meta-verify

meta-verify

A second pair of eyes on a skill's output, applying the exact standard the originating skill already committed to — its own ## Quality Gate table and ## Operating Rules. Catches what the first pass missed, especially when the same session that produced the output is also the one that's about to mark it done.

This isn't a second opinion using a different, invented rubric — it's a faithful re-application of the standard that skill already declared for itself. If a skill's own Quality Gate is weak, that's a meta-review finding against that skill's SKILL.md, not something this skill compensates for by inventing its own scoring.

Trigger

• When: A skill has produced output and it's about to be delivered

externally (sent to sales, shared with leadership, published) — a second pass before it leaves the building.

• Not for: Auditing a SKILL.md's own structure → use meta-review.

Extracting learnings from a completed session → use meta-learn. Detecting patterns across sessions → use meta-synthesis.

• Example prompts:
• "Check this positioning brief before I send it"
• "Verify the last GTM strategy output"
• "Did this retro actually pass its own quality gate?"
• "Second-pass this before it goes to leadership"

Inputs

• Args: The output to verify, and which skill produced it. N/A if

invoked immediately after that skill's own session — infer both from context.

• Defaults: If the originating skill isn't obvious from the output

itself, ask.

• Context keys: The originating skill's own SKILL.md — specifically

its ## Quality Gate and ## Operating Rules sections. No brain context needed beyond what the originating skill itself required.

Pre-flight

• Load the originating skill's SKILL.md. If it doesn't exist or doesn't

declare quality_gate: true, say so — there's no standard to re-check against, and this skill isn't the place to invent one on the spot.

• This skill is context-agnostic. It applies the originating skill's own

declared standard; it does not load /foundation/brain.md independently.

Steps

Step 1: Load the Originating Standard

Read the originating skill's ## Quality Gate table and ## Operating Rules in full. These are the only standard this pass applies — not a generic rubric, not this skill's own opinion of what "good" looks like.

Step 2: Re-run Every Quality Gate Check

For each row in the originating skill's Quality Gate table, check the actual output against it independently — don't trust that the first pass marked it correctly. Mark each pass/fail with the specific evidence.

## Second-Pass Verification — [skill-name] output

| Check (from [skill-name]'s own Quality Gate) | Pass? | Evidence |
|---|---|---|
| [Check name] | ✅ / ❌ | [Specific quote or observation] |

Step 3: Spot-Check Operating Rules

Operating Rules aren't a checklist — they're standing constraints. Scan the output against each rule from the originating skill and flag any it appears to violate, even if the Quality Gate table doesn't have a matching row for it.

Step 4: Report

## meta-verify — [skill-name] output

**Quality Gate:** [N]/[total] passed
**Operating Rules:** [clean / N flagged]

### Failed checks
- [Check] — [specific evidence of the gap, and what would fix it]

### Rule flags
- [Rule] — [where the output appears to violate it]

### Verdict
[CLEARS SECOND PASS / NEEDS FIXES BEFORE DELIVERY] — [one sentence]

If everything passes, say so plainly. Don't invent soft findings to seem thorough — a clean pass is a legitimate, useful result.

Outputs

• Files written: N/A — this skill produces a report only.
• Chat output format: The verification report from Step 4.
• External side effects: N/A

Verification

• Every row of the originating skill's actual Quality Gate table was

re-checked, not assumed passed from the first run.

• Operating Rules were scanned even where no Quality Gate row corresponds

to them.

• Every failed check has specific evidence, not a generic "doesn't meet

standard."

Do Not Use For

• meta-review — when the task is auditing the skill file itself

(SKILL.md structure) rather than one piece of output it produced

• meta-learn — when the task is capturing what the session taught the

user, not re-checking output correctness

• meta-synthesis — when the task is finding a pattern across multiple

sessions, not a single output's quality

Operating Rules

• Apply the originating skill's own standard — never a substitute one.

This skill has no independent rubric. If the originating skill's Quality Gate is thin, that's a finding for meta-review against that skill, not something to paper over here with invented criteria.

• Re-check independently, don't trust the first pass. The value of a

second pass is that it doesn't inherit the first pass's blind spots.

• Evidence, not adjectives. Every fail cites the specific text or gap —

"positioning statement absent" not "positioning could be stronger."

• Operating Rules matter even without a matching gate row. A skill's

rules are binding regardless of whether its own Quality Gate table happens to test each one.

• A clean pass is a real result. Don't manufacture findings to justify

having run. If nothing failed, report that plainly.

• No skill has a quality gate → no verification possible. If

quality_gate: false or the section is missing, say clearly that there's no standard to check against rather than inventing one.

Quality Gate