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 agents/policyengine/policyengine-claude/model-corroboratorgit clone --depth 1 https://github.com/PolicyEngine/policyengine-claudeWhat 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 | $0.00093 | $0.02941 |
| Opus 5 | $0.00046 | $0.01470 |
| Sonnet 5 | $0.00019 | $0.00588 |
| Haiku 4.5 | $0.00009 | $0.00294 |
Grade A, and why
model-corroborator scanned grade A with 1 finding 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -sS -X POST https://api.policyengine.org/us/policy \ How it starts
The opening of the file, as written. The whole thing — 188 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Model Corroborator
Purpose: independent verification that the model's data + mechanics are directionally correct for the reform's parameter family, by re-running externally-scored nearby reforms and checking the model can reproduce them.
This is the answer to "we have no exact-shape external comparator — how do we know our headline number isn't drifting?" Rather than widening tolerance bands and shrugging, we run external benchmarks' exact reform shapes through our model and check the agreement explicitly.
When this stage fires
reform-comparator calls this agent when its Step 2b external-benchmark agreement check yields fewer than 2 directly-comparable external sources within ±25% (the threshold for an unambiguous PASS). The original reform has no clean external anchor, so we corroborate the parameter-family calibration via mirror-shape runs.
/analyze-policy orchestrates this between Phase 5 (compare) and Phase 6 (calibration-diagnostics). If corroboration passes, the final verdict is upgraded to PASS-WITH-CORROBORATION. If corroboration fails, escalate directly to calibration-diagnostics (INVESTIGATE).
Inputs
our_reform_dict— the original reform we just ran (for parameter-family context)our_jurisdiction—{country, state?}benchmark_sources[]— the Tier 3 think-tank cluster fromprior-scores-finder, including each source'sreform_shapeandtheir_estimate_10yr_billionour_microsim_result— Stage 4 output (for the parameter-family sensitivity context)our_baseline_policy_id— typically 2 (US) or country equivalent
Process
Step 1: Pick mirror-shape candidates
Frozen-registry rule: candidates come ONLY from benchmark_sources[] —
the registry pre-registered before the microsim ran. You know our result,
so discovering new sources now would let the result pick its own judges.
WebFetch is for retrieving the already-registered sources' documents (to
extract their exact reform shapes), never for finding additional sources.
If the registry has no usable candidate, return NO-CORROBORATION-POSSIBLE
— do not go looking.
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 · 188 lines · 93 tokens per session scan A 0d378c565bdb
model-corroborator is an agent published in the GitHub repository PolicyEngine/policyengine-claude (31 stars, last pushed 8d ago), licensed MIT. It adds 93 tokens to every session and 2,941 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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