The agent-listing scanner (AGT) emits an always-loaded per-turn token cost
("Agent description is long, re-sent every turn in the always-loaded listing";
scanner category 'token-efficiency', "the dominant single always-loaded
source"). But SCANNER_TO_CATEGORY in humanizer.mjs had no AGT entry, so its
findings fell through to the 'Other' fallback (humanizer.mjs:140) — a bucket
that isn't even in the analyzer-agent's category list. Neither the scanner
prefix nor the per-finding category ('token-efficiency' is not in
CATEGORY_TO_IMPACT) resolved AGT to its true impact.
Same class as M-BUG-16/15: a finding type without its matching humanizer
mapping landing on a default that mismatches its own evidence. The analogous
SKL body finding correctly buckets "Wasted tokens"; AGT (the same always-loaded
token-waste mechanism) silently landed under the meaningless "Other".
Found during analyze-prep premise-verification of the linkedin-posts scan: the
3 AGT findings bucketed "Other" while the analogous SKL findings bucketed
"Wasted tokens".
Fix: add AGT: 'Wasted tokens' to SCANNER_TO_CATEGORY, alongside TOK/CPS/SKL.
RED-first (extended the Wasted-tokens category test to include AGT; the 'Other'
fallback test still uses a synthetic 'XXX' scanner, unaffected). Frozen v5.0.0
untouched (AGT post-dates it; humanizer bypassed for --raw/--json); no
default-output snapshot contains AGT -> 0 regen. Suite 1359/0.
Verified end-to-end on linkedin-posts: 3 AGT findings now "Wasted tokens", 0
"Other" remaining, 28 findings unchanged (category-only). All 16 orchestrator
scanner prefixes now covered by the category map (class closed).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01683eAqVecv9VZfQzL8CQ9h
The skill-listing check emits a third finding for an oversized skill BODY
(v5.11 B7, RAW title "Skill body is large (loads on demand when the skill
runs)"). The body is an ON-DEMAND cost — it loads only when the skill is
invoked, not the always-loaded listing Claude reads every turn. The scanner is
careful to distinguish the two (RAW title + comment + evidence note).
But the humanizer-data SKL.static map had no entry for this title, so it fell
through to SKL._default ("A skill is using more of the listing budget than it
should"). The humanized title therefore claimed a listing-budget cost and
directly contradicted the finding's own humanized evidence ("loads ON DEMAND
only ... NOT every turn like the always-loaded listing") — the same internal
contradiction class as M-BUG-15/M-BUG-14, and the same "new finding type added
without a matching humanizer entry" gap the scanner checklist warns about.
Found by finding-granularity premise-verification of the linkedin-posts scan
before feeding it to the analyze pipeline (the prior session's pass focused on
the GAP findings and did not catch the SKL fall-through).
- humanizer-data.mjs: add SKL.static entry for "Skill body is large (loads on
demand when the skill runs)" with on-demand-correct title ("A skill's body is
large (it loads only when that skill runs)"), description, and recommendation.
No listing-budget language; tier1/tier3 forbidden-word checks pass.
- RED-first tests at both layers: humanizer.test.mjs (humanizeFinding path:
title is not the listing-budget _default, conveys on-demand body) and
humanizer-data.test.mjs (static entry exists, on-demand-correct).
RAW envelope unaffected (humanizer bypassed for --raw/--json), frozen v5.0.0
snapshots untouched, default-output fixtures contain no oversized-body skill so
no snapshot regen. Suite 1357->1359/0 (+2).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01683eAqVecv9VZfQzL8CQ9h
The B3a cache filter already exposes discovery.staleCacheVersions; surface them
as a finding so the user knows the superseded plugin versions are safe to delete.
Honesty (Verifiseringsplikt): the finding loads on ZERO turns, so it must NOT
read as a per-turn token cost. TOK normally humanizes to "Wasted tokens"; a new
per-finding category override ('plugin-cache-hygiene' -> "Dead config") plus a
dedicated humanizer translation ("Old plugin versions are sitting on disk (safe
to delete) ... cost zero tokens per turn ... housekeeping, not a performance
problem") keep the prose accurate instead of the generic "using more space"
default. evidence carries the explicit "zero live-context impact" note.
- token-hotspots: Pattern H emits CA-TOK (low) from discovery.staleCacheVersions
when stale versions exist (`--global`); silent otherwise.
- humanizer: CATEGORY_TO_IMPACT lets a finding's category override the
scanner-default impact label (raw `category` field unchanged -> --json/--raw
byte-stable). humanizer-data: honest static translation for the finding.
- Tests: finding fires/severity/category, lists stale keys + zero-impact note,
silent when none; humanizer override -> Dead config; honest translation locked.
- Docs: tokens command render note (disk-cleanup, not a token problem) +
--no-exclude-cache flag; README + CLAUDE.md rows (7 patterns, cache-aware).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
New orchestrated scanner output-style-scanner.mjs — first new family since
SKL. Three findings, each pinned to a CONFIRMED V-row of the steering-model
plan + re-verified against code.claude.com/docs/en/output-styles:
- CA-OST-001 (medium, V10): user/project custom style missing
keep-coding-instructions:true (default false) → silently strips built-in
software-engineering instructions when active. Scoped to user/project.
- CA-OST-002 (low, V11): plugin style with force-for-plugin:true overrides the
user's selected outputStyle. Verifiseringsplikt correction — the plan bullet
said "project/user style," but force-for-plugin is plugin-styles-only per the
docs, so the check keys on source==='plugin'.
- CA-OST-003 (medium): settings outputStyle matching no built-in
(Default/Explanatory/Learning/Proactive, case-insensitive) nor discovered
custom style → dead config.
Byte-stability — a scanner addition, not a field addition. Growing the
scanners array + scanners_ok cannot be hidden by a field strip, but re-seeding
frozen v5.0.0 (the SKL precedent) would now bake in B2's hotspot triple +
claudeMd drift. So, per the B2 lesson, frozen v5.0.0 snapshots are PRESERVED
and the OST entry is stripped at compare time via new
tests/helpers/strip-added-scanner.mjs (wired into json/raw-backcompat + the
Step 5/6 humanizer tests); only SC-5 default-output is regenerated (additive
OST entry, diff reviewed). OST is fixture-gated (no output styles on
marketplace-medium / hermetic HOME → silent).
Wiring: orchestrator; humanizer (OST→Configuration mistake) + humanizer-data
OST family (title-coupled); scoring (OST→Settings, keeps 10 areas). Suite
1012→1023 (+11). Badges: scanners 14, tests 1023, TRANSLATIONS families 15.
Lore swept: README, CLAUDE.md, scanner-internals, humanizer.md. self-audit A/A.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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/
