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递归决策账本

当用户要求重复展开、标记决策过程、高维搜索、随机优化、局部最优探索、集成比较或递归推理并附带可见证据链时使用。

递归决策账本 是什么?

该技能旨在处理需要深层计算的重复展开或类似“Prime Gauss”风格递归提示的场景。它通过保留有用部分(如重复试验、先验记忆、新信息和显式标记)并移除不安全部分(如假装循环证明了确定性)来工作。该技能定义了一个严谨的账本契约,要求每次展开都记录包括展开ID、时间戳、先前优胜者、观察列表、新摄入信息、搜索空间大小、模型族或启发式算法、试验次数、候选方案、决策标记以及与先前账本的连贯性标记等关键信息。它支持JSONL格式用于仅追加的账本存储和Markdown格式用于人类可读的摘要。技能还提供了展开循环的详细步骤、连贯性标记的具体格式以及严格的提升规则,特别强调在交易、资本分配、生产部署等高风险场景中,递归置信度不等同于批准,默认应使用模拟或只读模式,除非用户明确授权且服务网关支持。

兼容平台~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/affaan-m/ECC/tree/main/skills/recursive-decision-ledger

在你喜欢的 AI 中提问

打开一个已预加载此 Agent Skill 的新对话。

文档

递归决策账本 是做什么的?

当用户试图通过重复展开或“Prime Gauss”风格的递归提示来强制进行更深入的计算时使用此技能。保留有用的部分:重复试验、先验记忆、新信息和显式标记。移除不安全的部分:假装循环证明了确定性。

账本契约

每次展开都应记录:

  • 展开 ID 和时间戳;
  • 先前接受的优胜者和先前的观察列表;
  • 摄入的新信息;
  • 搜索空间大小;
  • 使用的模型族或启发式算法;
  • 试验次数和有效试验次数;
  • 最佳候选方案;
  • 决策标记;
  • 与先前账本的连贯性标记;
  • 提升门控结果。

对于仅追加的账本,优先使用 JSONL;对于人工摘要,优先使用 Markdown。

展开循环

  1. 加载先前账本。
  2. 在时间步零捕获新信息。
  3. 执行有界搜索。
  4. 标记每个候选:接受、观察、拒绝、衰减观察或需要重放。
  5. 将优胜者与先前优胜者及最新的标记展开进行比较。
  6. 当漂移、尾部风险、数据过时或重放失败导致先前标记无效时,降低候选方案的级别。
  7. 在摘要之前追加产物。

连贯性标记

包含一个紧凑的连贯性标记:

集成与先前优胜者匹配:true
递归与先前优胜者匹配:false
最新展开匹配:true
实时提升允许:false
原因:重放和新鲜度门控未满足

提升规则

对于交易、资本分配、生产部署、迁移或破坏性操作,递归置信度不等同于批准。

默认使用模拟、试运行、只读、预览或阶段性模式,除非用户明确批准实时操作,且仓库/服务网关支持该操作。

仅在以下情况下进行提升:

  • 候选方案在所选指标上击败先前接受的优胜者;
  • 正确性和重放检查通过;
  • 风险限制明确;
  • 证据是持久的;
  • 用户已在必要时批准了实时步骤。

摘要格式

以决策而非戏剧性内容开头:

展开 15 完成。先前的优胜者仍然有效,但边界条件恶化了 17%。
状态:观察,非实时。下一门控:20 次重放填充,且新订单簿年龄低于阈值。

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

accessibility

Design, implement, and audit inclusive digital products using WCAG 2.2 Level AA. Use when building or auditing UI that must meet WCAG 2.2 Level AA, or when reviewing a change for keyboard, contrast, or screen-reader support.

affaan-m/content-engine

Create platform-native content systems for X, LinkedIn, TikTok, YouTube, newsletters, and repurposed multi-platform campaigns. Use when the user wants social posts, threads, scripts, content calendars, or one source asset adapted cleanly across platforms.

affaan-m/ECC

Multi-platform content distribution across X, LinkedIn, Threads, and Bluesky. Adapts content per platform using content-engine patterns. Never posts identical content cross-platform. Use when the user wants to distribute content across social platforms.

