x9-skill-creator

name	x9-skill-creator
description	Use when creating, auditing, fixing, or improving an Agent Skill for Claude Code or Codex — «создай скилл», «проверь скилл», «почини скилл», «улучши скилл», "create a skill", "audit skill". Do not use for ordinary code, non-skill prose, global agent instructions, or merely running an existing skill.
compatibility	Requires Python 3 and Ruby with Psych for structural validation. Full Create, Audit, and Fix work on agent-facing prose also requires x9-agent-instructions.

Skill creator

Create and audit skills through one cross-runtime quality contract: precise triggering, appropriate freedom, safe authority, progressive disclosure, and evidence proportional to maturity and risk.

The body of a skill and some reachable resources are agent instructions. For every Create, Audit, or Fix, load and apply x9-agent-instructions before writing or judging any agent-facing instructional prose in SKILL.md or reachable resources. It is a subordinate rubric, not the primary skill-authoring workflow, and it owns language, prescription, duplication, and what earns a line. This skill owns what makes the instruction a skill: triggering metadata, placement and runtime adapters, resource layout and progressive disclosure, evidence tier, structural validation, and the audit handoff.

If x9-agent-instructions is unavailable, continue only the remaining skill checks, record the instruction rubric as degraded, and do not claim that instruction quality was reviewed. A full skill audit cannot be clean in that state. Work whose explicit scope contains no agent-facing prose may record the rubric as not applicable.

Select action and evidence

Choose the action first:

• Create: the target skill does not exist or the user requests a new one.
• Audit: inspect an existing skill, read the complete skill and reachable resources, validate structure, and report findings without changing files.
• Fix: apply explicitly authorized improvements to an existing skill, then recheck the changed contract.

Choose the evidence tier separately:

• Static (default): inspect files, run structural validation, and judge contracts and dependencies without live model invocations.
• Behavioral (optional): run clean-context scenarios from references/evals.md when the user requests live evaluation or a stable/shared/high-risk skill needs behavioral proof for a load-bearing claim.

Ask only when a missing answer is load-bearing. A fully specified request does not need an interview ritual.

Invariants

• No skill is written or judged except against the rubric. patterns.md supplies examples, not templates.
• No new skill exists while another skill's triggers overlap it; extend the owner instead.
• Placement and target runtimes are settled before the first file exists. Personal cross-runtime skills live in ~/.agents/skills/<name>/; package-owned skills stay in their package. Adapter details: claude.md, codex.md.
• Rigidity matches fragility: flexible judgment in prose, repeatable deterministic work in scripts, heavy knowledge in references, output assets in assets.
• The skill's intent and user-owned resources survive the change. Escalate only irreversible or materially scope-changing decisions.
• No claim outruns its evidence tier. Behavioral evaluation runs only when behavioral evidence is selected; an unverified behavioral claim is recorded as unverified rather than treated as a defect.
• Every Audit handoff follows audit reporting.

Audit one target directly. For multiple targets, or when the user explicitly requests a batch audit, read references/batch-audit.md and delegate bounded groups to fresh workers. Batch execution changes only coordination and aggregation; every target still uses the same Audit action, evidence tier, rubric, validation, and verdict contract defined here.

For a genuinely underspecified new skill, use the focused questions in references/interview.md. Stop once triggers, non-triggers, runtime, input/output, authority, and success evidence are clear.

Structural check

python3 <resolved-x9-skill-creator-directory>/scripts/validate.py \
  --runtime <portable|claude|codex> \
  <absolute-target-skill-directory>
python3 <resolved-x9-skill-creator-directory>/scripts/test_validate.py

Use one --runtime per target runtime; repeat it to require one file to satisfy several runtimes. portable enforces the Agent Skills specification and is the default when the flag is omitted. Resolve the creator directory from the loaded skill resource and the target from the actual project/personal source under review; do not substitute a globally installed copy for a package-owned target. The first command validates that target. The second checks validator regression scenarios and is not a substitute for target validation. Structural checks cannot prove triggering, runtime-specific semantics, or output quality; verify platform-specific metadata in the target runtime during behavioral evaluation.

Optional behavioral check

When behavioral evidence is selected, run in a fresh context:

• a positive trigger;
• a near-miss that must not trigger;
• one ambiguity or authority boundary;
• one tool-failure/degraded-result scenario when tools are part of the contract;
• one observable completion assertion.

For stable or release-gated substantive changes, compare old/new or with-skill/without-skill behavior on the same prompts. Record evidence, not the evaluator's impression. Rapidly changing personal skills may defer this until real use exposes a stable scenario worth retaining.

Done

• Structural validation exits 0 without unexplained warnings.
• The handoff records the instruction rubric as applied, not applicable, or degraded; a full skill audit is not clean when it is degraded.
• The judge checklist in the rubric passes.
• The handoff states the action and evidence tier, does not claim evidence outside that scope, and follows the single or batch table contract in audit reporting.
• In batch mode, the orchestrator has quality-checked worker findings and removed unsupported or duplicate recommendations before presenting them.
• If behavioral evidence was selected, representative scenarios show correct trigger, boundary, failure, and completion behavior.
• Static evidence may support a clean audit without live evaluation; unverified behavioral claims remain explicit follow-up evidence, not an automatic finding.