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遞迴決策分類帳

當使用者要求重複展開、標記決策過程、高維度搜索、隨機最佳化、局部最佳點探索、集成比較或帶有可見證據軌跡的遞迴推理時使用此技能。

遞迴決策分類帳 是什麼?

此技能提供一個結構化框架,用於管理反覆計算展開(即「遞迴提示」或「Prime Gauss」風格),特別適用於交易、資本配置、生產部署與高維度搜索等領域。它強制記錄每次展開的詳細資訊,包括展開 ID、時間戳記、先前獲勝者、觀察清單、新資訊、搜索空間大小、模型系列、試驗次數、候選方案及決策標記。該技能透過「連貫性標記」來防止虛假確定性,該標記會將集成結果、遞迴結果與最新展開進行比較,並根據風險條件決定是否允許即時推廣。推廣僅在候選方案超越先前獲勝者、正確性與重播檢查通過、風險限制明確、證據持久且使用者明確批准即時操作時才會進行。該技能支援 JSONL(供附加專用分類帳使用)與 Markdown(供人類摘要使用)兩種格式,且廣泛適用於 Claude Code、Cursor 與 Codex 等 AI 代理平台。

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

在你喜歡的 AI 中提問

開啟一個已預先載入此 Agent Skill 的新對話。

說明文件

遞迴決策分類帳 是做什麼的?

當使用者嘗試透過重複展開或「Prime Gauss」風格的遞迴提示來強制進行更深層的計算時,請使用此技能。保留有用的部分:重複試驗、先前記憶、新鮮資訊與明確標記。移除不安全的部份:假裝迴圈能證明確定性。

分類帳合約

每次展開都應記錄:

  • 展開 ID 與時間戳記;
  • 先前接受的獲勝者與先前的觀察清單;
  • 所攝取的新資訊;
  • 搜索空間大小;
  • 使用的模型系列或啟發式方法;
  • 試驗次數與有效試驗次數;
  • 頂尖候選方案;
  • 決策標記;
  • 與先前分類帳的連貫性標記;
  • 推廣閘門結果。

對於附加專用分類帳,偏好使用 JSONL;對於人類摘要,則使用 Markdown。

展開迴圈

  1. 載入先前的分類帳。
  2. 在時間步驟零捕獲新資訊。
  3. 執行有界搜索。
  4. 標記每個候選方案:接受、觀察、拒絕、觀察衰減或需要重播。
  5. 將獲勝者與先前獲勝者及最新的標記展開進行比較。
  6. 當偏移、尾端風險、過時資料或重播失敗使先前的標記失效時,降級候選方案。
  7. 在總結之前附加產出物。

連貫性標記

包含一個簡潔的連貫性標記:

集成匹配先前獲勝者:是
遞迴匹配先前獲勝者:否
最新展開匹配:是
即時推廣允許:否
原因:重播與新鮮度閘門未滿足

推廣規則

對於交易、資本配置、生產部署、遷移或破壞性操作,遞迴可信度並非核准依據。

預設使用紙上、試運行、唯讀、預覽或暫存模式,除非使用者明確核准即時操作且倉庫/服務閘門支援。

僅在以下條件滿足時才進行推廣:

  • 候選方案在選定的指標上勝過先前接受的獲勝者;
  • 正確性與重播檢查通過;
  • 風險限制明確;
  • 證據持久;
  • 必要時使用者已核准即時步驟。

摘要格式

以決策為開頭,而非戲劇性:

展開 15 完成。先前的獲勝者仍然維持,但邊際惡化了 17%。
狀態:觀察,非即時。下一個閘門:20 次重播填補,且新訂單簿年齡低於閾值。

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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