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预测市场风险审查

审查预测市场、篮子、预言机和交易代理工作流,确保其合规性、安全性、数据质量、隐私和风险执行。在涉及场所认证、用户投资组合数据、API密钥或交易规划之前使用。

预测市场风险审查 是什么?

该技能用于在预测市场相关的工作流触及用户财务上下文、场所认证、投资组合数据、自动化或可执行工具之前进行审查。它会检查信息是否确实为建议性内容,确认场所和监管限制,评估数据质量(如流动性、价差、解析规则),确保安全性(不存储私钥、使用只读作用域、要求断路器),并最小化用户数据隐私。审查完成后输出范围、通过/警告/失败项、被阻止的操作、必要缓解措施和安全的下一步。如果涉及可执行步骤,需要单独的实施计划和明确的用户批准。

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

在你喜欢的 AI 中提问

打开一个已预加载此 Agent Skill 的新对话。

文档

预测市场风险审查 是做什么的?

在执行预测市场工作流之前,如果涉及用户财务上下文、场所认证、投资组合数据、自动化或可执行工具,请先使用此技能。

审查关卡

建议边界

  • 确认输出为信息性内容。
  • 删除买入/卖出/持有/规模建议。
  • 保持手动用户决策点明确。

场所与监管边界

  • 识别场所条款、地区限制、账户限额和API规则。
  • 对涉及赌博、衍生品、证券或商品的不明确内容,标记为需要法律审查。
  • 不得绕过场所限制或速率限制。

数据质量

  • 检查市场流动性、价差、解析规则、过时价格和来源时间戳。
  • 将公共场所数据与 Itô 门控数据分开。
  • 若无明确标签,不得混合公共和私有来源。

安全性

  • 不得请求或存储私钥、种子短语或密码。
  • 将 ITO_API_KEY 和场所 API 密钥保留在日志和文档之外。
  • 默认使用只读作用域。
  • 在私有实现添加执行之前,要求断路器、支出限制、空跑和人工批准。

隐私

  • 最小化用户投资组合、财务和知识库数据。
  • 在公共工件中遮盖私有来源。
  • 仅保留审查所需的字段。

输出契约

返回:

  1. 审查范围
  2. 通过/警告/失败项
  3. 被阻止的操作
  4. 必要缓解措施
  5. 安全的下一步

如果请求任何可执行步骤,需要单独的实施计划和明确的用户批准。

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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