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Capstone synthesis for shipping LLM systems — the full production failure taxonomy (symptom, root cause, detection, mitigation, owner skill), the four-axis inference-stack tradeoff map (latency/quality/cost/reliability), a production-readiness checklist, and incident response for systems that fail silently with no stack trace.
Passed 19 of 23 test cases with the skill, and 15 of 23 without it.
and 3 more in the full report
5 fixed14 passed either way3 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 5 test cases the skill fixed. Same model, same prompt, with and without the skill.
Our production customer service agent is silently returning poor responses to end users. Our traditional HTTP monitoring shows 200 OK statuses and normal uptime, but users are complaining about incorrect answers and missing context since yesterday. Can you give me a structured incident response guide that lays out the exact order of diagnostic checks to run, potential root causes across the inference stack, and canary/rollback procedures to recover safely?
| Checked | Without skill | With skill |
|---|---|---|
| Diagnostic step 1 prioritizes checking deploy logs or recent commits before checking downstream infrastructure. | Passed | Passed |
| Diagnostic step 2 checks provider status and 429 response ratios. | Missed | Passed |
| Diagnostic step 5 specifies comparing current eval scores against a baseline to detect silent model-version drift. | Missed | Passed |
The instructions your model reads when the skill loads.
This is the capstone. The four sibling skills each own one slice of the stack — harness-and-context-engineering (the agent loop, prompts, tools, context packing), rag-architecture (retrieval, grounding, indexing), inference-caching-and-kv (serving, prefix/KV cache, throughput), quantization-and-model-compression (weight/activation precision, model size). This skill ties them together at the layer where they all meet: what breaks in production, and what every fix costs you elsewhere.
A traditional service fails loud — a 500, a stack trace, a thrown exception, an alert. An LLM system mostly fails quiet. The HTTP 200 still comes back. The JSON still parses. The agent still finishes. The answer is just worse — subtly wrong, stale, refused, hallucinated, or off-policy — and nothing in your exception tracker fires.
Three consequences drive everything below:
Eighteen modes in six categories. Columns: symptom (what you observe) · root cause · detection (the signal that catches it) · mitigation · owner skill (where the deep fix lives). "Owner" is a routing hint, not a hard boundary — most real incidents span two.
The model is a text generator wearing a structured-output costume. It will return something shaped like JSON or a tool call even when it has no business doing so. The discipline: **never
test cases passed, vs 15 / 23 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.