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 agentmods add skills/grainulation/grainulator/calibratenpx skills add grainulation/grainulator --skill calibrategit clone --depth 1 https://github.com/grainulation/grainulatorWhat 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 | $0.00016 | $0.00446 |
| Opus 5 | $0.00008 | $0.00223 |
| Sonnet 5 | $0.00003 | $0.00089 |
| Haiku 4.5 | $0.00002 | $0.00045 |
Grade A, and why
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 3d 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
/calibrate -- Score predictions vs outcomes
The user wants to check what actually happened after a sprint's recommendations were implemented.
Arguments
$ARGUMENTS
Expected format: /calibrate --outcome "what happened" or /calibrate <claim_id> "actual result"
Instructions
-
Parse the outcome: The user provides outcome data as free text or claim-specific results.
-
Match outcomes to predictions: Use
wheat_searchto find the original estimate, recommendation, or risk claims that predicted something. Compare prediction to actual outcome. -
Create calibration claims as
cal###claims with evidence tierproduction(these are real outcomes):- If prediction was accurate: factual claim noting the match
- If prediction was wrong: factual claim noting the delta (predicted X, actual Y)
- If prediction was partially right: estimate claim with the refined numbers
-
Compute accuracy scorecard:
- Group by evidence tier: what % of
statedvswebvsdocumentedvstestedclaims were accurate? - Group by claim type: are estimates less accurate than factual claims?
- This validates whether the evidence tier system is predictive
- Group by evidence tier: what % of
-
Run
wheat_compile. -
Print scorecard:
Calibration results: Predictions scored: <N> Accurate: <N> (<percent>) Partially accurate: <N> Wrong: <N> Accuracy by evidence tier: stated: <percent> web: <percent> documented: <percent> tested: <percent> Next steps: /brief -- recompile with calibrated data /research <topic> -- investigate where predictions went wrong
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.
- 3d ago First seen · 58 lines · 16 tokens per session scan A f32fab1470bc
calibrate is a skill published in the GitHub repository grainulation/grainulator (86 stars, last pushed 4mo ago), licensed MIT. It adds 16 tokens to every session and 446 once invoked, about $0.0001 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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