microsim-runner

A PolicyEngine audit workflow that reads records of user actions, resource changes, sign-ins, and access events. Harness is the platform whose activity records it examines.

In plain words
What is it for?
Use it to list and filter events by organization, project, user, resource, or action such as create, update, delete, login, logout, and access.
Why use it?
It makes it easier to review account activity and assemble evidence for compliance checks or security investigations.

Agent

Install

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.

agentmods
npx agentmods add agents/policyengine/policyengine-claude/microsim-runner
Clone the repo
git clone --depth 1 https://github.com/PolicyEngine/policyengine-claude
Per session 56 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 4,857 The whole file, excluding the scripts and references it only reads on demand.
Security scan B 2 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 2d ago against content hash dd5725793bc1, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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(
agents/microsim-runner.md · 328 lines

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:

  1. API path (default, no install required): call api.policyengine.org.
  2. Local path (heavy, more flexible): install the policyengine package (latest, >=5.0.1) and run through its managed pe.{us,uk} surfaces (see "Process — Local path").

Inputs

  • reform_dict: PolicyEngine parameter changes (from parameter-locator)
  • jurisdiction: {country, state?} (e.g., {country: us, state: ri} or {country: us} for federal)
  • year: simulation year (default 2026)
  • mode: api (default) or local

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:

Read the full file on GitHub · 328 lines

Changes

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.

  1. 2d ago First seen · 328 lines · 56 tokens per session scan B dd5725793bc1

Subscribe to this mod's changes

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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