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/audit-usage

Generate a value/ROI report for YOUR knowledge corpus — cost-surface token math plus prospect/retrospect quality distributions, month trends, and an honest confound-aware read of whether ARIA is earning its keep. Opt-in, never routine. Trigger: '/audit usage', 'is ARIA worth

From plugin
aria-knowledge
1740 skills1 command12 MCP
Install
$ npx -y skills add mikeprasad/aria-knowledge --skill audit-usage --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/audit-usage

Context preview

The summary Claude sees to decide when to auto-load this skill.

Generate a value/ROI report for YOUR knowledge corpus — cost-surface token math plus prospect/retrospect quality distributions, month trends, and an honest confound-aware read of whether ARIA is earning its keep. Opt-in, never routine. Trigger: '/audit usage', 'is ARIA worth

SKILL.md

audit-usage.SKILL.md
description: "Internal facet of the audit family — invoke via '/audit usage'. Value/ROI report over your own knowledge corpus (cost surface + quality distributions + trends). (Code port — ADR-094.)"

/audit usage — Value/ROI Self-Analysis

Canonical invocation: **`/audit usage`**. The direct `/audit-usage` form is retained for compatibility and is not advertised.

Generate a value-analysis report computed against the user's OWN knowledge corpus — the user-facing counterpart to the plugin's published `docs/value-analysis.md` (which is the author's N=1 digest). Deterministic metrics come from `bin/usage-metrics.sh`; the interpretive narrative is written here, gated on sample size.

Step 0: Resolve Config

Read `~/.gemini/antigravity/aria-knowledge.local.md`; extract `knowledge_folder`. If missing: stop with "aria-knowledge is not configured. Run /setup to get started."

Step 1: Gather Metrics

Run the deterministic emitter once:

bash ${CLAUDE_PLUGIN_ROOT}/bin/usage-metrics.sh

Parse the labeled block (each line is `KEY value`, or `KEY subkey value` for month buckets). If the output contains `USAGE_METRICS_ERROR`, stop and tell the user their knowledge_folder is unset or missing.

If `PROSPECT_TOTAL` and `RETRO_TOTAL` are both 0: stop with "No prospect/retrospect history yet — run some /prospect and /retrospect cycles, then check back. (Cost-surface metrics are still available; say 'cost only' to see them.)" Do not fabricate a report.

Step 2: Write the Analysis (sample-size honest)

Compose the report over the user's numbers. Sections:

1. **TL;DR** — one-line verdicts (needs-changes rate = `PROSPECT_PWC`/`PROSPECT_TOTAL`, clean rate, per-fix-verdict rate = `RETRO_VERDICT_FILES`/`RETRO_TOTAL`, fixed cost = `SKILL_DISCOVERY_BYTES`÷4 tokens). 2. **Cost surface** — `SKILL_DISCOVERY_BYTES` (÷4 ≈ tokens), `SKILL_COUNT`, per-session floor note; state it's the universal fixed cost every session pays (mostly cache-eligible if the session stays warm). 3. **Quality — plan rigor** — prospect distribution table with `n = PROSPECT_TOTAL`. Interpret the PWC/clean/hold split (PWC = plans that needed pre-execution correction). 4. **Quality — validation discipline** — retrospect outcome table (`RETRO_CLOSED`/`PARTIAL`/`MIXED`/`UNRESOLVED`) + per-fix-verdict rate. 5. **Trends** — month tables from `PROSPECT_MONTH` / `RETRO_MONTH` **only for months whose total ≥ 10 logs**. Any month below 10: omit from the table and note "insufficient sample — directional only." If NO month clears 10, print "Not enough history for a trend yet (need ≥10 logs in a month)." — no table. 6. **Confounds + limits** — always include the standing caveats: author-learning, the tool trains its own user, work-mix shift; and the N=1 limit (this is the user's own corpus, not a controlled study — no counterfactual proof that uncorrected plans would have shipped wrong).

Never assert "improving over time" unless at least two months each clear the ≥10 threshold AND the direction is monotonic. Otherwise say "directional only."

Step 3: Persist

Write `{knowledge_folder}/references/usage-analysis.md` (full rewrite each run) with frontmatter — get the timestamp via `date -u +%Y-%m-%dT%H:%M:%SZ`:

--- synthesized_at: <UTC now> measured_at_corpus: <PROSPECT_TOTAL>/<RETRO_TOTAL> plugin_version: <from plugin.json if resolvable, else "unknown"> ---

followed by the Step 2 report body. This is the one file the skill creates. Create the `references/` directory if absent.

Step 4: Report

Print a 3-4 line inline summary (needs-changes rate, clean rate, per-fix-verdict rate, fixed-token cost) and "Full report written to {knowledge_folder}/references/usage-analysis.md".

Rules

  • **Opt-in only** — never fired by a cadence nudge; only explicit `/audit usage` or a menu pick.
  • **Honest on small samples** — print every `n`; gate trends on ≥10/month; never fabricate on a zero corpus.
  • **Metrics are the script's job** — do not re-derive counts inline; `bin/usage-metrics.sh` is the single source of truth. The skill only interprets and persists.
  • **The user's corpus, not the author's** — the report reflects THIS user's logs; do not import the published doc's numbers.
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Ships witharia-knowledge

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