config-audit/commands/posture.md

name	description	argument-hint	allowed-tools	model
config-audit:posture	Quick configuration health assessment — scorecard with A-F grades	[path] [--drift] [--plugin-health]	Read, Write, Glob, Grep, Bash	sonnet

Config-Audit: Health Assessment

Quick, deterministic configuration health scorecard. No agents needed — runs all scanners + scoring in one pass.

What the user gets

• Health grade (A-F) with plain-language explanation
• Per-area breakdown for 10 quality areas (incl. Token Efficiency, Plugin Hygiene) with grades and actionable notes
• Opportunity count — how many features could enhance their setup (not a grade)
• Grade-appropriate next steps

Implementation

Step 1: Determine target and flags

Split $ARGUMENTS into a path and flags. Path is the first non-flag argument (default: current working directory). Resolve relative paths. Recognized flags:

• --raw — pass-through to the scanner; produces v5.0.0 verbatim output (bypasses the humanizer). Power-user mode for byte-stable diffs and machine consumption.
• --drift — append a "Configuration Drift" section (see Step 5).
• --plugin-health — append a "Plugin Health" section (see Step 5).

Tell the user:

## Configuration Health

Running quick assessment{if path != cwd: " on `{path}`"}...

Step 2: Run posture scanner

Run silently — JSON goes to a file, the humanized scorecard prints to stderr (default mode). The humanized stderr scorecard already includes the grade headline and area-score lines in plain language, so render those directly rather than re-deriving prose tables.

RAW_FLAG=""
if echo "$ARGUMENTS" | grep -q -- "--raw"; then RAW_FLAG="--raw"; fi
node ${CLAUDE_PLUGIN_ROOT}/scanners/posture.mjs <target-path> --output-file /tmp/config-audit-posture-$$.json $RAW_FLAG 2>/tmp/config-audit-posture-stderr-$$.txt; echo $?

If exit code is non-zero, tell the user: "Assessment couldn't complete. Check that the path exists and contains Claude Code configuration files."

If --raw was passed, treat the captured stderr as v5.0.0-shape verbatim text and present it as-is in a code block; skip the humanized rendering steps below.

Step 3: Read and interpret results

Read the JSON output file using the Read tool. Extract:

• overallGrade, opportunityCount
• areas[] — each with name, grade, score, findingCount
• scannerEnvelope.scanners[].findings[] — when surfacing individual findings, prefer the humanizer-provided fields: userImpactCategory (e.g., "Configuration mistake", "Wasted tokens"), userActionLanguage (e.g., "Fix this now", "Fix soon", "Optional cleanup"), and relevanceContext ("affects-everyone", "affects-this-machine-only", "test-fixture-no-impact"). These let you group and prioritize without hardcoded severity-to-prose mappings.

Also Read the captured stderr file — its body is the humanized scorecard (grade headline, area-score block, opportunity hint). You can present it verbatim or interleave its lines with the JSON-driven table.

Step 4: Present the scorecard

**Health: {overallGrade}** | {qualityAreaCount} areas scanned

{Use the headline line from the humanized stderr scorecard — it carries grade-context prose already (e.g., " Health: A (97/100) — Healthy setup, only minor polish needed"). Do not re-derive an A/B/C/D prose table here; the humanizer owns that vocabulary.}

### Area Scores

| Area | Grade | Score | Findings | |
|------|-------|-------|----------|-|
{for each area EXCEPT Feature Coverage:}
| {name} | {grade} | {score}/100 | {findingCount} | {plain-language note: A="Excellent", B="Good", C="Needs work", D/F="Issues found"} |

{if opportunityCount > 0:}
{opportunityCount} feature opportunities available — run `/config-audit feature-gap` for context-aware recommendations.

### What's next

Group "what's next" suggestions by userActionLanguage from the humanized findings:

• Findings tagged "Fix this now" / "Fix soon" → suggest /config-audit fix first, then /config-audit plan.
• Findings tagged "Fix when convenient" / "Optional cleanup" → suggest /config-audit feature-gap and routine maintenance.
• No high-urgency findings → suggest /config-audit feature-gap for opportunities and re-running posture after major config changes.

Avoid hardcoded grade-to-prose ladders here — the humanized scorecard headline already supplies grade context, and userActionLanguage supplies finding-level urgency.

Step 5: Optional sections

If --drift flag is present:

Run drift comparison silently:

node ${CLAUDE_PLUGIN_ROOT}/scanners/drift-cli.mjs <target-path> --output-file /tmp/config-audit-posture-drift-$$.json 2>/dev/null; echo $?

Read that file and append a "Configuration Drift" section showing what changed since the last baseline. Both scanners report to stderr in default mode, which 2>/dev/null discards — the payload is the only readable output.

If --plugin-health flag is present:

Run plugin health scanner silently:

node ${CLAUDE_PLUGIN_ROOT}/scanners/plugin-health-scanner.mjs <target-path> --output-file /tmp/config-audit-posture-plh-$$.json 2>/dev/null; echo $?

Read that file and append a "Plugin Health" section, using its plugins[] rows for per-plugin grades.

If both flags: Use scanners/lib/report-generator.mjs to produce a unified markdown report.

Step 6: Save to session (if active)

If a config-audit session exists, save results:

node ${CLAUDE_PLUGIN_ROOT}/scanners/posture.mjs <target-path> --json --output-file ~/.claude/config-audit/sessions/<session-id>/posture.json 2>/dev/null