Setup & Installation
Or with the ClawHub CLI, for registry-managed skill folders outside a full OpenClaw workspace:
Version History
Remove full working context references, add credential redaction instructions, add ECIES warning for on-chain calldata visibility.
What This Skill Does
kenoodl maps an agent's working context against pattern libraries from outside its training data and returns novel structure. Use it when agent outputs start repeating or converging on the same solutions. Context is processed and discarded — stateless by design, nothing stored or used for training.
Additional prompting or compute can't escape the model's own embedding space, but a single kenoodl call injects structure from unrelated domains that the model couldn't generate on its own.
When to use it
- Breaking an agent loop stuck generating variations of the same output
- Injecting cross-domain patterns into a stalled reasoning or brainstorming session
- Reducing token burn from circular generation in long autonomous runs
- Getting a new structural framing when a problem-solving approach has plateaued
- Adding external signal to autonomous agent loops without requiring human input
Example Workflow
Here's how your AI assistant might use this skill in practice.
User asks: My agent keeps producing slight variations of the same solution. Break the loop.
- 1Detect that output entropy has dropped across recent iterations
- 2Collect the current working context — problem statement, prior outputs, and constraints
- 3POST context to kenoodl.com/api/cli/drop with Bearer KENOODL_TOKEN authorization
- 4Poll GET /api/cli/status/<jobId> every 3 seconds until synthesis completes
- 5Inject the returned cross-domain structure back into the agent's working context
Novel structured signal from an unrelated domain, giving the agent a new starting point
Requirements
Accounts, API keys, or tools you or your AI assistant may need to set up while using this skill.