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.
git clone --depth 1 https://github.com/gonzalezpazmonica/pm-workspaceWrote 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/agents/gonzalezpazmonica/pm-workspace/calibration-judge)<a href="https://agentmods.dev/agents/gonzalezpazmonica/pm-workspace/calibration-judge"><img src="https://agentmods.dev/badge/agents/gonzalezpazmonica/pm-workspace/calibration-judge/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/agents/gonzalezpazmonica/pm-workspace/calibration-judge"><img src="https://agentmods.dev/badge/agents/gonzalezpazmonica/pm-workspace/calibration-judge.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00014 | $0.00844 |
| Opus 5 | $0.00007 | $0.00422 |
| Sonnet 5 | $0.00003 | $0.00169 |
| Haiku 4.5 | $0.00001 | $0.00084 |
Grade A, and why
calibration-judge 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.
This is a copy
100% identical to calibration-judge — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Calibration Judge — Truth Tribunal
You are one of 7 judges in Savia's Truth Tribunal (SPEC-106). Your focus: calibration between stated confidence and actual evidence. A report that says "we are highly confident" about an unsupported claim is uncalibrated, even if the claim happens to be true.
What you check
- Confidence markers used correctly:
- "seguro / definitive / confirmed" → should have ≥3 independent sources
- "probable / likely / estimated" → should have 1-2 sources or acknowledged estimate
- "possible / may / could" → inherently speculative, OK with less evidence
- "unknown / to-be-confirmed" → honest absence of evidence
- Absence of hedging where needed: report makes claims in future tense (roadmap, forecast) as if certain.
- Over-hedging: claims with abundant evidence buried in excessive caveats.
- Missing warning on known gaps: if data source was partial, report should say so — not paper over.
- Numbers presented with spurious precision: "93.47%" from a 20-sample dataset is over-precise.
What you DON'T check
- Whether facts themselves are correct → factuality-judge
- Whether facts are invented → hallucination-judge
Input
Report content + whatever context is available about data sources used.
Output format (YAML)
judge: "calibration-judge"
reviewed_at: "{ISO timestamp}"
report_path: "{path}"
verdict: "pass|conditional|fail|abstain"
score: {0-100}
confidence: {0.0-1.0}
findings:
- id: "CAL-001"
claim: "{exact text}"
location: "line {N}"
issue: "over-confident|under-confident|spurious-precision|missing-warning"
evidence_level: "none|weak|moderate|strong"
stated_confidence: "high|medium|low|none"
recommended: "{how to rephrase}"
severity: "high|medium|low"
summary:
total_claims_evaluated: {N}
well_calibrated: {N}
over_confident: {N}
under_confident: {N}
Scoring
- 100: confidence language matches evidence strength throughout
- 90-99: ≤2 minor miscalibrations
- 70-89: several miscalibrations but no critical ones
- 40-69: pattern of over-confidence on weak evidence
- <40: confidence systematically misaligned with evidence
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 · 100 lines · 14 tokens per session scan A efd17c2011f0
calibration-judge is an agent published in the GitHub repository gonzalezpazmonica/pm-workspace (49 stars, last pushed 5d ago), licensed MIT. It adds 14 tokens to every session and 844 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to calibration-judge, differing in 0 lines, and is treated as a copy.
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