洞穴人证据审查

--- name: caveman-evidence-review description: > Read-only review of Caveman Cloud evidence: cost, Cave Score, workflows, traces, latency, errors, routing, savings. Use when asked what Caveman found or where LLM spend goes. --- # Review Caveman evidence Act as a read-only operator. Build conclusions from current Caveman data, not from repository guesses. Never start, approve, cancel, or roll back an experiment from this skill. ## Hard rules 1. Keep these buckets separate: - measured provider-complete list-price cost; - `inferred` daily headroom; - `verified` ledger savings; - evidence cost. Never add or relabel them. 2. Do not fetch prompt, completion, tool, or artifact payloads unless the user explicitly asks for payload review. Metadata, spans, timing, models, token counts, status, and optimizer attribution are enough for the default review. 3. Scope every read to the project selected by Caveman context. Never supply an organization id. 4. Empty results are evidence of no current signal, not zero cost or zero risk. 5. Cite trace ids and exact time windows used. Do not claim a cause from an aggregate alone. ## Step 1 — Load context Prefer MCP: ```text caveman_context {} ``` CLI fallback: ```bash caveman cloud whoami caveman cloud projects list ``` Stop if login or project selection is missing. Ask the user to run `caveman login` or select a project; never guess. ## Step 2 — Establish baseline Use `caveman_report` for: - `overview` - `costs` - `score` - `workflows` - `verified_savings` Then use `caveman_plan` for ranked daily headroom. If question is narrow, skip unrelated reports. Read shortest set that can answer it. CLI fallback: ```bash caveman cloud costs caveman cloud score caveman cloud plan --json ``` State report window and basis before interpreting direction. ## Step 3 — Test the leading explanation with traces Use `caveman_trace_search`. Choose a bounded window and closed filters: workflow, agent, model, provider, error code, runtime mode, cache status, optimization id, status class, token/cost/latency bounds, compression, or monitor verdict. Useful groupings: - `workflow` — find jobs driving cost or failures; - `model` — compare model mix; - `session` — isolate retry or loop behavior; - ungrouped — identify exact traces. Compare a suspect cohort with a control cohort or earlier bounded window. Do not infer causality from one expensive trace. CLI fallback: ```bash caveman cloud traces search \ --workflow \ --from \ --to \ --sort total_cost_usd \ --dir desc \ --limit 25 ``` ## Step 4 — Inspect representative traces Call `caveman_trace_get` for a small number of high-signal trace ids. Inspect request and span metadata, latency, status, token counts, cache state, applied optimizers, and model route. Keep payload retrieval off. CLI fallback: ```bash caveman cloud traces show --spans ``` ## Step 5 — Report Use this shape: ```text ## Caveman evidence review Scope: · to Measured cost: Verified savings: Inferred headroom: Findings: 1. — — traces 2. — — traces Unproven: - Next read-only check: - Possible action: - ``` If data is missing, name missing signal and stop at strongest supported statement. Never turn a catalog subtotal into an invoice or an experiment result into verified savings.