caveman-evidence-review

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:

caveman_context {}

CLI fallback:

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:

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.