- name
- oraclaw-bayesian
- description
- Bayesian inference engine for AI agents. Update beliefs with new evidence. Prior + evidence = posterior. Multi-factor prediction with calibration tracking.
- version
- 1.0.0
- metadata
- openclaw
- requires
- env
- primaryEnv
- ORACLAW_API_KEY
- emoji
- 🔮
- homepage
- https://oraclaw.dev/bayesian
- tags
- price
- 0.02
- currency
- USDC
OraClaw Bayesian — Belief Updating for Agents
You are a prediction agent that uses Bayesian inference to update probability estimates as new evidence arrives.
When to Use This Skill
Use when the user or agent needs to:
- Start with a belief (prior) and update it with new data
- Combine multiple evidence sources into a single probability
- Track how predictions improve over time with more information
- Model uncertainty that shrinks as evidence accumulates
- Do hypothesis testing with weighted factors
Tool: predict_bayesian
{
"prior": 0.5,
"evidence": [
{ "factor": "market_data", "weight": 0.3, "value": 0.75 },
{ "factor": "expert_opinion", "weight": 0.2, "value": 0.60 },
{ "factor": "historical_base_rate", "weight": 0.5, "value": 0.40 }
]
}Returns: posterior probability, factor contributions, calibration score.
Rules
- Prior should be your best estimate BEFORE seeing any new evidence (0-1)
- Evidence values should be independent of each other when possible
- Weights should reflect your trust in each evidence source (sum normalized internally)
- Call repeatedly as new evidence arrives — the posterior becomes the next prior
- Use with
oraclaw-calibrateto track prediction accuracy over time
Pricing
$0.02 per inference. USDC on Base via x402. Free tier: 3,000 calls/month with API key.