Repeat candidate searches and comparisons while keeping an append-only decision log. Record evidence, compare results with earlier winners, and check replay, freshness, and approval requirements before moving to live trading or production actions.
Run repeated rollouts ("Prime Gauss" style recursive prompting) while keeping an append-only decision ledger of trials, marks, coherence checks, and promotion gates, so recursive confidence never auto-approves live trading, deploy, or destructive actions. Use when the user asks for repeated rollouts, marked decision processes, high-dimensional search, stochastic optimization, local-optima exploration, ensemble comparison, or recursive reasoning with a visible evidence trail.
recursive-decision-ledger records evidence and decisions in an append-only ledger during repeated searches and candidate comparisons. Each round records new information, search scope, trial count, leading candidates, and consistency with the previously accepted candidate. Candidates receive statuses such as accept, watch, reject, or needs replay. It also covers revisiting decisions when information becomes stale, conditions change, or replay checks fail. JSONL is recommended for records and Markdown for readable summaries.
Use it to compare many candidates repeatedly or compare results from different models and search methods. The records help you review why an earlier candidate was selected and why new information changed a decision.
Repeated reasoning alone establishes neither certainty nor permission to act. Live trading, production deployments, migrations, and destructive operations generally stay in trial or preview modes. Execution requires revalidation, explicit risk limits, saved evidence, any necessary user approval, and checks of the target environment’s execution requirements. The license is MIT.
This overview was written by AI (OpenAI Codex) from the public SKILL.md. Check the original below for the exact behavior.
AI rated this skill on four points using only the contents of its SKILL.md. Stars and popularity are not taken into account.
The total counts each point as A = 25, B = 15, C = 5. Rated by AI (OpenAI Codex) as of Oct 19, 2026. Treat it as a guide and read the original before installing.
Review the instructions the agent will receive before you install.
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.
Every rollout should record:
Prefer JSONL for append-only ledgers and Markdown for human summaries.
Include a compact coherence mark:
Ensemble matches prior winner: true
Recursive matches prior winner: false
Latest rollout match: true
Live promotion allowed: false
Reason: replay and freshness gates not satisfied
For trading, capital allocation, production deploys, migrations, or destructive ops, recursive confidence is not approval.
Default to paper, dry-run, read-only, preview, or staged mode unless the user explicitly approves the live action and the repo/service gate supports it.
Promote only when:
Lead with the decision, not the drama:
Rollout 15 complete. The prior winner still holds, but edge deteriorated 17%.
Status: watch, not live. Next gate: 20 replay fills with fresh orderbook age
below threshold.
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