eval-cost

name	eval-cost
description	Use when the user asks to make an AI app cheaper or faster, reduce tokens, latency, model/tool/retrieval/rerank/self-check/retry/evaluator cost, or compare cost before/after.

Eval Cost

Use this skill for tokenomics RCA. Cost changes are accepted only when Galileo quality metrics do not regress.

Conditional References

• Load skills/eval-engineer/references/tokenomics-rca.md when choosing the tokenomics workflow or diagnosing why cost moved.
• Run skills/eval-engineer/scripts/compare_tokenomics_packets.py when both baseline and verification packets exist.
• Load skills/eval-engineer/assets/cost-diagnosis-template.md, skills/eval-engineer/assets/tokenomics-fix-plan-template.md, and skills/eval-engineer/assets/quality-preserving-verification-template.md only when writing those artifacts.

Do

• Compare cost, latency, tokens, retrieved context, tool calls, retries, rerank/self-check spans, model spans, and evaluator cost.
• Run compare_tokenomics_packets.py without explicit quality metrics first when packets use custom quality names, then inspect the inferred Quality metrics compared list before accepting the decision.
• Treat behavior counters such as handoff count, tool count, step count, retry count, and self-check count as efficiency or workflow evidence, not quality gates by default. Promote one to quality only when the metric profile states the desired direction for that route or segment.
• Protect named quality metrics and segment gates.
• Reject cheaper candidates when aggregate quality holds but any required segment gate regresses.
• Treat lower traffic volume as inconclusive unless per-trace efficiency also improves.

Gotchas

• Cost, latency, wall time, duration, token count, span count, and tool count are efficiency evidence, not quality gates by default.
• Quality metrics are not always higher-is-better. Error, toxicity, hallucination, policy-violation, and tool-error rates regress when they rise.
• Aggregate quality can hide route, risk-profile, or customer-segment regressions.
• For RAG pruning, include hard multi-source or multi-hop cases before keeping a top-k reduction.
• For agentic workflows, compare agent steps, planner spans, rerank passes, self-check spans, and tool calls so the cheaper loop is explainable.

Validation Loop

Before keeping a candidate:

1. Compare baseline and verification packets with compare_tokenomics_packets.py.
2. Inspect inferred quality metrics before accepting the decision.
3. Check segment metrics when available.
4. Confirm metric gaps are recorded as inconclusive or follow-up work.
5. Reject or revise the candidate if quality evidence is missing or regressed.

Output

State keep/reject/inconclusive first. Explain why the cost moved, which quality metrics do not regress, and which latency/tool-count tradeoffs remain.