Manage the CogOps skill lifecycle (Evolution layer). Actions: - list: Show all skills with status, fitness, and run counts - benchmark: Run test cases and compute fitness score (0.0-1.0) - promote: Promote (fitness >= 0.7) or prune (fitness <= 0.3) Args: action: list | benchmark | promote skill_i...
AI agents invoke manage_skills to trigger actions in Entroly Context Engine. What it does depends on the arguments the agent supplies, and its effects often reach beyond the immediate call — builds kicked off, notifications sent, workflows started.
The 'benchmark' action runs test cases (code execution), and 'promote/prune' actions modify or remove skill objects in the system lifecycle. Since running benchmarks involves executing code/tests and promoting/pruning changes system state, this spans Execute and Write categories. Execute is the most severe applicable category here, as the benchmark action runs test cases.
From the tool's definition benchmark: Run test cases and compute fitness score; promote: Promote (fitness >= 0.7) or prune (fitness <= 0.3) — triggers execution of test cases and lifecycle state changes on skills
Documented attack patterns abuse exactly the kind of access manage_skills gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Entroly Context Engine, and nothing reaches the server without passing your rules. This is the rule we recommend for manage_skills:
{
"version": "1",
"default": "deny",
"tools": {
"manage_skills": {
"limits": [
{
"counter": "manage_skills_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
}manage_skills stays usable, but rate-capped — a runaway agent can't fire it dozens of times a minute. Everything else on the server is denied unless you say otherwise.
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Manage the CogOps skill lifecycle (Evolution layer). Actions: - list: Show all skills with status, fitness, and run counts - benchmark: Run test cases and compute fitness score (0.0-1.0) - promote: Promote (fitness >= 0.7) or prune (fitness <= 0.3) Args: action: list | benchmark | promote skill_id: Required for benchmark/promote actions. It is categorised as a Execute tool in the Entroly Context Engine MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Entroly Context Engine MCP server in PolicyLayer and add a rule for manage_skills: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches Entroly Context Engine. Nothing to install.
manage_skills is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.
Yes. Add a rate_limit block to the manage_skills rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.
Set action: deny in the PolicyLayer policy for manage_skills. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.
manage_skills is provided by the Entroly Context Engine MCP server (juyterman1000/entroly). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from Entroly Context Engine, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
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