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/policyengine/policyengine-claude/policyengine-calibration-diagnosticsnpx skills add PolicyEngine/policyengine-claude --skill policyengine-calibration-diagnosticsgit clone --depth 1 https://github.com/PolicyEngine/policyengine-claudeWrote 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/policyengine/policyengine-claude/policyengine-calibration-diagnostics)<a href="https://agentmods.dev/skills/policyengine/policyengine-claude/policyengine-calibration-diagnostics"><img src="https://agentmods.dev/badge/skills/policyengine/policyengine-claude/policyengine-calibration-diagnostics.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 | $0.00188 | $0.02653 |
| Opus 5 | $0.00094 | $0.01326 |
| Sonnet 5 | $0.00038 | $0.00531 |
| Haiku 4.5 | $0.00019 | $0.00265 |
Grade A, and why
policyengine-calibration-diagnostics 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 — 177 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PolicyEngine calibration diagnostics
Converts tribal "I'd check the takeup rate first" knowledge into a structured sensitivity
registry, and pairs it with the live per-target calibration API so hypotheses are ranked against
real relative_error numbers rather than assumptions. When an /analyze-policy comparison
returns INVESTIGATE, this skill supplies the ranked candidate causes.
The calibrated microdata is now a Microcosm build (see the policyengine-data skill for how
targets, weights, and L0 sparsity work). Calibration targets live in the Microcosm build's target
set — not in a hand-maintained loss file — and their fit is queryable per release from the
dashboard API below.
When to use
- Stage 5.6 / Stage 6 of
/analyze-policy— invoked by thecalibration-diagnosticsagent. - Code review of microsim PRs where the headline number differs from priors.
- Designing or auditing a Microcosm calibration target.
- Debugging why a state-level run looks volatile.
Top-level architecture
PolicyEngine microsim results depend on three layers:
- Country model logic (policyengine-us, policyengine-uk, policyengine-canada) — formulas, parameters.
- Calibrated microdata (Microcosm) — survey weights + imputations matched to administrative targets.
- Behavioral assumptions (takeup rates, labor-supply elasticities) — usually parameters but easy to overlook.
A magnitude mismatch is almost always rooted in layer 2 or 3, not layer 1 (layer-1 mismatches show up as outright simulation errors, not magnitude drift).
Live calibration API (check this first)
Per-target fit for the current Microcosm release, no auth, reads the release from Hugging Face:
BASE = https://calibration-diagnostics.vercel.app/calibration/dashboard/api/populace
GET {BASE}/target-diagnostics?source=<source> # every target for a source, with relative_error
GET {BASE}/target-investigation?target=<id> # full investigation packet for one target
GET {BASE}/releases # release ids for pinning
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 · 177 lines · 188 tokens per session scan A a55216268497
policyengine-calibration-diagnostics is a skill published in the GitHub repository PolicyEngine/policyengine-claude (32 stars, last pushed 3d ago), licensed MIT. It adds 188 tokens to every session and 2,653 once invoked, about $0.0009 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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