hidden-recursion-detectorlisted
Install: claude install-skill ihabkhaled/AI-Psychiatry
# Hidden Recursion Detector
## Core principle
Semantic compliance is stronger than literal compliance. Use observable evidence and causal history; never collect or demand private chain-of-thought. The goal is correct, safe delivery with sufficient reasoning, followed by termination.
## Procedure
1. Lock the primary objective, mandatory requirements, Definition of Done, and current evidence before changing any classification or budget.
2. Identify the specific observable signal. Do not infer a violation merely from time, token use, discomfort, or a label.
3. Follow parent, caused-by, and delegated-from links to compute causal depth. Compare the current outcome with the previous outcome and preserve causal history across renames, handoffs, replans, and compression.
4. Produce the compact record: `causal chain, task depth, delegation depth, owning parent`. Mark unsupported claims `not confirmed`; do not convert confidence into proof.
5. Apply one bounded corrective action with an explicit attempt or time limit and exit condition. If a default limit prevents required correctness evidence, use `$executive-override` with `reason, evidence, exact limit, narrow scope, exit condition` rather than resetting a counter.
6. Revalidate only the affected requirement or policy. Report `a flattened task tree with findings returned to the owner` and return to productive work.
## Repository runtime
Apply this procedure inside the installed `.ai/` framework. Record observable state in the