137 lines
3.8 KiB
Markdown
137 lines
3.8 KiB
Markdown
---
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description: "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.\n"
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---
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# Review Caveman evidence
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Act as a read-only operator. Build conclusions from current Caveman data, not
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from repository guesses. Never start, approve, cancel, or roll back an
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experiment from this skill.
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## Hard rules
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1. Keep these buckets separate:
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- measured provider-complete list-price cost;
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- `inferred` daily headroom;
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- `verified` ledger savings;
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- evidence cost.
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Never add or relabel them.
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2. Do not fetch prompt, completion, tool, or artifact payloads unless the user
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explicitly asks for payload review. Metadata, spans, timing, models, token
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counts, status, and optimizer attribution are enough for the default review.
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3. Scope every read to the project selected by Caveman context. Never supply an
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organization id.
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4. Empty results are evidence of no current signal, not zero cost or zero risk.
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5. Cite trace ids and exact time windows used. Do not claim a cause from an
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aggregate alone.
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## Step 1 — Load context
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Prefer MCP:
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```text
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caveman_context {}
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```
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CLI fallback:
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```bash
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caveman cloud whoami
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caveman cloud projects list
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```
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Stop if login or project selection is missing. Ask the user to run
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`caveman login` or select a project; never guess.
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## Step 2 — Establish baseline
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Use `caveman_report` for:
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- `overview`
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- `costs`
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- `score`
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- `workflows`
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- `verified_savings`
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Then use `caveman_plan` for ranked daily headroom. If question is narrow, skip
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unrelated reports. Read shortest set that can answer it.
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CLI fallback:
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```bash
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caveman cloud costs
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caveman cloud score
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caveman cloud plan --json
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```
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State report window and basis before interpreting direction.
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## Step 3 — Test the leading explanation with traces
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Use `caveman_trace_search`. Choose a bounded window and closed filters:
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workflow, agent, model, provider, error code, runtime mode, cache status,
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optimization id, status class, token/cost/latency bounds, compression, or
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monitor verdict.
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Useful groupings:
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- `workflow` — find jobs driving cost or failures;
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- `model` — compare model mix;
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- `session` — isolate retry or loop behavior;
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- ungrouped — identify exact traces.
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Compare a suspect cohort with a control cohort or earlier bounded window.
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Do not infer causality from one expensive trace.
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CLI fallback:
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```bash
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caveman cloud traces search \
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--workflow <slug> \
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--from <RFC3339> \
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--to <RFC3339> \
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--sort total_cost_usd \
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--dir desc \
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--limit 25
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```
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## Step 4 — Inspect representative traces
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Call `caveman_trace_get` for a small number of high-signal trace ids. Inspect
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request and span metadata, latency, status, token counts, cache state, applied
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optimizers, and model route. Keep payload retrieval off.
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CLI fallback:
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```bash
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caveman cloud traces show <trace-id> --spans
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```
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## Step 5 — Report
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Use this shape:
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```text
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## Caveman evidence review
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Scope: <project> · <from> to <to>
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Measured cost: <value and basis>
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Verified savings: <ledger value, kept separate>
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Inferred headroom: <per-day band, kept separate>
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Findings:
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1. <finding> — <aggregate evidence> — traces <ids>
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2. <finding> — <aggregate evidence> — traces <ids>
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Unproven:
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- <plausible explanation lacking a control, trace, or eval>
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Next read-only check:
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- <one bounded query>
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Possible action:
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- <proposal only; use caveman-manage for read-only lifecycle review and safety gate>
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```
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If data is missing, name missing signal and stop at strongest supported
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statement. Never turn a catalog subtotal into an invoice or an experiment result
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into verified savings.
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