---
title: "Agent Skill to Prediction Market Oracle Research"
platform: "Promptally"
canonical_url: "https://app.promptally.dev/p/skill-prediction-market-oracle-research"
modality: "SKILL"
category: "Fullstack & Backend Engineering"
compatible_models: [Universal / Model Agnostic]
access_tier: "Free Public Artifact (No Auth Required)"
license: "MIT"
---

# Agent Skill to Prediction Market Oracle Research

**Summary:** Agent Skill to Prediction Market Oracle Research is a verified SKILL blueprint on Promptally in Fullstack & Backend Engineering optimized for leading AI models. Research prediction markets as data sources or oracle signals for products, agents, dashboards, and corporate decision intelligence. Use for source-grounded analysis of market-implied probabilities, caveats, and integration patterns without investment advice. Use when evaluating prediction markets as a data source or oracle signal for a product, agent, or dashboard. Available to explore, customize with pre-filled variables, and copy with zero authentication required.

## Technical Specifications
| Property | Value |
| :--- | :--- |
| Platform Registry | Promptally (https://app.promptally.dev) |
| Modality | SKILL |
| Category | Fullstack & Backend Engineering |
| Compatible Engines | Universal / Model Agnostic |
| Dynamic Variables | None (Static Blueprint) |
| Approx. Token Length | 576 |
| Access Tier | Free Public Artifact (No Auth Required) |
| License | MIT |

### Pre-Filled Template & Production Source

````md
---
name: prediction-market-oracle-research
description: Research prediction markets as data sources or oracle signals for products, agents, dashboards, and corporate decision intelligence. Use for source-grounded analysis of market-implied probabilities, caveats, and integration patterns without investment advice. Use when evaluating prediction markets as a data source or oracle signal for a product, agent, or dashboard.
metadata:
  origin: ECC
---

# Prediction Market Oracle Research

Use this skill when prediction markets are being considered as a data source,
forecasting input, oracle-like signal, or decision-intelligence layer.

## Guardrails

- Do not treat market prices as objective truth.
- Do not provide investment advice or trading recommendations.
- Separate venue mechanics, liquidity, incentives, and resolution rules from the
  implied signal.
- Call out manipulation, thin liquidity, stale markets, and ambiguous outcomes.
- For on-chain or execution-linked systems, run `llm-trading-agent-security`
  before granting any write authority.

## Research Workflow

1. Define the decision the signal is meant to inform.
2. Find relevant markets, events, tags, and venues.
3. Record market-implied probabilities with timestamps and source links.
4. Evaluate signal quality:
   - liquidity
   - spread
   - market age
   - trader/incentive concentration if known
   - resolution authority
   - geography or account restrictions
5. Compare against non-market sources such as filings, news, polls, research,
   customer data, or internal KPIs.
6. Recommend whether the signal is usable, weak, or unsuitable for the stated
   decision.

## Integration Patterns

- Research assistant: source-grounded context for a human analyst.
- Dashboard signal: market-implied probability alongside internal metrics.
- Agent memory input: a time-stamped signal that can be retrieved later.
- Alerting input: notify when probabilities, spreads, or liquidity cross a
  threshold.
- Scenario planning: compare multiple event outcomes without automating trades.

## Output Contract

Use:

1. decision context
2. market sources
3. signal quality
4. comparison sources
5. integration recommendation
6. caveats

End with:

```text
Prediction-market signals are informational inputs, not investment advice.
```

````

## Verified Execution Output / Benchmark
```text
Verified execution output available on the canonical page.
```

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