feat(humanizer): update audit/analysis command templates group A [skip-docs]

Wave 5 Step 13. Threads the humanizer vocabulary through five audit/
analysis command templates and adds a shape test that locks the
structure in place.

- commands/posture.md, tokens.md, feature-gap.md (findings-renderers):
  reference userImpactCategory/userActionLanguage/relevanceContext;
  remove hardcoded A/B/C/D/F-to-prose tables (humanizer owns the
  grade-context vocabulary now via the stderr scorecard headline).
- commands/manifest.md, whats-active.md (inventory CLIs): add --raw
  pass-through for CLI-surface consistency. --raw is a no-op in these
  CLIs, but the flag is threaded through so users get uniform behaviour.
- All five files: --raw flag parsed from $ARGUMENTS and passed verbatim
  to the underlying scanner CLI when present.

tests/commands/group-a-shape.test.mjs (new, +5 tests, 767 → 772):
  - structural: every file has a bash invocation block, Read tool
    reference, and --raw/$ARGUMENTS plumbing
  - findings-renderers only: at least one humanized field referenced;
    no hardcoded "[grade] grade is..." prose tables

plugins/config-audit/commands/tokens.md

@@ -28,16 +28,21 @@ Complementary to `/config-audit whats-active`:
 Split `$ARGUMENTS` into a path and flags. Path is the first non-flag argument. Default to `.` (current working directory). Recognized flags:
 
 - `--global` — also include the user-level `~/.claude/` cascade
-- `--json` — emit raw JSON instead of rendered tables (power-user mode)
+- `--json` — emit raw JSON instead of rendered tables (power-user mode; bypasses the humanizer for byte-stable v5.0.0 output)
+- `--raw` — pass-through to the scanner; produces v5.0.0 verbatim JSON (bypasses the humanizer). Use when piping into v5.0.0-baseline diff tooling.
 - `--with-telemetry-recipe` — include `telemetry_recipe_path` in the JSON output, pointing to `knowledge/cache-telemetry-recipe.md`. Use this when you want to verify a structural fix actually improved cache hit rate (manual jq recipe, opt-in)
 
 ### Step 2: Run the CLI silently
 
 Tell the user: **"Analysing token hotspots for `<path>`..."**
 
+Default mode (no `--json`, no `--raw`) emits a humanized JSON envelope: each finding carries `userImpactCategory`, `userActionLanguage`, and `relevanceContext` in addition to the v5.0.0 fields. Pass `--raw` through verbatim if the user requested it.
+
 ```bash
 TMPFILE="/tmp/config-audit-tokens-$$.json"
-node ${CLAUDE_PLUGIN_ROOT}/scanners/token-hotspots-cli.mjs <path> --output-file "$TMPFILE" [--global] 2>/dev/null; echo $?
+RAW_FLAG=""
+if echo "$ARGUMENTS" | grep -q -- "--raw"; then RAW_FLAG="--raw"; fi
+node ${CLAUDE_PLUGIN_ROOT}/scanners/token-hotspots-cli.mjs <path> --output-file "$TMPFILE" [--global] $RAW_FLAG 2>/dev/null; echo $?
 ```
 
 **Exit code handling:**
@@ -58,10 +63,10 @@ Use the Read tool on `$TMPFILE`. Extract:
 
 - `total_estimated_tokens` — top-line number
 - `hotspots[]` — top 10 ranked sources
-- `findings[]` — Opus 4.7 pattern findings (CA-TOK-001..003)
+- `findings[]` — Opus 4.7 pattern findings (CA-TOK-001..003); each finding in default mode carries humanizer fields (`userImpactCategory`, `userActionLanguage`, `relevanceContext`) alongside the v5.0.0 fields
 - `counts` — severity breakdown
 
-Render as markdown:
+Render as markdown. Group findings by `userImpactCategory` (e.g., "Wasted tokens" vs "Configuration mistake") rather than re-deriving severity prose; lead each line with `userActionLanguage` ("Fix this now", "Fix soon", "Optional cleanup", etc.) so the urgency phrasing stays consistent with the rest of the toolchain. The humanizer already replaced jargon-heavy `title`/`description`/`recommendation` strings with plain-language equivalents — render them verbatim.
 
 ```markdown
 **Token hotspots for `<path>`** — ~{total_estimated_tokens} estimated tokens loaded per turn
@@ -72,13 +77,14 @@ Render as markdown:
 |------|--------|--------|-----------------|
 | {rank} | `{source}` | ~{estimated_tokens} | {recommendations joined as `· ` bullets} |
 
-### Opus 4.7 pattern findings
+### Findings, grouped by impact
 
-{For each finding, render:}
+{Group findings[] by their userImpactCategory. Within each group, sort by userActionLanguage urgency (Fix this now → Fix soon → Fix when convenient → Optional cleanup → FYI), then render:}
 
-- **{id}** ({severity}) — {title}
+- **{userActionLanguage}** — {title}  ({id})
   - {description}
   - **Fix:** {recommendation}
+  - _{relevanceContext}_ when not "affects-everyone" (mention the scope so the user knows whether a fix touches shared config or just their machine)
 
 ### Severity summary