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 commands/grainulation/wheat/calibrategit clone --depth 1 https://github.com/grainulation/wheatWhat 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 | $0.00000 | $0.00327 |
| Opus 5 | $0.00000 | $0.00163 |
| Sonnet 5 | $0.00000 | $0.00065 |
| Haiku 4.5 | $0.00000 | $0.00033 |
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 2d 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 Past Predictions Against Reality
You are checking what actually happened after a sprint's recommendations were implemented. This closes the feedback loop by comparing predictions to outcomes.
Process
-
Parse the outcome: The user provides outcome data, either as:
- Free text:
/calibrate --outcome "Shipped Auth0. Took 3 weeks not 2. Costs $18K/year not $15K." - Claim-specific:
/calibrate e003 "actual: 3 weeks, $18K/year"
- Free text:
-
Read the sprint data and match outcomes to original predictions.
-
Create calibration claims (
cal###prefix, evidence:production). -
Compute accuracy scorecard by evidence tier, source origin, and claim type.
-
Write/update calibration.json and add claims to claims.json:
npx @grainulation/wheat compile --summary -
Print the scorecard to the terminal.
The meta-insight
This is the only command that validates the framework itself. If tested claims are right 95% of the time and web 65%, the tier system works.
Git commit
Commit: wheat: /calibrate — scored <N> predictions against outcomes
Tell the user
- The accuracy scorecard
- Which predictions were wrong and by how much
- Whether the evidence tier hierarchy is predictive
- Suggest: future sprints should weight evidence tiers based on this data
$ARGUMENTS
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.
- 2d ago First seen · 42 lines · 0 tokens per session scan A 217c0f169aeb
calibrate is a command published in the GitHub repository grainulation/wheat (20 stars, last pushed 4mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 327 tokens. 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.
Other commands, from other repositories
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guide
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pm-review
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new
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card
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linear-bulk
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