From ecc
Manages recursive decision processes with repeated rollouts, evidence trails, and promotion gates. Useful for high-dimensional search, stochastic optimization, and ensemble comparison.
How this skill is triggered — by the user, by Claude, or both
Slash command
/ecc:recursive-decision-ledgerThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Use this skill when the user is trying to force deeper computation through
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.
npx claudepluginhub danielnguyenfinhub/ecc140plugins reuse this skill
First indexed Jun 3, 2026
Showing the 6 earliest of 140 plugins
Manages recursive decision processes with repeated rollouts, evidence trails, and promotion gates. Useful for high-dimensional search, stochastic optimization, and ensemble comparison.
Manages recursive decision rollouts with consistency markers and gating. Defaults to safe modes unless explicitly approved. Supports high-dimensional search and ensemble comparisons.
Applies Karpathy's autoresearch loop—goal, mechanical fitness, mutable surface, keep-or-revert iteration—to measurable workflows like code, content, sales, research. Triages unsuitable fat-tailed or slow-feedback problems.