"
"Resolves git conflicts"	"Use when git reports merge conflicts during rebase, merge, or cherry-pick"

IDE-Compatibility Frontmatter

Field	Effect
copilot_compatible: false	Excludes from .github/skills/ mirror (Claude Code-only)
codex_compatible: false	Excludes from .codex/skills/ mirror
disable-model-invocation: true	Tells GitHub Copilot not to invoke LLM (script-only skills)

Provider-scoped skills set these to opt out of the Copilot and Codex mirrors while staying available in Claude Code; no skill currently uses this scoping.

Examples

Example — create a brand-new skill

User: "the framework needs a capability for OpenAPI schema validation — create the skill"

/ai-scaffold skill ai-openapi

Loads pain context, delegates draft + TDD to skill-creator, registers in manifest.yml, runs sync_command_mirrors.py, verifies tests still pass.

Integration

Delegates to: Anthropic skill-creator (TDD + evals + description optimization). Reads: manifest.yml, decision-store.json, LESSONS.md. Calls: python scripts/sync_command_mirrors.py. See also: /ai-skill-improve (improve existing), /ai-prompt-tune (description-only).

$ARGUMENTS