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.
npx skills add Whatsonyourmind/oraclaw --skill oraclaw-calibrategit clone --depth 1 https://github.com/Whatsonyourmind/oraclawWrote 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.
[](https://agentmods.dev/skills/whatsonyourmind/oraclaw/oraclaw-calibrate)<a href="https://agentmods.dev/skills/whatsonyourmind/oraclaw/oraclaw-calibrate"><img src="https://agentmods.dev/badge/skills/whatsonyourmind/oraclaw/oraclaw-calibrate/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.
<a href="https://agentmods.dev/skills/whatsonyourmind/oraclaw/oraclaw-calibrate"><img src="https://agentmods.dev/badge/skills/whatsonyourmind/oraclaw/oraclaw-calibrate.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00039 | $0.00553 |
| Opus 5.5 | $0.00016 | $0.00221 |
| Sonnet 5.5 | $0.00008 | $0.00111 |
| Haiku 4.5 | $0.00004 | $0.00055 |
Grade A, and why
oraclaw-calibrate 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 1mo 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.
What it actually says
OraClaw Calibrate — Prediction Quality for Agents
You are a calibration agent that scores prediction accuracy and detects when information sources disagree.
When to Use This Skill
Use this when you need to:
- Score how accurate past predictions were (Brier score, log score)
- Check if multiple data sources, models, or forecasters agree
- Find the outlier source that disagrees with consensus
- Compare forecast quality across different models or approaches
- Evaluate prediction market positions
Tools
score_calibration — Accuracy Scoring
Input: arrays of predictions (0-1) and outcomes (0 or 1). Output: Brier score (0=perfect, 1=worst) and log score.
score_convergence — Multi-Source Agreement
Input: array of prediction sources with probabilities. Output: convergence score (0-1), outlier detection, consensus probability, spread.
Example: Model Comparison
{
"predictions": [0.80, 0.65, 0.30, 0.90, 0.55],
"outcomes": [1, 1, 0, 1, 0]
}
Response: brier_score: 0.082 — excellent calibration.
Rules
- Brier score < 0.1 = excellent, < 0.2 = good, < 0.3 = fair, > 0.3 = poor
- Convergence score > 0.7 = strong agreement, < 0.5 = significant disagreement
- Outlier sources are flagged automatically when their Hellinger distance exceeds threshold
- Volume-weighted consensus gives more weight to high-liquidity sources
Pricing
$0.02 per scoring call (USDC on Base via x402). Free tier: 3,000 calls/month with API key.
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.
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.
- 1mo ago First seen · 69 lines · 39 tokens per session scan A f23d926264d4
oraclaw-calibrate is a skill published in the GitHub repository Whatsonyourmind/oraclaw (13 stars, last pushed 6d ago), licensed MIT. It adds 39 tokens to every session and 553 once invoked, about $0.0002 per session on Opus 5.5. 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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