| name | prompt-audit |
| description | Adversarially reviews a draft prompt against a versioned rule set to find what will make it underperform — archetype mismatch, wrong prescription level for the target model, the Fable reasoning-echo trap, negatives that should be positives, unstated expectations, rejected params, and more — then returns tagged findings (rule_id + severity) plus a rewritten, --fix-style prompt. Use when asked to review / audit / critique / improve a prompt, check whether a prompt is good, tighten a system prompt, or before shipping a prompt to a model. |
prompt-audit
Requires the bundled prompt-audit-rules.md (checks) and, for
requires_tier rules, ~/.claude/model-profiles.md (read via
model-recommender). If the profiles file is absent, audit the
model-independent rules and flag that tier-specific checks were skipped —
never guess a model's posture.
Adversarial review of a draft prompt. The job is to find what will underperform,
not to praise. "Good prompt" is model-relative — several rules invert across
tiers — so audit against both the task's archetype and the target model's posture.
The rules live in a file
Load the rule set from prompt-audit-rules.md (next to this SKILL.md) and parse
its ```yaml rules block. Each rule carries id, requires_tier, severity
(block | warn | nit), fires_when, finding, and fix. This skill does
not restate the checks — the file is the only rule source, so the audit can't
drift from it.
Establish the target tier first (the dependency)
Rules with requires_tier: true need the target model's tier and profile:
- If the user named a target model, use it. Otherwise call
model-recommender
on the prompt's underlying task to resolve its archetype → tier.
- Load that tier's profile from
~/.claude/model-profiles.md (apply its
meta.staleness_rule; refresh or flag stale). The profile supplies
prescription_posture, subagent_posture, rejected_params,
refusal_triggers, etc. that several rules test against.
requires_tier: false rules are model-independent — evaluate them regardless.
Evaluate honestly
For each rule, fire it only when its fires_when genuinely holds for this
prompt. Calibration is the whole point — a linter that flags everything is
worthless:
- Respect the severities as written (
block / warn / nit); tag each finding
at exactly the severity the rule assigns.
- A rule's
fires_when is the gate, not a vibe. R4, for instance, targets
steering negatives ("don't be verbose"); a genuine safety prohibition
("never force-push", "never merge") is not a steering negative — leave it.
- A well-written, outcome-oriented prompt should mostly pass. A couple of
findings on a strong prompt is normal; wanting to gut it means the audit
miscalibrated — recheck before reporting.
Output contract
Findings — most severe first, one per fired rule:
[<rule_id> | <severity>] span: "<the offending text, or location>"
why: <one line: why it underperforms for this task/model>
Rewritten prompt — --fix style: the corrected prompt with every finding
applied, ready to paste, then a short bullet changelog mapping each edit to its
rule_id.
Never rewrite silently: always show the findings that justify the rewrite. If no
rule fires, say the prompt is clean, name the archetype/tier it suits, and skip
the rewrite rather than inventing an edit.
