79b6e29073
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
81 lines
3.3 KiB
Markdown
81 lines
3.3 KiB
Markdown
---
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name: config-audit:manifest
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description: Show ranked token-source manifest — every CLAUDE.md, plugin, skill, MCP server, and hook ordered DESC by estimated tokens
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argument-hint: "[path] [--json]"
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allowed-tools: Read, Bash
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model: sonnet
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---
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# Config-Audit: Manifest
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Produce a ranked, single-table view of every token source loaded for a given repo path. Where `whats-active` shows separate tables per category, `manifest` collapses everything into one ordered list — making it easy to see what's costing the most regardless of category.
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## UX Rules (MANDATORY — from `.claude/rules/ux-rules.md`)
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1. **Never show raw JSON or stderr output.** Always use `--output-file` + `2>/dev/null`.
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2. **Narrate before acting.** Tell the user what you're about to do.
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3. **Read, don't dump.** Read the JSON file and render a formatted table.
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4. **End with context-sensitive next steps.**
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## Implementation
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### Step 1: Parse `$ARGUMENTS`
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First non-flag argument is the path (default `.`). Recognized flags:
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- `--json` — emit raw JSON instead of the rendered table.
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- `--raw` — pass-through to the scanner; accepted for CLI surface consistency with the other config-audit commands. The manifest CLI is data-table only (no findings prose), so `--raw` is a no-op here, but the flag is still threaded through so users get uniform behaviour across `--raw`.
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### Step 2: Run the CLI silently
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Tell the user: **"Building token-source manifest for `<path>`..."**
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```bash
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TMPFILE="/tmp/ca-manifest-$$.json"
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RAW_FLAG=""
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if echo "$ARGUMENTS" | grep -q -- "--raw"; then RAW_FLAG="--raw"; fi
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node ${CLAUDE_PLUGIN_ROOT}/scanners/manifest.mjs <path> --output-file "$TMPFILE" $RAW_FLAG 2>/dev/null; echo $?
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```
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**Exit code handling:**
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- `0` → continue
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- `3` → tell user: "Couldn't read configuration. Check that the path exists and is a directory." Stop.
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### Step 3: If `--json` was requested, cat the file and stop
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```bash
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cat "$TMPFILE"
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```
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Do NOT render the table in JSON mode.
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### Step 4: Read JSON and render
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Use the Read tool on `$TMPFILE`. Extract `meta.repoPath`, `total`, and `sources[]`. Render the top 20 sources (or fewer if the manifest is shorter):
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```markdown
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**Token-source manifest for `<repoPath>`** — ~{total} tokens at startup
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| Rank | Kind | Name | Source | Tokens |
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|------|------|------|--------|--------|
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| 1 | {kind} | `<name>` | {source} | ~{estimated_tokens} |
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| ... | ... | ... | ... | ... |
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_Estimates assume ~4 chars/token (Claude ballpark). Real token count varies ±15%._
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```
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If `sources.length > 20`, follow the table with: _"Showing top 20 of {N} sources. Run with `--json` to see the full list."_
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### Step 5: Suggest next steps
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```markdown
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**Next steps:**
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- `/config-audit tokens` — Opus-4.7 token-hotspot patterns (cache-breaking, redundant perms, deep imports, MCP budget)
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- `/config-audit whats-active` — same data grouped by category, with disable suggestions
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- `/config-audit feature-gap` — what *could* improve here, grouped by impact
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```
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Tone:
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- High total (>50k): empathetic — "That's a heavy startup cost; tokens bullet anything you'd otherwise spend on the actual conversation."
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- Moderate (10–50k): neutral — "Reasonable. Skim the top 5 to see if anything is unexpectedly large."
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- Low (<10k): encouraging — "Tight setup. The model has plenty of room for the actual work."
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