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Agent Skill

Caveman Manage

caveman-manage

Inspect Caveman Cloud's experiment lifecycle and block unsafe execution. Use when asked to start, approve, cancel, promote or roll back a Caveman experiment.

JuliusbrusseeDevOpsGoAiAnthropicCavemanClaudeClaude-codeLlmMemePrompt-engineeringSkillTokens

59K installs

juliusbrussee/caveman

by Juliusbrussee

Score

8.5

/ 10

Installs

59K

Repo Stars

106.8K

Last Updated

0d ago

Fresh

Quality Ratio

86%

Description

Verified

Language

Go

First Published

Aug 2026

Summary

The Caveman Manage agent skill safely inspects and proposes actions for Caveman Cloud experiment lifecycles, critically blocking any unsafe or unauthorized execution of changes. This agent skill is essential for DevOps professionals and engineers working with Caveman Cloud experiments who need to ensure controlled, evidence-based management of their deployments. This is a skill with 12K installs, indicating its broad utility within the Caveman ecosystem. It strictly adheres to non-negotiable gates, preventing approval of experiments with pending results or absent guardrails, and instructs the agent to read current state and results using `caveman_experiment_get` commands before proposing actions. Crucially, it blocks the agent from executing any lifecycle mutations directly, explaining that the server does not yet atomically enforce all transitions, thus ensuring operations remain read-only for safety. A key limitation is that it intentionally keeps the agent's control actions read-only, preventing direct execution of lifecycle mutations even after user approval.

Skill Definition

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.

How to Use

Use in O-mega

Claude Code

npx skills add juliusbrussee/caveman caveman-manage