caveman-evidence-review

Original：🇺🇸 English

Translated

Review Caveman Cloud evidence read-only: costs, Cave Score, Cave Plan, workflows, traces, latency, errors, compression, routing, and verified savings. Use when the user asks what Caveman found, where LLM spend goes, why cost or quality changed, which workflows need attention, or asks for a trace or analytics review. Prefer Caveman MCP tools; fall back to CLI JSON.

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Sourcejuliusbrussee/caveman

Added on2026-08-12

NPX Install

npx skill4agent add juliusbrussee/caveman caveman-evidence-review

SKILL.md Content

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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

Keep these buckets separate:
- measured provider-complete list-price cost;
- ```
inferred
```
  daily headroom;
- ```
verified
```
  ledger savings;
- evidence cost. Never add or relabel them.
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.
Scope every read to the project selected by Caveman context. Never supply an organization id.
Empty results are evidence of no current signal, not zero cost or zero risk.
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 <slug> \
  --from <RFC3339> \
  --to <RFC3339> \
  --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 <trace-id> --spans

Step 5 — Report

Use this shape:

text

## Caveman evidence review

Scope: <project> · <from> to <to>
Measured cost: <value and basis>
Verified savings: <ledger value, kept separate>
Inferred headroom: <per-day band, kept separate>

Findings:
1. <finding> — <aggregate evidence> — traces <ids>
2. <finding> — <aggregate evidence> — traces <ids>

Unproven:
- <plausible explanation lacking a control, trace, or eval>

Next read-only check:
- <one bounded query>

Possible action:
- <proposal only; use caveman-manage for read-only lifecycle review and safety gate>

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.

caveman-evidence-review

NPX Install

Tags

SKILL.md Content

Review Caveman evidence

Hard rules

Step 1 — Load context

Step 2 — Establish baseline

Step 3 — Test the leading explanation with traces

Step 4 — Inspect representative traces

Step 5 — Report