recursive-decision-ledger
affaan-m/ecc
Recursive decision-making with explicit evidence trails and promotion gates for high-dimensional search and stochastic optimization.
What is recursive-decision-ledger?
A framework for repeated rollouts and marked decision processes that preserve evidence, prior memory, and fresh information while avoiding false certainty. Use this when exploring multiple candidates, optimizing across high-dimensional spaces, or making staged decisions with visible reasoning trails.
- Record every rollout with timestamps, search space metadata, and decision marks (accept, watch, reject, decay, replay)
- Compare candidates against prior winners and latest rollouts with coherence checks
- Enforce promotion gates: require metric improvement, replay validation, and explicit user approval before live actions
- Maintain append-only JSONL ledgers and human-readable Markdown summaries of decision history
- Downgrade candidates when drift, tail risk, stale data, or failed replays invalidate prior marks
- Default to paper, dry-run, or staged modes for trading, deploys, and destructive operations
How to install recursive-decision-ledger
npx skills add null --skill recursive-decision-ledgerHow to use recursive-decision-ledger
- 1.Load or initialize the prior decision ledger (JSONL or Markdown)
- 2.Capture fresh information and set time-step zero for the new rollout
- 3.Run your bounded search (trials, ensemble, optimization loop) and collect candidates
- 4.Mark each candidate with a decision status: accept, watch, reject, decay watch, or needs replay
- 5.Compare top candidates against the prior accepted winner and latest marked rollout
- 6.Generate a coherence mark showing ensemble/recursive match, freshness, and promotion eligibility
- 7.Append the rollout record to the ledger before summarizing
- 8.Summarize the decision: state the winner, whether it beats the prior, and the next gate (e.g., replay fills, freshness threshold)
Use cases
- Stochastic optimization: run multiple trials, track winners, and promote only when gates pass
- Trading or capital allocation: maintain a decision ledger showing prior winners, replay validation, and coherence marks before live execution
- Production deploys or migrations: stage candidates through paper/preview modes with explicit evidence before promoting to live
- Ensemble model comparison: record each model family's performance, coherence against priors, and promotion eligibility
- High-dimensional search: explore local optima, mark candidates by freshness and correctness, and avoid premature promotion
- Data scientists and ML engineers optimizing across multiple trials or ensemble methods
- Trading and quantitative finance teams requiring audit trails and staged promotion
- DevOps and SRE teams managing staged deployments with explicit gates and rollback criteria
- Researchers exploring high-dimensional or stochastic search spaces with reproducible evidence
recursive-decision-ledger FAQ
Use this skill when you need repeated trials, want to track prior winners, must validate candidates against fresh data, or are making staged decisions (paper → dry-run → live). Single-shot decisions don't need the ledger overhead.
A coherence mark is a compact summary showing whether your ensemble and recursive results match the prior winner, whether the latest rollout matches, and whether promotion gates are satisfied. It prevents false confidence by explicitly stating when replay or freshness checks fail.
No. Default to paper, dry-run, read-only, or staged mode. Promote to live only when the candidate beats the prior winner on your metric, replay and correctness checks pass, risk limits are explicit, and the user has explicitly approved the live step.
Record rollout ID, timestamp, prior winner, fresh information ingested, search space size, model families used, trial count, top candidates, decision marks, coherence marks, and promotion gate result. Use JSONL for append-only ledgers and Markdown for human summaries.
Recursive confidence is not approval. Always enforce promotion gates: require metric improvement over the prior winner, validate replay on fresh data, set explicit risk limits, and require user approval for live actions. Downgrade candidates when drift, tail risk, or stale data invalidates prior marks.
Full instructions (SKILL.md)
Source of truth, from affaan-m/ecc.
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. 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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