check-target
Skill2 starsChanged 46 days agoMITView on GitHub
Tests whether a failure pattern named as "the thing to fix" is one real defect class, by recomputing its signature over all rows (passing ones included), reporting hits, failures, precision and counterexamples, and splitting the hits by cause. Use when a cluster, tell, signature, or failure pattern has been named as the fix target — before proposing, scoping, or starting any fix. Triggers on "the biggest failure class is X", on a fix target inherited from a handoff note, roadmap, or review, and on sizing a defect class by counting rows that match a tag or substring. Not for producing the candidate targets (use cluster-failures) or for locating the code site to change (use spy-site).
In this repository22 other files it gives its agents.
- bounded-goalsSkill
- change-asserted-valueSkill
- cluster-failuresSkill
- councilSkill
- enumerate-firstSkill
- eval-loopSkill
- fable-reviewSkill
- gauntlet-loopSkill
- handoffSkill
- kickoffSkill
- promo-videoSkill
- prove-invariantSkill
- refusal-censusSkill
- reuse-before-rerunSkill
- run-evalSkill
- run-gateSkill
- scientific-posterSkill
- spy-siteSkill
- test-gateSkill
- understand-before-mergeSkill
- verify-sourceSkill
- watching-background-jobsSkill
The file
Files
Check the target before you propose a fix
A signature that matches passing rows is a lead, not a defect. This skill exists because one project's review named a naming-output pattern (a prefix emitted without its locant) as the top fix target, and measurement then showed:
- 30 hits, 29 failures — the 30th row already round-tripped correctly in the shipped config, so the flagged construction is legal in one sub-family
- three unrelated causes, not one: missing-locant on an acyl chain (~15), a glycosidic-oxygen case (~13), a raw input string emitted verbatim as output (2)
- the glycosidic sub-case is a double-counted feature — the parent structure was named as if a substituted oxygen were a free hydroxyl, so a locant-only fix would leave the output wrong
The lesson generalizes past naming: a whole iteration was scoped against "one defect" that was three, one of which was not a defect at all.
The procedure
1. Resolve the target to an explicit row list from a committed eval/benchmark run. Not a description, not a cluster name — the actual inputs (SMILES, molecule IDs, or whatever your unit of analysis is).
2. Recompute the signature over ALL emitted rows, not just failures. This is the whole point. A signature computed inside a failures loop is structurally incapable of seeing its own counterexamples — a clustering script that only iterates failing rows has exactly this bug.
3. Report four numbers:
| number | meaning |
| hits | rows matching the signature |
| failures | hits whose outcome is a failure |
| precision = failures / hits | <1.0 means passing rows match too |
| non-failing hits | list them explicitly — these are the counterexamples |
4. Decompose by cause. Inspect the actual outputs (names, labels, values). If the matched rows split into groups with different mechanisms, the target is N targets: re-scope to the largest one and return to Step 1.
5. Check the outcome spread. A single defect class does not usually span
parse_fail + wrong_structure + exact_match. If the hits span 3 or more outcome types,
return to Step 4: there is more than one cause.
Refusal conditions
Report NOT A SINGLE DEFECT and stop if:
- precision < 1.0 — the signature matches rows that already succeed. Call it a lead and name what distinguishes the failures from the passes.
- n < 10 — too small to be worth a task, and too small to see a second cause.
- the hits decompose into ≥2 mechanisms — re-scope to the largest single mechanism and re-run this check on it.
- the hits span ≥3 outcome types — almost certainly multiple causes.
- the signature is a string pattern on the emitted output and no structural cause has been identified. Output patterns are symptoms; the same wrong output arises from unrelated causes.
Size from the named inputs, not from a tag
Size a defect class only from the actual inputs, each traced to its cause; a count of rows that match a tag or substring is not a size. Projects repeatedly over-count 15–50× by counting tags: a curated "~100 candidate defects" list that turns out to have 2 genuine instances on re-derivation; a static sweep's "12 live defects" that turns out to be 4 once each case is traced.
Related
spy-site— once the target survives this check, prove the code site is on the path.cluster-failures— produces the candidate targets this skill validates.- Your project's fix-methodology / engineering-log doc, if it has an equivalent invariant.
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