prediction-market-oracle-research

prediction-market-oracle-research is a skill for Claude Code, Codex from nklofy/code-agent-skills. It costs 50 tokens per session (441 once invoked), scanned A, original, Apache-2.0.

A research guide for using prediction markets as data sources or signals in products, agents, dashboards, and business decisions. Prediction markets are markets where participants trade contracts tied to the outcomes of future events.

In plain words
What is it for?
Use it to find relevant markets, record their implied probabilities, assess signal quality, compare them with other evidence, and plan integrations for decision-support systems.
Why use it?
It helps assess whether market-implied probabilities are useful without treating them as certain facts or investment advice. It calls attention to issues such as low trading activity, stale prices, manipulation, and unclear settlement rules.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to find relevant markets, record their implied probabilities, assess signal quality, compare them with other evidence, and plan integrations for decision-support systems.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/nklofy/code-agent-skills/prediction-market-oracle-research
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add nklofy/code-agent-skills --skill prediction-market-oracle-research
Clone the repo
git clone --depth 1 https://github.com/nklofy/code-agent-skills

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for prediction-market-oracle-research

README.md
[![agentmods](https://agentmods.dev/badge/skills/nklofy/code-agent-skills/prediction-market-oracle-research/github.svg)](https://agentmods.dev/skills/nklofy/code-agent-skills/prediction-market-oracle-research)
Your own site
<a href="https://agentmods.dev/skills/nklofy/code-agent-skills/prediction-market-oracle-research"><img src="https://agentmods.dev/badge/skills/nklofy/code-agent-skills/prediction-market-oracle-research/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for prediction-market-oracle-research

Your own site · 80×15
<a href="https://agentmods.dev/skills/nklofy/code-agent-skills/prediction-market-oracle-research"><img src="https://agentmods.dev/badge/skills/nklofy/code-agent-skills/prediction-market-oracle-research.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 50 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 441 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00050 $0.00441
Opus 5 $0.00025 $0.00220
Sonnet 5 $0.00010 $0.00088
Haiku 4.5 $0.00005 $0.00044

Measured 8d ago against content hash 86d9ceb6abb8, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

prediction-market-oracle-research scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

affaan-m-ECC/prediction-market-oracle-research/SKILL.md · 65 lines

What it actually says

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:

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

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 8d ago First seen · 65 lines · 50 tokens per session scan A 86d9ceb6abb8

Subscribe to this mod's changes

prediction-market-oracle-research is a skill published in the GitHub repository nklofy/code-agent-skills (18 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 50 tokens to every session and 441 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.

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