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juliusbrussee/caveman-manage

juliusbrussee

caveman-manage

Inspect Caveman Cloud's eval-gated experiment lifecycle and block unsafe execution. Use when the user asks to start, approve, cancel, promote, or roll back a Caveman experiment, or asks what action an experiment's evidence supports. Read evidence first; do not execute lifecycle mutations until server-authoritative transition and evidence gates ship.

v1.0Latest
New~1.0kUpdated Aug 17, 2026

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:

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 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: <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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Overall Score

87/100

Grade

A

Excellent

Safety

92

Quality

85

Clarity

87

Completeness

82

Summary

This skill guides an AI agent to inspect and manage Caveman Cloud's experiment lifecycle by reading current state and evidence, then proposing safe actions without executing mutations. The skill enforces strict guardrails: reads are always authorized, but lifecycle mutations (start, approve, cancel, rollback) are never executed by the agent—only recommended after evidence validation.

Detected Capabilities

read-only API calls via MCPCLI read commands (caveman cloud experiments list/show/results)state inspection and evidence evaluationhuman-in-the-loop decision support

Trigger Keywords

Phrases that MCP clients use to match this skill to user intent.

manage caveman experimentapprove experimentinspect experiment evidencereview experiment resultscheck experiment guardrailscaveman lifecycleexperiment rollback

Risk Signals

INFO

Agent is explicitly forbidden from executing lifecycle mutations, even with user approval

Non-negotiable gates, item 5
INFO

Requires server-authoritative enforcement before mutations are safe

Step 1 introductory paragraph
INFO

Uses read-only MCP by design; control API mutations deferred to human

Step 1, CLI fallback section

Use Cases

  • Inspect experiment status and evidence before human approval
  • Determine whether experiment results meet guardrail requirements
  • Recommend approval, cancellation, or rollback based on observed state
  • Block unsafe mutations and defer control to human operators
  • Validate evidence completeness before lifecycle transitions

Quality Notes

  • Clear security boundaries: reads are always authorized; mutations are permanently blocked at the agent level
  • Non-negotiable gates are explicit and enforceable (6 numbered constraints)
  • Evidence evaluation criteria well-defined: sample sizes, guardrails, result status, cost
  • Error handling specified: unknown states and server errors 'fail closed' with cave_snake_code reporting
  • Post-action verification workflow documented in Step 5
  • Uses structured MCP and CLI examples for clarity
  • Appropriately scoped: focuses on a specific production control task (experiment lifecycle gating)
  • Strong emphasis on human-in-the-loop decision making and attestation
  • Limitations clearly stated: current server does not yet enforce atomicity; agent surface is read-only
Model: claude-haiku-4-5-20251001Analyzed: Aug 17, 2026

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