generateHealthScorecard signature: 2-arg → 3-arg (areaScores, opportunityCount,
options = {}). options.humanized=true renders friendlier title, grade-context
line per overall grade, and rephrased opportunity line. options.humanized=false
(or 2-arg call) preserves v5.0.0 verbatim output for backwards-compat.
topActions also gets an optional options.humanized that swaps recommendations
through humanizeFinding lookup.
posture.mjs main():
--json → write JSON to stdout, suppress stderr scorecard
--raw → write JSON to stdout (byte-identical to --json), write v5.0.0
verbatim scorecard to stderr
default → humanized scorecard to stderr, no stdout
posture.test.mjs scorecard-prose assertions re-anchored to --raw mode (the
explicit v5.0.0 path) — Wave 0 audit only covered finding-title strings;
scorecard prose surfaces here for the first time.
Wave 3 / Step 6 of v5.1.0 humanizer.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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/
Wave 1 / Step 2 of v5.1.0 plain-language UX humanizer.
scanners/lib/humanizer-data.mjs exports TRANSLATIONS keyed by
scanner prefix (CML, SET, HKV, RUL, MCP, IMP, CNF, GAP, TOK, CPS,
DIS, COL, PLH). Each scanner has:
- static: exact-title -> {title, description, recommendation}
- patterns: array of {regex, translation} for template-literal titles
- _default: graceful fallback for unknown findings
Architectural change vs. plan: keys translations by exact scanner
title (not finding ID). Reason: finding IDs are sequence-based
(global counter in lib/output.mjs:34), not stable per finding-type
— two runs can produce different IDs for the same logical issue.
Title strings ARE stable (defined as string literals or template
patterns in the scanner source).
Translations follow research/03 SR-1..SR-17:
- active voice, second person, present tense
- sentences <= 25 words
- tier1 absolute prohibitions and tier3 domain jargon are kept out
of prose
- tier1/tier3 terms are permitted inside `backtick spans` (code
references like filenames and field names) — established
technical-doc convention
Test (12 cases): all 13 scanners covered; every static and pattern
entry has the 3 required fields; tier1 and tier3 forbidden-word
checks pass (with backtick-span exclusion); reference-stable
imports. All pass.
Regression: 657/657 tests (645 + 12 new).
Project: .claude/projects/2026-05-01-config-audit-ux-redesign/
Wave 1 / Step 1 of v5.1.0 plain-language UX humanizer.
tests/lint-forbidden-words.json defines the SC-3 forbidden-words
vocabulary used by the lint runner (Wave 4 / Step 8) and the
humanizer-data translation guard (Wave 1 / Step 2).
- Tier 1: 19 absolute prohibitions (failure if matched in default
output) — sourced from Microsoft Writing Style Guide, Federal
Plain Language, GOV.UK, Google Developer Style, Apple HIG.
- Tier 2: 24 strong-avoidance terms (warning if matched) — same
sources plus Mailchimp.
- Tier 3: 12 domain-specific jargon terms (failure if matched in
default output, allowed in --raw and --json paths) — sourced
from research/03 jargon table.
Counts diverge from plan.md (18/21/11) — JSON tracks the brief's
verbatim lists at research/03 lines 200-202 plus tier3 hook entry
from the brief's table. Plan revision noted in audit-doc.
Test: 10 cases verifying parse, count, schema completeness, spot
checks per tier, no cross-tier duplicates. All pass.
Regression: 645/645 tests (635 + 10 new).
Project: .claude/projects/2026-05-01-config-audit-ux-redesign/
Two MCP enumeration paths in readActiveMcpServers now pass kind='mcp'
to estimateTokens with optional toolCount derived from def.tools array
(populated when callers cache MCP discovery — Step 14 wires that up).
Hook callers keep kind='item' (no schema overhead).
Visible effect: every active MCP server jumps from estimatedTokens=15
to >= 500 (or higher when toolCount is known). The whats-active output
and TOK hotspots now reflect actual MCP cost.
Tests: assert mcpServers[].estimatedTokens >= 500 in fixture.
Replace count-based pass-rate with severity-weighted penalty:
- penalty = sum(count[s] * WEIGHTS[s])
- maxBudget = max(10, findingCount * 4)
- passRate = max(0, 100 - penalty / maxBudget * 100)
A few lows no longer crater an area's grade; a single high or critical
consumes a large fraction of budget. Mirrors the operator intuition that
severity, not count, is the signal.
BREAKING (intentional): scoring semantics differ from v4 for non-clean
configs. Add scoringVersion: 'v5' to the returned struct so consumers
can detect the version. baseline-all-a remains all-A (no critical/high
on that fixture).
Tests: +6 cases for severity weighting; existing "many findings" test
updated to use highs (where v5 still drops the grade as expected).
Promote WEIGHTS const to named export with Object.freeze for downstream
use in scoring.mjs (severity-weighted scoreByArea, F3).
Tests: +2 cases asserting WEIGHTS shape.
New read-only command that shows everything Claude Code actually loads for a
given repo — plugins, skills, MCP servers, hooks, CLAUDE.md cascade — with
source attribution (user/project/plugin) and rough token estimates. Helps
identify candidates for disabling without guessing.
Added:
- scanners/lib/active-config-reader.mjs — pure async helper: readActiveConfig,
detectGitRoot, walkClaudeMdCascade, readClaudeJsonProjectSlice (longest-prefix
matching for .claude.json projects), enumeratePlugins, enumerateSkills,
readActiveHooks, readActiveMcpServers, estimateTokens (markdown 4 c/tok,
json 3.5 c/tok, frontmatter cap 150 tokens, item flat 15)
- scanners/whats-active.mjs — thin CLI shim: --json, --output-file, --verbose,
--suggest-disables
- commands/whats-active.md — renders tables via Read tool; honors UX rules
- tests/lib/active-config-reader.test.mjs — 36 tests, all green (integration
fixture built in tmpdir with fake HOME, .claude.json prefix matching,
plugin discovery, hook/MCP merge from all scopes)
Verified:
- Performance budget: <2s wall-clock (smoke test: 102ms on real repo)
- Token estimates within ±20% of hand-computed values
- Read-only: no writeFile/mkdir/unlink in production code
- Self-audit: Plugin Health scanner reports 0 findings (Grade A)
- Full test suite: 522 tests, 512 pass (10 pre-existing conflict-detector
failures on main — unrelated to this change, reproducible on clean HEAD)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
