Fase 4 Items 2+3 (CC 2.1.114→181 gap-review). New orchestrated scanner
`skill-listing-scanner.mjs` (prefix SKL) flags every active skill whose
description exceeds the verified 1,536-char listing cap (CC 2.1.105, changelog
L1502). Past the cap, Claude Code silently truncates the description the model
reads to route skill invocation — dropping the trigger phrases at the tail.
HOME-scoped over all user + plugin skills via enumerateSkills (COL is the model).
- CA-SKL-001 (medium): description > 1,536 chars. Remediation folds in Item
2(b) — recommends disableBundledSkills + skillOverrides + trimming
(designvalg A: no standalone GAP-check, which would fire for nearly everyone).
- Designvalg B: v1 ships the verified cap ONLY. The aggregate 2%-of-context
listing budget is deferred — it needs a context-window assumption that would
turn a verified fact into a guess (would carry a CALIBRATION_NOTE if added).
- Choice C: recognize the skillOverrides settings key (CC 2.1.129) in
KNOWN_KEYS. Left OUT of TYPE_CHECKS — the value is a per-skill object
(off/user-invocable-only/name-only), not a string; a 'string' check (as the
plan sketched) would create a NEW false positive. Verify-first deviation.
Registration: scan-orchestrator (13th scanner), humanizer (SKL → 'Wasted
tokens' + static/_default translations), scoring SCANNER_AREA_MAP (→ Token
Efficiency; no 11th area), README badge 12→13, CLAUDE.md (finding-id +
test-count), docs/scanner-internals.md, gap-matrix + plan status notes.
Snapshots reseeded hermetically (SEED_SNAPSHOT/UPDATE_SNAPSHOT): SKL entry with
0 findings in empty HOME, scanners_ok 11→12, claudeMdEstimatedTokens bump from
the CLAUDE.md edits flowing through the cascade. Contamination grep clean.
Suite 868/868 (856 baseline + 11 SKL + 1 skillOverrides). RED→GREEN logged
per cycle.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Ter3E2JSi1Khgmuf2kady8
Wave 1 / Step 3 of v5.1.0 plain-language UX humanizer.
scanners/lib/humanizer.mjs exports three pure functions:
- humanizeFinding(f) -> new finding object with translated
title/description/recommendation + three new fields
(userImpactCategory, userActionLanguage, relevanceContext).
- humanizeFindings(findings) -> mapped array.
- humanizeEnvelope(env) -> walks env.scanners[].findings.
Plus computeRelevanceContext(filePath) as a named export for
unit testing.
Field semantics:
- userImpactCategory: from scanner prefix per research/02 line 124
(Configuration mistake / Conflict / Wasted tokens / Dead config /
Missed opportunity / Other).
- userActionLanguage: from severity per research/02 line 134
(Fix this now / Fix soon / Fix when convenient / Optional cleanup
/ FYI).
- relevanceContext: deterministic file-path heuristic — looks for
/tests/fixtures/ or /test/fixtures/ substring (test-fixture-no-impact),
*.local.* basename (affects-this-machine-only), defaults to
affects-everyone. No subprocess, no network.
Lookup order per scanner: static[title] -> patterns regex match ->
_default -> fall through to original strings (when scanner prefix
absent).
Original id, scanner, severity, file, line, evidence, category,
autoFixable, and optional details are preserved exactly. Pure —
verified by deepEqual of input before/after.
Test (32 cases): purity, field preservation across all paths,
known/unknown scanner handling, all 5 severities, all 6 categories,
relevance heuristic for 4 path types, envelope walking, ANSI-free
guarantee. All pass.
Regression: 689/689 tests (657 + 32 new = 54 new across Wave 1).
Project: .claude/projects/2026-05-01-config-audit-ux-redesign/
