caveman-manage
juliusbrussee/caveman
Inspect and recommend on Caveman Cloud experiment lifecycle changes with safety gates.
What is caveman-manage?
Manages eval-gated experiments in Caveman Cloud by reading experiment state, results, and evidence, then proposing safe lifecycle actions (start, approve, cancel, rollback). Use when asked to review, approve, or control an experiment's progression through its lifecycle.
- Read experiment state, results, and guardrail status via MCP or CLI
- Evaluate evidence completeness and safety before recommending actions
- Propose one supported lifecycle action (start, approve, cancel, rollback) with reasoning
- Block unsafe execution and explain server-side enforcement gaps
- Re-read and report post-action state when operator confirms execution
How to install caveman-manage
npx skills add https://github.com/juliusbrussee/caveman --skill caveman-manage- Caveman Cloud account with logged-in identity and project scope
- Experiment already created in Caveman Cloud (skill reads only, does not create)
- MCP server or CLI access to caveman cloud experiments commands
How to use caveman-manage
- 1.Call caveman_context {} to load project and identity
- 2.Call caveman_experiment_get with action 'get' or 'list' to retrieve experiment details
- 3.Call caveman_experiment_get with action 'results' to fetch evidence and guardrail status
- 4.Evaluate the evidence report against non-negotiable gates (pending results, missing guardrails, breaches)
- 5.Propose one action (start, approve, cancel, rollback) with reasoning and experiment id
- 6.If operator confirms execution, re-read experiment state and report server-observed post-state
Use cases
- Review experiment results and recommend approval or rollback before production impact
- Inspect guardrail status (latency, error, cost, retry, drop, escalation) to catch safety breaches
- Cancel non-active experiments that no longer meet business goals
- Audit experiment lifecycle transitions and verify evidence gates were enforced
- Coordinate experiment promotion from draft through active real-traffic states
- Experiment operators managing Caveman Cloud deployments
- Engineering leads reviewing candidate changes before approval
- Platform teams enforcing evidence and safety gates on experiments
- DevOps engineers auditing experiment state and rollback policies
caveman-manage FAQ
No. The skill blocks execution and explains that the server does not yet enforce every evidence/state transition atomically. Only reads are exposed.
A required field (guardrail, result, or safety class) is absent. Do not propose approval until all required evidence is present and passes.
Only when results are complete (not pending), all configured guardrails pass, no breaches are reported, and the safety class matches the current role's authority.
Cancel stops a non-active experiment the user no longer wants. Rollback reverts an active or harmful change through the server's linked policy path.
Re-read the experiment state after the operator confirms execution. Report the server-observed post-state, any audit response, and policy-delivery status.
Full instructions (SKILL.md)
Source of truth, from juliusbrussee/caveman.
name: caveman-manage description: > Inspect Caveman Cloud's experiment lifecycle and block unsafe execution. Use when asked to start, approve, cancel, promote or roll back a Caveman experiment.
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
<action>:<experiment_id>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":"<id>"}
caveman_experiment_get {"action":"results","experiment_id":"<id>"}
Use {"action":"list"} when the user has not named an id.
CLI fallback:
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 withcave_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: <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.
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