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預測市場風險審查

在預測市場、籃子交易、預言機及交易代理工作流程中,審查其合規性、安全性、資料品質、隱私與執行風險。請在處理任何涉及場所認證、使用者投資組合資料、API 金鑰或交易規劃的工作流程前使用此技能。

預測市場風險審查 是什麼?

此技能專門用於審查與預測市場、籃子交易、預言機及交易代理相關的工作流程,確保其在合規、安全、資料品質、隱私及執行風險方面符合標準。它特別適用在任何涉及敏感使用者財務資料、場所認證、API 金鑰或自動化交易規劃的步驟之前使用。此技能支援多種平台與工具,並提供明確的審查關卡,包括建議邊界、場所與監理邊界、資料品質、安全性與隱私等面向。最終會產生包含審查範圍、通過/警告/失敗結果、阻擋的動作、必要的緩解措施以及安全下一步的輸出合約,確保任何具執行能力的步驟都需經過獨立實作計畫與明確的使用者核准,有效降低自動化交易流程中的潛在風險。

相容平台~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/affaan-m/ECC/tree/main/skills/prediction-market-risk-review

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

預測市場風險審查 是做什麼的?

請在預測市場工作流程接觸到使用者財務背景、場所認證、投資組合資料、自動化或具執行能力的工具之前,使用此技能。

審查關卡

建議邊界

  • 確認輸出為資訊性內容。
  • 移除買入/賣出/持有/規模建議。
  • 明確標示需使用者手動決策的環節。

場所與監理邊界

  • 辨識場所條款、地理限制、帳戶限制及 API 規則。
  • 在相關情況下,標記涉及賭博、衍生性金融商品、證券或商品的模糊地帶,以供法律審查。
  • 不得繞過場所限制或速率限制。

資料品質

  • 檢查市場流動性、價差、結算規則、過時價格及資料來源時間戳。
  • 區分公開場所資料與 Itô 閘門資料。
  • 不得在未標示的情況下混合公開與私有資料來源。

安全性

  • 不得請求或儲存私鑰、助記詞或密碼。
  • 將 ITO_API_KEY 與場所 API 金鑰保留在記錄與文件之外。
  • 預設使用唯讀範圍。
  • 在私人實作新增任何執行能力之前,要求設置斷路器、支出限制、乾執行及人為核准。

隱私

  • 最小化使用者投資組合、財務及知識庫資料的使用。
  • 在公開成品中遮罩私有來源。
  • 僅保留審查所需欄位。

輸出合約

回傳:

  1. 審查範圍
  2. 通過/警告/失敗結果
  3. 阻擋的動作
  4. 必要的緩解措施
  5. 安全下一步

若請求任何具執行能力的步驟,需要求一份獨立的實作計畫與明確的使用者核准。

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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