RED-7 (example-priming): the STEP-2 worked example named live IDs (LRN-014 +
LRN-016) and modeled merging them — but they are complementary (header-ids vs
checkbox-CSS), a merge the skill's own rule forbids. Live IDs in an example prime
the skill to act on those exact entries on real data. Fictionalized the whole
STEP-2 example to 9xx IDs (cannot match a live registry); the merge example now
models a same-concept merge. Closed by a DETERMINISTIC test (run-deterministic.sh
RED-7: the example must carry only 9xx ids) per LRN-046, not a flaky behavioral
fixture. The test caught its own ugrep false-green first (a leading-dash pattern
parsed as an option) — fixed via /usr/bin/grep, the same dodge the skill's verify
already uses at line 189.
RED-8 (added-negation inversion): re-reviewed, consciously accepted as a documented
limit in BACKLOG — remote (compression subtracts tokens), and an FP-safe increase
check is non-trivial (needs the HEAD entry-id set to exclude legit new/merged 0->N);
a noisy guard is worse than the honest limit on a destructive skill (LRN-047).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01C6bUdvHnajCNzgVQefZowj
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
