recursive-decision-ledger

recursive-decision-ledger is a skill for Claude Code, Codex from JunMystery/Agent-Guidance-Python. It costs 44 tokens per session (532 once invoked), scanned A, a copy of recursive-decision-ledger, MIT.

A ledger and workflow for comparing repeated trials, candidates, and decisions in complex searches or optimization tasks. It keeps an evidence trail so changes and uncertainty remain visible instead of treating repetition as proof.

In plain words
What is it for?
Use it for repeated rollouts, high-dimensional searches, stochastic optimization, local-optima exploration, ensemble comparisons, or recursive reasoning with recorded evidence.
Why use it?
It helps track why a candidate was accepted, watched, or rejected, and exposes when an earlier decision may no longer hold because data or conditions changed.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it for repeated rollouts, high-dimensional searches, stochastic optimization, local-optima exploration, ensemble comparisons, or recursive reasoning with recorded evidence.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/junmystery/agent-guidance-python/recursive-decision-ledger
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add JunMystery/Agent-Guidance-Python --skill recursive-decision-ledger
Clone the repo
git clone --depth 1 https://github.com/JunMystery/Agent-Guidance-Python

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for recursive-decision-ledger

README.md
[![agentmods](https://agentmods.dev/badge/skills/junmystery/agent-guidance-python/recursive-decision-ledger/github.svg)](https://agentmods.dev/skills/junmystery/agent-guidance-python/recursive-decision-ledger)
Your own site
<a href="https://agentmods.dev/skills/junmystery/agent-guidance-python/recursive-decision-ledger"><img src="https://agentmods.dev/badge/skills/junmystery/agent-guidance-python/recursive-decision-ledger/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for recursive-decision-ledger

Your own site · 80×15
<a href="https://agentmods.dev/skills/junmystery/agent-guidance-python/recursive-decision-ledger"><img src="https://agentmods.dev/badge/skills/junmystery/agent-guidance-python/recursive-decision-ledger.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 44 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 532 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin 100% copy Near-identical to another mod in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00044 $0.00532
Opus 5.5 $0.00018 $0.00213
Sonnet 5.5 $0.00009 $0.00106
Haiku 4.5 $0.00004 $0.00053

Measured 2mo ago against content hash 33fa679cf499, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

recursive-decision-ledger scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

This is a copy

100% identical to recursive-decision-ledger — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

skills/recursive-decision-ledger/SKILL.md · 80 lines

How it starts

The opening of the file, as written. The whole thing — 80 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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

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:

Read the full file on GitHub · 80 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2mo ago First seen · 80 lines · 44 tokens per session scan A 33fa679cf499

Subscribe to this mod's changes

recursive-decision-ledger is a skill published in the GitHub repository JunMystery/Agent-Guidance-Python (2 stars, last pushed 3mo ago), licensed MIT. It adds 44 tokens to every session and 532 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to recursive-decision-ledger, differing in 0 lines, and is treated as a copy.

Related

recursive-decision-ledger compared with skills from other repositories

recursive-decision-ledger next to 6 skills from other repositories
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recursive-decision-ledgerJunMystery/Agent-Guidance-PythonSkill23mo agoMITA44Claude Code, Codex
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