AGENT SKILL

prediction-market-risk-review

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.

License 许可
Apache-2.0
最近更新
2026-11-11
Stars
★ 13
Forks
1

01 前言 Front Matter

name
prediction-market-risk-review
origin
ECC
description
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.

03 SKILL.md 正文

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_KEY and 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:

  1. scope reviewed
  2. pass/warn/fail findings
  3. blocked actions
  4. required mitigations
  5. safe next step

If any execution-capable step is requested, require a separate implementation plan and explicit user approval.

04 同仓库更多 Skills

Horonomos-laps
Horonomos-laps/Yana-AI

openai--sentry--sentry

Use when the user asks to inspect Sentry issues or events, summarize recent production errors, or pull basic Sentry health data via the Sentry API; perform read-only queries with the bundled script and require `SENTRY_AUTH_TOKEN`.

★ 13
Horonomos-laps
Horonomos-laps/Yana-AI

ito-basket-compare

Compare Itô prediction-market baskets against a user's knowledge base, portfolio notes, financial context, watchlist, or research thesis. Use for read-only basket comparison and gap analysis without investment advice or live trading.

★ 13
Horonomos-laps
Horonomos-laps/Yana-AI

implementing-network-traffic-baselining

Build network traffic baselines from NetFlow/IPFIX data using Python pandas for statistical analysis, z-score anomaly detection, and hourly/daily traffic pattern profiling

★ 13
Horonomos-laps
Horonomos-laps/Yana-AI

detecting-evasion-techniques-in-endpoint-logs

Detects defense evasion techniques used by adversaries in endpoint logs including log tampering, timestomping, process injection, and security tool disabling. Use when investigating suspicious endpoint behavior, building detection rules for evasion tactics, or conducting threat hunting for stealthy adversary activity. Activates for requests involving evasion detection, defense evasion analysis, log tampering detection, or MITRE ATT&CK TA0005.

★ 13
Horonomos-laps
Horonomos-laps/Yana-AI

implementing-microsegmentation-with-guardicore

Implementing microsegmentation using Akamai Guardicore Segmentation to map application dependencies, create granular network policies, visualize east-west traffic flows, and enforce least-privilege communication between workloads across data centers and cloud.

★ 13
Horonomos-laps
Horonomos-laps/Yana-AI

ramdisk-tmpfs-patterns

暂无简介

★ 13