0a3e76611d
Only destructive skill, previously untested. A RED suite (tests/) proved 6 dangers; each closed by a deterministic guard: - RED-1 removed false "Fixed in v1.1 (TDD found it)" verify claim - RED-2 STEP 0 dirty-tree is now a real exit 1 (was a prose-only STOP) - RED-3 STEP 3.4 negation-sentence verbatim guard (no silent inversion) - RED-4 STEP 1-A collapse safety-critical exception (NEVER/ALWAYS/PERMANENT) - RED-5 STEP 4 fidelity census (count-based, per-entry x per-category) - RED-6 STEP 4 trailing-space false-ORPHAN fix Tests: run-deterministic.sh (all-green), run-behavioral.md, fixtures, BACKLOG (RED-7/RED-8 open). Validated on the real learnings.md: 0 fidelity false-positive vs 13, scope held, registry reverted. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01W9sqAwZxBMZSynZoVrEJhd
2.1 KiB
prune-memory — test backlog (future REDs)
RED-7 (candidate) — example-priming in the merge pass
Observed during the 2026-06-25 real-data measurement on the live
learnings.md: the skill merged LRN-014 + LRN-016 — the EXACT pair
named as the worked example in SKILL.md STEP 2
("LRN-014 + LRN-016 — both pandoc rendering quirks → merge into NEW
LRN-017").
Hypothesis: the skill's own illustrative example PRIMED the merge on real data, rather than a genuine content overlap between those two entries.
If confirmed, this is a design defect: a skill's example must not steer its behavior on real registries.
- VERIFY FIRST: read the real LRN-014 / LRN-016 — do they actually overlap, or did the example drive the merge?
- RED (if priming confirmed): fixture with entries at LRN-014/016 that do NOT overlap (distinct topics) → assert the skill does NOT merge them.
- GREEN: fictionalize the SKILL.md example (obviously-fake IDs, or an explicit "hypothetical" framing) so example IDs cannot match real entries.
Status: filed, not built. Surfaced by the real-data A-measurement.
RED-8 (candidate) — added-negation inversion (documented limit, not a test yet)
The RED-5 fidelity guard flags negation/permanent token DROPS; it cannot catch an ADDED negation that inverts meaning ("X works" -> "X never works") — that is a count INCREASE. The STEP 3.4 NEGATION GUARD only protects sentences that ALREADY contain a negation, so it does not stop a non-negation sentence being rewritten WITH a negation. So NEITHER guard closes this case — a real hole, documented honestly rather than claimed covered.
Practically remote: caveman compression and merge SUBTRACT tokens (drop filler); they do not author new negations. Producing "X never works" from "X works" requires ADDING a word, contrary to an operation that shortens.
- RED (if pursued): assert no op INCREASES an existing entry's negation count.
- Caveat: must exclude new/merged-entry ids (HEAD count 0 -> N is legitimate), so an increase-check needs care to avoid its own false positives. Status: documented limit, not built (low practical risk + non-trivial FP risk).