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/microsim-runnergit 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 | $0.00056 | $0.04857 |
| Opus 5 | $0.00028 | $0.02429 |
| Sonnet 5 | $0.00011 | $0.00971 |
| Haiku 4.5 | $0.00006 | $0.00486 |
Grade B, and why
microsim-runner scanned grade B with 2 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.
Sends data to an external URLmediumData exfiltration
A POST to an outside endpoint may be telemetry or may be exfiltration; either way the mod talks to somewhere, and you should know where.
response = requests.post( "https://api.policyengine.org/us/policy", Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
response = requests.post( How it starts
The opening of the file, as written. The whole thing — 328 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Microsim Runner
Runs a single reform through PolicyEngine and returns structured impact results.
Two execution paths — pick based on environment:
- API path (default, no install required): call
api.policyengine.org. - Local path (heavy, more flexible): install the
policyenginepackage (latest,>=5.0.1) and run through its managedpe.{us,uk}surfaces (see "Process — Local path").
Inputs
reform_dict: PolicyEngine parameter changes (fromparameter-locator)jurisdiction:{country, state?}(e.g.,{country: us, state: ri}or{country: us}for federal)year: simulation year (default2026)mode:api(default) orlocal
Process — API path
Load the policyengine-python-client skill for current endpoint shapes.
Step 1: Create the policy
import requests
response = requests.post(
"https://api.policyengine.org/us/policy",
json={"data": reform_dict},
)
policy_id = response.json()["result"]["policy_id"]
Step 2: Request economy-wide impacts
Critical: the URL is /economy/{reform_policy_id}/over/{baseline_policy_id}, NOT /over/1. The over/{N} segment is the baseline policy ID to diff against, not a year count. Common baseline IDs:
| Country | Current-law baseline policy_id |
|---|---|
| US | 2 |
| UK | (verify per-environment; typically 1 or 2) |
Calling /over/1 against an arbitrary reform usually returns a misleading parse error or computes against a stale baseline. Always use the documented current-law baseline.
region = state.lower() if state else "us"
baseline_id = 2 # US current law — verify
url = f"https://api.policyengine.org/us/economy/{policy_id}/over/{baseline_id}"
response = requests.get(url, params={
"region": region,
"time_period": str(year),
"dataset": "enhanced_cps", # advertised name; backed by populace-us-2024 as of PE-US 1.729.0
})
Dataset naming — IMPORTANT:
The deployed API advertises only two dataset names at /us/metadata/economy_options/datasets:
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 · 328 lines · 56 tokens per session scan B dd5725793bc1
microsim-runner is an agent published in the GitHub repository PolicyEngine/policyengine-claude (31 stars, last pushed 7d ago), licensed MIT. It adds 56 tokens to every session and 4,857 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it B with 2 findings (sends data to an external url, 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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