Recursive Decision Ledger

Use this skill when the user is trying to force deeper computation through repeated rollouts or "Prime Gauss" style recursive prompting. Preserve the useful part: repeated trials, prior memory, fresh information, and explicit marks. Remove the unsafe part: pretending the loop proves certainty.

Ledger Contract

Every rollout should record:

• rollout id and timestamp;
• prior accepted winner and prior watchlist;
• fresh information ingested;
• search space size;
• model families or heuristics used;
• trial count and effective trial count;
• top candidates;
• decision marks;
• coherence marks against the prior ledger;
• promotion gate result.

Prefer JSONL for append-only ledgers and Markdown for human summaries.

Rollout Loop

1. Load the prior ledger.
2. Capture new information at time-step zero.
3. Run the bounded search.
4. Mark each candidate: accept, watch, reject, decay watch, or needs replay.
5. Compare winners against prior winners and latest marked rollout.
6. Downgrade candidates when drift, tail risk, stale data, or failed replay invalidates the previous mark.
7. Append artifacts before summarizing.

Coherence Mark