What is oraclaw-bayesian?

A probability tool that starts with an initial belief and updates it when new evidence arrives. Bayesian inference combines the prior belief and evidence into a revised probability called the posterior.

Use it to combine evidence sources, test hypotheses, track changing predictions, model uncertainty, and update a probability repeatedly as new data arrives.

It gives agents a structured way to change predictions as information accumulates. It also shows how much each evidence source contributed to the result.

oraclaw-bayesian — Skill for Claude Code and Codex by Whatsonyourmind

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 Whatsonyourmind/oraclaw --skill oraclaw-bayesian

Clone the repo

git clone --depth 1 https://github.com/Whatsonyourmind/oraclaw

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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/whatsonyourmind/oraclaw/oraclaw-bayesian/github.svg)](https://agentmods.dev/skills/whatsonyourmind/oraclaw/oraclaw-bayesian)

Your own site

<a href="https://agentmods.dev/skills/whatsonyourmind/oraclaw/oraclaw-bayesian"><img src="https://agentmods.dev/badge/skills/whatsonyourmind/oraclaw/oraclaw-bayesian/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.

Your own site · 80×15

<a href="https://agentmods.dev/skills/whatsonyourmind/oraclaw/oraclaw-bayesian"><img src="https://agentmods.dev/badge/skills/whatsonyourmind/oraclaw/oraclaw-bayesian.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 34 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 487 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. Third-party audits

NVIDIA SkillSpector pass 7 Sept 2026

How audits are shown

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.

Model	Per session	Once invoked
Fable 5.1	$0.00034	$0.00487
Opus 5	$0.00017	$0.00244
Sonnet 5	$0.00007	$0.00097
Haiku 4.5	$0.00003	$0.00049

Measured 11d ago against content hash 62140498c1b7, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

oraclaw-bayesian 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 11d 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.

mission-control/packages/clawhub-skills/oraclaw-bayesian/SKILL.md · 63 lines

What it actually says

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-calibrate to track prediction accuracy over time

Pricing

$0.02 per inference. USDC on Base via x402. Free tier: 3,000 calls/month with API key.

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

package.json 803 B

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.

11d ago First seen · 63 lines · 34 tokens per session scan A 62140498c1b7

Subscribe to this mod's changes

oraclaw-bayesian is a skill published in the GitHub repository Whatsonyourmind/oraclaw (13 stars, last pushed today), licensed MIT. It adds 34 tokens to every session and 487 once invoked, about $0.0002 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 2026-08-30.

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