Caveman Manage
DevOps & deploy117.8K installs
What it does
Inspect Caveman Cloud's experiment lifecycle and block unsafe execution. Use when asked to start, approve, cancel, promote or roll back a Caveman experiment.
Install
npx skills add https://github.com/juliusbrussee/caveman --skill caveman-manageWebsite
www.skills.sh/juliusbrussee/caveman/caveman-manageDetails
From the skill’s SKILL.md or README, by its authors. View the original
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
- A request to review, inspect, explain, or recommend authorizes reads only.
- Never approve an experiment whose results are pending, whose required
guardrails are absent, or whose evidence reports a breach.
- Never convert experiment lift into
verified_savings. Only active real
traffic plus provider-causal, provider-complete ledger evidence can do that.
- Never supply an organization id. Project and tenant scope come from the
logged-in Caveman identity and server RBAC.
- Never execute a lifecycle mutation, even after user approval. Exact
: strings are agent-generatable and are not proof of human intent.
- Unknown states and server errors fail closed. Report exact
cave_snake_code.
Step 1 — Load project and experiment
Prefer MCP:
caveman_context {}
caveman_experiment_get {"action":"get","experiment_id":""}
caveman_experiment_get {"action":"results","experiment_id":""}
Use {"action":"list"} when the user has not named an id.
CLI fallback:
caveman cloud experiments list
caveman cloud experiments show
caveman cloud experiments results
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:
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:
Action:
Before:
Server response:
After:
Basis: experiment evidence only. Verified savings unchanged unless the signed
ledger independently records active, provider-causal real-traffic savings.More DevOps & deploy skills
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