Setup & Installation
Or with the ClawHub CLI, for registry-managed skill folders outside a full OpenClaw workspace:
What This Skill Does
Addresses a silent failure mode in agentic AI where an LLM keeps pushing a broken approach past the point of reason. When an agent is stuck, looping, or failing repeatedly, it forces a structured stop: name the failed assumption, generate three genuinely different alternatives using inversion, analogy, and brute-force simplicity, then probe each for signal. Loops through a design-thinking cycle until a viable path is found or the problem is escalated with a clear failure diagnosis.
Most agents default to cosmetic retries or parameter tweaks around a broken core assumption; this skill forces genuine strategic divergence by requiring each alternative to violate at least one assumption of the previous approach.
When to use it
- Agent retrying the same broken API call three times in a row
- Debugging session where every attempted fix hits the same compiler error
- Code generation task looping on the same logic structure without progress
- Automation workflow timing out at the same step across multiple runs
- Data extraction script returning wrong results despite incremental parameter tweaks
Example Workflow
Here's how your AI assistant might use this skill in practice.
User asks: fix this script that keeps failing to parse the API response
- 1Recognize the trigger: same approach attempted 3 times with no material improvement
- 2Write a dead end declaration naming the failed assumption and what it revealed
- 3Generate 3 alternatives: Inversion (parse locally instead of calling the API), Analogy (treat the response like a stream, not a document, borrowing from log-parsing patterns), Reduction (hardcode a known-good response shape and verify just the extraction logic)
- 4Probe each alternative for signal without needing to fully complete each one
- 5Run a third-party audit on the strongest result, checking for scope drift and edge cases before declaring done
Working parser using the local-computation approach, with audit confirming it handles the actual response structure