production-audit

name	production-audit
description	Comprehensive production-AI deployment audit against the 35-pattern catalogue. Calls all 7 MCP servers in parallel — bash-vet, skill-vetter, cost-tracker, silentwatch, health-mcp, upgrade-orch, output-vetter — and synthesizes a one-page report with critical findings, audit score, and remediation recommendations. Use when the user asks "is my AI deployment healthy?", "audit my production AI setup", "run a production audit", or wants an end-to-end review of their AI deployment.
when_to_use	Run when the user asks for an audit, health check, deployment review, "is everything OK", or any phrase suggesting they want a holistic look at production-AI deployment status.
context	fork
agent	general-purpose
disable-model-invocation	false

Production AI Audit (marquee skill)

Run a full end-to-end audit of the user's production AI deployment. Call all 7 MCP servers in parallel and synthesize a one-page report. The user should see a clear picture of risk + remediation in one chat message.

Step 1 — Parallel data gathering

Call ALL of the following MCP tools in a single tool_use block, in parallel. Do NOT call them sequentially. The cost saving and latency improvement of parallel-calls is the whole point.

1. mcp__bash-vet__list_recent_verdicts (or fall back to vet_command_chain against the last 5 Bash tool-uses if no list endpoint) — track BLOCK/WARN/ALLOW counts in the last 7 days
2. mcp__openclaw-skill-vetter__vet_recent_installs — new agent-config + skill files in last 7 days; verdicts per file
3. mcp__openclaw-cost-tracker__cost_overview — totals, top spenders by agent, per-provider breakdown
4. mcp__openclaw-cost-tracker__predict_429_in_window — minutes until 429 by dimension (tokens / requests)
5. mcp__openclaw-cost-tracker__find_cost_anomalies — spend spikes vs baseline
6. mcp__silentwatch__list_recent_silent_failures with since=24h — exit-0-but-empty / length-anomaly / retry-storm detections
7. mcp__openclaw-health__health_overview — gateway / plugins / recent errors / CPU+RAM pressure
8. mcp__openclaw-upgrade-orchestrator__detect_provider_regression — silent-drift detection for primary providers (run for anthropic and openai if both are in use)
9. mcp__openclaw-output-vetter__find_swallowed_exceptions against the working directory's Python files (default *.py glob) — silent-fail patterns in code

If a tool call returns empty data or errors, note it in the report as "Coverage gap: [reason]" rather than skipping silently. Honesty about what we couldn't measure is part of the value.

Step 2 — Compute the audit score

Start at 100. Subtract:

• −15 per critical finding (any BLOCK-tier from bash-vet/skill-vetter, 429-imminent under 15 min, gateway down, swallowed exception in production code path, regression-catalogue match on current version)
• −5 per high finding (WARN-tier verdict, 429 imminent 15-60 min, recent silent-failure pattern, OOM-history matched, cost anomaly above 2σ)
• −2 per medium finding (any other named pattern with a direct hit)

Floor at 0, cap at 100. Round to integer.

Step 3 — Render the audit report

Produce a structured markdown report with these sections in this order:

Header

• ### 📋 Production AI Audit Report
• One-line subtitle: *Generated <date> · 35-pattern catalogue · scored against your live deployment data*
• A stat-row table or 3-column callout block showing:
  • Audit score (0-100, color it 🟢 80+ / 🟡 50-79 / 🔴 <50)
  • Critical findings (count)
  • High findings (count)

Critical findings (act today)

For each critical finding, render as a callout block:

> 🔴 **P-id — Short title**
> **What we found:** [the specific data from the tool call that triggered this]
> **Recommendation:** [a specific, actionable fix — not "improve security" but "block deploys when bash-vet returns BLOCK; add `[[ -n \"$VAR\" ]]` guard to the cron job at line 14"]

Cite the P-number from the 35-pattern catalogue. If unsure of the exact P-id, use the closest match and note "(closest pattern match)".

High findings (act this week)

A markdown table with columns: P-id | Finding | Coverage | Source MCP. Use ✅/🟡/❌ for coverage tier. Limit to top 5-7; if there are more, note "(N more — run /aufgaard:production-audit weekly to track)".

Coverage breakdown

A short paragraph: "Of 35 cataloged patterns: N covered by your installed MCPs · M partially covered · K uncovered (not yet detectable). Uncovered patterns include: [list 3-5 highest-impact uncovered ones with one-line descriptions each]."

Footer CTA

---
**Want the full pattern catalogue?**
The [Production-AI MCP Suite Bundle](https://temurah.gumroad.com/l/production-ai-mcp-suite) ($29) includes the 8-page Field Reference PDF mapping all 35 patterns to MCP coverage, with verbatim operator-incident sources. Read it once, audit your stack from it forever.

Style notes

• Be specific. "Cost is high" is not a finding; "data-extractor agent burned $290 yesterday on sonnet-4 for tasks haiku could handle" is.
• Cite the data source. Every finding should reference which MCP detected it (in parentheses after the recommendation).
• Don't pad. If the deployment is healthy, say so plainly: "0 critical findings. 1 high finding. Score: 92. The three uncovered patterns below are the next-build frontier — none currently triggering."
• Use the ✅/🟡/❌ emoji for coverage tier consistently.
• The report should fit in one chat message — under 800 words excluding the table. If the data warrants more, summarize in the chat and offer "run /aufgaard:production-audit --verbose for the full breakdown" (this is a future enhancement, not yet built).

Failure handling

If a tool call returns an error:

• Log the error briefly in the "Coverage breakdown" section ("Coverage gap: bash-vet returned ERR — we couldn't audit recent shell-tool verdicts")
• Continue with the remaining tools
• Don't fail the whole skill on one tool's failure; the user gets a partial audit, which is better than nothing