菜单
Glossary of production-AI failure modes with verbatim operator phrasings. When the user uses any of these phrases or describes a symptom matching them, you'll recognize the pattern and respond with the right framing + MCP routing. Auto-loaded so the buyer-vocabulary is always available.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
| name | failure-mode-glossary |
| description | Glossary of production-AI failure modes with verbatim operator phrasings. When the user uses any of these phrases or describes a symptom matching them, you'll recognize the pattern and respond with the right framing + MCP routing. Auto-loaded so the buyer-vocabulary is always available. |
| when_to_use | Anytime the user describes a symptom in their own words. Translate buyer-vocabulary to P-numbered patterns and the right MCP. Cite the named pattern in your response. |
| user-invocable | false |
When users report problems, they don't say "P3.4 HERMES.md" — they say "my Anthropic bill jumped overnight". This glossary maps buyer-vocabulary to the named pattern + the MCP that catches it.
predict_429_in_window + cost_overview)predict_429_in_windowcost_overview (per-agent breakdown)vet_command_chainvet_command_chain (chain_mode=true)list_recent_silent_failuresvet_agent_configverify_action_outcomefind_swallowed_exceptionscheck_known_regressionsdetect_provider_regression (April-23 case)health_overview/aufgaard:production-audit/aufgaard:production-auditvet_command_chain catches this — let me run it on the command you're worried about."Show current production AI cost overview — totals, top spenders by agent, per-provider breakdown, anomaly detection, and time-to-429 rate-limit prediction. Use when the user asks "what's my AI bill", "which agent is burning tokens", "am I about to hit the rate limit", or wants a cost dashboard. Cross-provider — works for Anthropic, OpenAI, Gemini, Bedrock, Ollama.
Scan Python source files for swallowed-exception patterns that silently turn errors into fake successes. Catches bare `except` blocks that pass / return None / return mock objects, log-and-fake-success handlers, and mock-substitution-on-error. AST-based — not just regex. Use before any deploy of new agent code, on the working directory after a bug fix, or routinely on production-path Python files.
Show current AI deployment health overview — gateway status, plugin/skill registry, recent errors, CPU/RAM pressure, OOM history, cron status, disk pressure, upgrade outcome. The "vital signs" panel for a production AI deployment. Use when the user asks "is my deployment healthy", "what's wrong with my agent", "are services up", or wants an at-a-glance status check.
One-page operator cheatsheet for the Aufgaard plugin. The "what to check, when, why" reference. Auto-loaded so day-to-day routing is fast. Use as the lightweight summary when the user wants a quick recap of capabilities.
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.
Check whether upgrading a package / runtime / model version is safe. Looks up the user-driven regression catalogue (8+ entries from real field reports) AND runs provider-side regression detection (catches Anthropic-April-23-style silent reasoning-effort downgrades). Returns a recommended upgrade path with mitigations. Use before any significant package upgrade, model-version bump, or runtime change.