Disqualifier scan

Skill ralfyishere/piensalo/micro-skills/disqualifier-scan

PIÉNSALO is the open artificial cortex for AI—reduce context tokens, verify response quality, expand when needed, and safely fall back.

Install

npx -y skills add ralfyishere/piensalo --skill disqualifier-scan

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

2 things to look at

25 days oldThe repository was created 25 days ago. New is not bad, but a brand new repository carrying a familiar-sounding name is the shape a typosquat arrives in, and there has been no time for anyone else to find a problem with it.
1 stars1 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Before shipping, sweep for conditions that void the entire answer regardless of its quality — violated mandatory rules, missing required elements, forbidden resources, wrong output form. A hit is fixed, never caveated.

The file declares its own license as MIT. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.

SKILL.md

2.8 KB, 529 tokens by cl100k_base, as published. Nobody here has run it

disqualifier-scan

Trigger (observable): The task defines pass/fail conditions, mandatory requirements, or a required format — or any deliverable is about to be declared finished against a spec, rubric, or submission rule set.

When NOT to activate: No external spec or rule set exists to disqualify against; early drafting where the structure is still moving; the same scan already ran on this draft version.

Procedure

List every condition that would make the answer worth zero regardless of its other qualities: mandatory elements, format requirements, prohibited content or resources, scope rules, eligibility criteria. Take them from the task/spec, not from imagination.
Check the draft against each disqualifier one at a time; quote the evidence for any hit.
Any hit is repaired before shipping — a disclosed disqualifier is still a disqualifier; writing it down does not resolve it.
If a disqualifier cannot be repaired, the verdict is 'does not qualify', stated first — not a polished delivery with a caveat.
Record which disqualifiers were checked, so the final claim is scoped to what was actually swept.

Required output

A disqualifier list with per-item check results (pass / HIT with quoted evidence / not-checkable), placed before the completion claim.

Verification

Every listed disqualifier traces to an explicit condition in the task/spec text.
No disqualifier hit survives into the shipped version — each hit is fixed, or the verdict is changed to does-not-qualify. Disclosure alone never clears a hit.
Net effect: every mandatory condition stated in the task/spec appears in the list, and no hit ships undeclared or merely caveated.

Known risk: Sweeping invented disqualifiers that the spec never stated, burning tokens on phantom rules. Mitigation: step 1 requires each item to trace to the task text.

Max intended cost: ≤250 added output tokens; re-read of the spec's requirement clauses.

Evidence status: EXPERIMENTALLY_TESTED — this repair content lifted a weak model on procedural tasks in controlled runs; the effect is task-concentrated, and automatic router selection of micro-skills is a separate, unproven layer.

Lineage: Grounded in an evidence-backed completion-integrity principle (a deliverable is not done while a voiding condition stands) and in a documented reasoning-failure mode — verifying surface form while missing the semantic requirements; applies the disqualifier-sweep pattern from the broader Piénsalo skill family.