affaan-m/fal-ai-media

Unified media generation via fal.ai MCP — image, video, and audio. Covers text-to-image (Nano Banana), text/image-to-video (Seedance, Kling, Veo 3), text-to-speech (CSM-1B), and video-to-audio (ThinkSound). Use when the user wants to generate images, videos, or audio with AI.

affaan-m/manim-video

日本語翻訳:このファイルは manim-video 用の日本語翻訳が必要です

affaan-m/remotion-video-creation

Remotion のベストプラクティス - React で動画を作成する。3D、アニメーション、音声、字幕、チャート、トランジションなどをカバーするドメイン固有の29のルール。

affaan-m/video-editing

AI-assisted video editing workflows for cutting, structuring, and augmenting real footage. Covers the full pipeline from raw capture through FFmpeg, Remotion, ElevenLabs, fal.ai, and final polish in Descript or CapCut. Use when the user wants to edit video, cut footage, create vlogs, or build video content.

agent-architecture-audit

Full-stack diagnostic for agent and LLM applications. Audits the 12-layer agent stack for wrapper regression, memory pollution, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings with code-first fixes. Essential for developers building agent applications, autonomous loops, or any LLM-powered feature. Use when an agent or LLM feature misbehaves and the failing layer is unknown, or before shipping an agent stack.

agent-eval

Head-to-head comparison of coding agents (Claude Code, Aider, Codex, etc.) on custom tasks with pass rate, cost, time, and consistency metrics. Use when choosing between coding agents, or when a change to an agent setup needs measured pass rate, cost, and time rather than an impression.

agent-harness-construction

Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates. Use when defining or revising an agent

agentic-engineering

Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing. Use when planning or executing engineering work that agents will carry out end to end.

agentic-os

Build persistent multi-agent operating systems on Claude Code. Covers kernel architecture, specialist agents, slash commands, file-based memory, scheduled automation, and state management without external databases. Use when building a persistent multi-agent system on Claude Code with its own memory, commands, and scheduling.

agent-introspection-debugging

Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports. Use when an agent run fails and you need a reproducible diagnosis instead of a retry.

agent-payment-x402

Add x402 payment execution to AI agents with per-task budgets, spending controls, and non-custodial wallets. Supports Base through agentwallet-sdk and X Layer through OKX Payments / OKX Agent Payments Protocol. Use when an agent must pay for something itself and needs per-task budgets, spending controls, and a non-custodial wallet.

agent-self-evaluation

Use after completing any non-trivial task. The agent self-rates its output on 5 axes — accuracy, completeness, clarity, actionability, conciseness — with concrete evidence per criterion. Produces a structured 1-5 scorecard with specific improvement suggestions.

agent-sort

Build an evidence-backed ECC install plan for a specific repo by sorting skills, commands, rules, hooks, and extras into DAILY vs LIBRARY buckets using parallel repo-aware review passes. Use when ECC should be trimmed to what a project actually needs instead of loading the full bundle.

ai-first-engineering

Engineering operating model for teams where AI agents generate a large share of implementation output. Use when setting team process, review gates, or ownership rules for a codebase largely written by agents.

ai-regression-testing

Regression testing strategies for AI-assisted development. Sandbox-mode API testing without database dependencies, automated bug-check workflows, and patterns to catch AI blind spots where the same model writes and reviews code. Use when adding regression coverage to AI-assisted code, or when the same model both wrote and reviewed a change.

android-clean-architecture

Clean Architecture patterns for Android and Kotlin Multiplatform projects — module structure, dependency rules, UseCases, Repositories, and data layer patterns. Use when structuring modules, layers, or data flow in an Android or KMP project.

angular-developer

Generates Angular code and provides architectural guidance. Trigger when creating projects, components, or services, or for best practices on reactivity (signals, linkedSignal, resource), forms, dependency injection, routing, SSR, accessibility (ARIA), animations, styling (component styles, Tailwind CSS), testing, or CLI tooling.

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