prediction-market-risk-review
Helps your agent review prediction market workflows. It checks safety, data quality, and risks before operating.
What it does
This tool checks if a trading or prediction market plan is safe and follows the rules. It ensures sensitive information isn't exposed and the plan doesn't break any restrictions.
When to use it
- ✓Before sharing user financial data.
- ✓When checking if a trading plan follows a website's rules.
- ✓To ensure private information isn't exposed.
Example of what to ask your AI
“Review this trading plan to ensure it's safe and compliant.”
Want more? Open the sections below.
How to use
The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.
Skill link
Ready-to-paste message
How it works
The tool analyzes the plan for buy/sell recommendations, website restrictions, data quality, and security/privacy risks. It checks for sensitive information and rule adherence.
Installation
Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.
In the terminal (Claude Code, Codex, Cursor), in your project folder:
npx skills add affaan-m/ECC --skill prediction-market-risk-reviewOr download just the file:
curl -L https://raw.githubusercontent.com/affaan-m/ECC/main/skills/prediction-market-risk-review/SKILL.md -o SKILL.mdTechnical content (original SKILL.md)
Original text by the author, straight from GitHub.
Prediction Market Risk Review
Use this skill before a prediction-market workflow touches user financial context, venue authentication, portfolio data, automation, or execution-capable tools.
Review Gates
Advice Boundary
- Confirm the output is informational.
- Remove buy/sell/hold/size recommendations.
- Keep manual user decision points explicit.
Venue And Regulatory Boundary
- Identify venue terms, geography restrictions, account limits, and API rules.
- Flag betting, derivatives, securities, or commodities ambiguity for legal review when relevant.
- Do not bypass venue restrictions or rate limits.
Data Quality
- Check market liquidity, spread, resolution rules, stale prices, and source timestamps.
- Separate public venue data from Itô gated data.
- Do not mix public and private sources without labels.
Security
- Do not request or store private keys, seed phrases, or passwords.
- Keep
ITO_API_KEYand venue API keys out of logs and docs. - Use read-only scopes by default.
- Require circuit breakers, spend limits, dry runs, and human approval before any private implementation adds execution.
Privacy
- Minimize user portfolio, financial, and knowledge-base data.
- Redact private sources in public artifacts.
- Preserve only the fields needed for the review.
Output Contract
Return:
- scope reviewed
- pass/warn/fail findings
- blocked actions
- required mitigations
- safe next step
If any execution-capable step is requested, require a separate implementation plan and explicit user approval.
FAQ
What are 'prediction markets'?
These are platforms where people bet on the outcome of future events, like elections or sports results.
What does 'execution risk' mean?
It's the risk that a planned action (like a trade) might not be executed correctly or could cause unexpected problems.
Why is checking 'data quality' important?
To make sure the information used for decision-making is accurate and reliable, preventing mistakes based on incorrect data.
Details
Original description by the author
Review prediction-market, basket, oracle, and trading-agent workflows for compliance, safety, data-quality, privacy, and execution risk. Use before any workflow handles venue auth, user portfolio data, API keys, or trade planning.
repo: affaan-m/ECC
Popularity
- GitHub stars
- 280k
- Ranking position
- #258
- License
- MIT
- Updated
- 10/15/2026
Comments
- …
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