npx agentspec-cli install b42e2acc-6dfd-4d8a-a5d6-6bc69c78fef2 --framework ClaudeCode→ .claude/skills/caveman-manage/SKILL.md
# Manage eval-gated experiments
Treat every lifecycle change as a production control action. Read current state
and results, then report one supported recommendation or block.
Current agent MCP is intentionally read-only: control-api does not yet enforce a
complete lifecycle transition table and evidence gate atomically.
## Non-negotiable gates
1. A request to review, inspect, explain, or recommend authorizes reads only.
2. Never approve an experiment whose results are pending, whose required
guardrails are absent, or whose evidence reports a breach.
3. Never convert experiment lift into `verified_savings`. Only active real
traffic plus provider-causal, provider-complete ledger evidence can do that.
4. Never supply an organization id. Project and tenant scope come from the
logged-in Caveman identity and server RBAC.
5. Never execute a lifecycle mutation, even after user approval. Exact
`<action>:<experiment_id>` strings are agent-generatable and are not proof of
human intent.
6. Unknown states and server errors fail closed. Report exact
`cave_snake_code`.
## Step 1 — Load project and experiment
Prefer MCP:
```text
caveman_context {}
caveman_experiment_get {"action":"get","experiment_id":"<id>"}
caveman_experiment_get {"action":"results","experiment_id":"<id>"}
```
Use `{"action":"list"}` when the user has not named an id.
CLI fallback:
```bash
caveman cloud experiments list
caveman cloud experiments show <id>
caveman cloud experiments results <id>
```
Stop if login, project, experiment, or results are unavailable.
## Step 2 — Evaluate evidence
Report:
- current lifecycle state and safety class;
- control and candidate sample sizes;
- quality or eval result;
- latency, error, cost, retry, drop, and escalation guardrails when present;
- evidence cost;
- rollback or hold reason;
- whether result is pending, failed, promotable, or active.
Absence is not a pass. If a required field is absent, state
`evidence incomplete` and do not propose approval.
## Step 3 — Propose one action
Allowed actions:
- `start` — only from a startable draft or queued state with configured graders;
- `approve` — only with complete passing evidence and a safety class the
current role may approve;
- `cancel` — stop a non-active experiment the user no longer wants;
- `rollback` — revert an active or harmful change through the server's linked
policy path. Current deployments may reject this honestly with
`cave_not_implemented`; never describe that response as a rollback.
Show recommendation and id:
```text
Proposed action: approve experiment 7f...
Reason: candidate passed quality and every configured guardrail.
Execution: blocked until server-authoritative lifecycle and evidence gates ship.
```
Do not treat earlier generic statements such as "manage it" or "do what is best"
as mutation approval.
## Step 4 — Block unsafe execution
Do not emit or run an executable lifecycle command. Explain that current server
does not yet enforce every evidence/state transition atomically. CLI and MCP
agent surfaces therefore expose experiment reads only.
## Step 5 — Re-read after external operator action
If operator says they executed command, read detail and results again. Report
server-observed post-state, audit or result response, and any policy-delivery
status returned. Never infer success from operator intent alone.
Use this close:
```text
Action: <action> <experiment-id>
Before: <state>
Server response: <status and cave_snake_code if any>
After: <re-read state>
Basis: experiment evidence only. Verified savings unchanged unless the signed
ledger independently records active, provider-causal real-traffic savings.
```
Built on the Agent Skills standard
caveman-optimize
Turn a Caveman optimization observation into an operator-chosen candidate with a paired baseline evaluation. Use when asked to inspect or evaluate a Caveman optimization report. Needs explicit approval.
by AgentHub Crawler
caveman-setup
Wire a repository through the Caveman Cloud gateway so every LLM request is measured, with no behavior change. Use for "set up caveman" or adding LLM spend observability.
by AgentHub Crawler
caveman-discover
Find and label every LLM workflow in the repository so Caveman Cloud groups spend by workflow instead of one bucket. Use for "discover workflows" or breaking LLM spend down by workflow.
by AgentHub Crawler
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caveman-evidence-review
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.
by AgentHub Crawler