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/aaronbassett/agent-foundry/calibrationgit clone --depth 1 https://github.com/aaronbassett/agent-foundryWrote 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/commands/aaronbassett/agent-foundry/calibration)<a href="https://agentmods.dev/commands/aaronbassett/agent-foundry/calibration"><img src="https://agentmods.dev/badge/commands/aaronbassett/agent-foundry/calibration.svg" alt="Measured on agentmods" 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 | $0.00033 | $0.00821 |
| Opus 5 | $0.00016 | $0.00411 |
| Sonnet 5 | $0.00007 | $0.00164 |
| Haiku 4.5 | $0.00003 | $0.00082 |
Grade A, and why
calibration 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 5d 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.
How it starts
The opening of the file, as written. The whole thing — 67 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/decision-making:calibration
When to use
The decision depends on a quantitative or probabilistic estimate, and you need a confidence check on the estimate itself — not just an answer. Examples: "how long will the migration take?", "what's the probability this ships by Q3?", "how much will the infra cost increase?"
Cost tier
Low. Exactly 3 parallel general-purpose subagents, one round. Fan-out is not configurable. See references/cost-tiers.md.
Input
A quantitative or probabilistic question + relevant context.
Why fan-out is fixed at 3
Two estimators give no triangulation signal — if they disagree, you have no way to tell which one is closer, and if they agree you can't distinguish real convergence from shared blind spots. Five or more estimators is diminishing returns for double the cost: the third estimator buys most of the signal, and each additional one buys less. Keeping the fan-out fixed at 3 forces users toward the right shape for this tool and prevents misuse as a generic parallel-thinking knob that gets dialed up whenever someone wants "more thinking."
Workflow
-
Spawn exactly 3 parallel estimators — each
general-purposesubagent is given the same question, the same context, and the same prompt. They cannot see each other's outputs. Use this exact prompt template:You are producing a calibrated estimate for a quantitative or probabilistic question. The question: [QUESTION] Context: [CONTEXT] Return three things: 1. A point estimate (a single number with units) 2. A confidence interval (e.g., "80% CI: 4-12 weeks") 3. The reasoning behind your estimate (2-4 sentences explaining what assumptions drove it) Do not hedge with "it depends" unless you also give a specific number that you would commit to if forced. Do not copy the question back. Commit to a number. Good-faith rules: no fabricated base rates, no straw-manning the question, acknowledge what you're uncertain about inside your reasoning.
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.
- 5d ago First seen · 67 lines · 33 tokens per session scan A 41ff93be7471
calibration is a command published in the GitHub repository aaronbassett/agent-foundry (4 stars, last pushed 20d ago), licensed MIT. It adds 33 tokens to every session and 821 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 2026-08-31.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.