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recursive-decision-ledger

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.

DeepseekModel Curated skill Quality Excellent · 90 v1.0.0

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Download .skill Standard format with system_prompt and model_config, ready for any agent framework
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name recursive-decision-ledger description 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. license MIT metadata {"origin":"ECC"} tools Read, Write, Edit, Bash, Grep, Glob 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 Load the prior ledger. Capture new information at time-step zero. Run the bounded search. Mark each candidate: accept, watch, reject, decay watch, or needs replay. Compare winners against prior winners and latest marked rollout. Downgrade candidates when drift, tail risk, stale data, or failed replay invalidates the previous mark. Append artifacts before summarizing. Coherence Mark 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 Promotion Rules 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: the candidate beats the prior accepted winner on the chosen metric; correctness and replay checks pass; risk limits are explicit; the evidence is durable; the user has approved the live step when needed. Summary Shape 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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formatFormat tag (skill/v1)
skill_idUnique skill ID
nameSkill name
versionVersion
descriptionDescription
categoryCategories (array)
trigger_wordsTrigger words
tagsTags
sourceSource
source_urlSource URL (this page)
exported_atExported at (set per download)
system_promptSystem prompt body
model_configModel config: provider / model / temperature / max_tokens / top_p
examplesExamples
install_guideImport guide for Coze / Dify / Claude / custom frameworks
The same skill can be exported in different platform formats.
.skill Standard format with system_prompt and model_config, ready for any agent framework Download
.skillpro Enhanced format with scripts, tools, dependencies and hooks Download
.json Plain JSON export with system_prompt and model parameters only Download
Coze Markdown with frontmatter, for Coze platform import Download
Dify Dify DSL, import directly after creating an app Download

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