Communitygithub.com

预测市场预言机研究

将预测市场作为产品或智能体的数据源、预言机信号进行研究,用于基于源头的市场隐含概率分析、注意事项及集成模式,不提供投资建议。

预测市场预言机研究 是什么?

该技能旨在帮助开发者或AI智能体用户系统评估预测市场是否适合作为数据源、预言机信号或决策智能层。它提供了从定义决策意图到最终集成建议的完整研究流程,涵盖市场选择、信号质量评估(如流动性、价差、市场年龄、交易者激励集中度等)、与非市场来源的对比分析等关键步骤。技能支持多种集成模式,包括作为研究助手、仪表盘信号、智能体记忆输入、警报触发与场景规划工具,且明确禁止将市场价格视为客观真理或提供投资建议。其输出合同要求包含决策上下文、市场来源、信号质量、对比来源、集成推荐及注意事项,最终输出需附上“预测市场信号仅为信息输入,不构成投资建议”的声明。该技能适用于需要将预测市场数据融入仪表盘、智能体或自动化系统,但需谨慎评估信息可靠性的场景。

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

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

预测市场预言机研究 是做什么的?

当考虑将预测市场作为数据源、预测输入、类似预言机的信号或决策智能层时,使用本技能。

边界规则

  • 不要将市场价格视为客观真理。
  • 不提供投资建议或交易推荐。
  • 将市场机制、流动性、激励和裁决规则与隐含信号区分开来。
  • 指出操纵、流动性不足、过时市场以及模糊结果的情况。
  • 对于链上或执行关联系统,在授予任何写权限之前,先运行 llm-trading-agent-security。

研究工作流程

  1. 定义信号旨在辅助的决策。
  2. 查找相关市场、事件、标签和场所。
  3. 记录市场隐含概率,附时间戳和来源链接。
  4. 评估信号质量:
    • 流动性
    • 价差
    • 市场年龄
    • 已知的交易者/激励集中度
    • 裁决机构
    • 地域或账户限制
  5. 与非市场来源进行对比,如申报文件、新闻、民意调查、研究、客户数据或内部指标。
  6. 建议该信号对所述决策是否可用、较弱或不可用。

集成模式

  • 研究助手:为人类分析师提供基于源头的上下文。
  • 仪表盘信号:市场隐含概率与内部指标并列显示。
  • 智能体记忆输入:带时间戳的信号,可后期检索。
  • 警报输入:当概率、价差或流动性超过阈值时通知。
  • 情景规划:在不自动交易的情况下比较多个事件结果。

输出合同

使用:

  1. 决策上下文
  2. 市场来源
  3. 信号质量
  4. 对比来源
  5. 集成推荐
  6. 注意事项

结尾处包含:

预测市场信号是信息输入,而不是投资建议。

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.

相关技能