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-model-developmentnpx skills add PolicyEngine/policyengine-claude --skill policyengine-model-developmentgit 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 | $0.00202 | $0.01453 |
| Opus 5 | $0.00101 | $0.00727 |
| Sonnet 5 | $0.00040 | $0.00291 |
| Haiku 4.5 | $0.00020 | $0.00145 |
Grade A, and why
policyengine-model-development 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 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.
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 — 92 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PolicyEngine model development
Engineering patterns for the country-model repos — policyengine-us, policyengine-uk,
policyengine-canada. These packages encode enacted law (and proposed reforms) as variables
(Python formulas) driven by parameters (YAML), validated by YAML tests, all running on
the vectorized policyengine-core engine.
This skill is the how-to-write-it layer. To run calculations or score reforms, use the
policyengine skill instead. Verified against policyengine-us 1.764.x / policyengine-core 3.30.x
(2026-07).
Mental model
- Parameters are a YAML tree under
parameters/gov/.... Each leaf is a dated value (or a bracket/breakdown table) with required metadata. Accessed in formulas asparameters(period).gov.agency.program.thing. Federal undergov/{agency}/, state undergov/states/{st}/, contributed reforms undergov/contrib/. - Variables live one-per-file under
variables/gov/..., each aVariablesubclass withvalue_type/entity/definition_period/ metadata and either aformulaor anadds/subtractslist (never both). Formulas run vectorized over the whole population. - Entities nest:
Person→TaxUnit/SPMUnit/Family/MaritalUnit/Household(US);Person→BenUnit/Household(UK). Aggregation across entity levels is automatic viaadds/add(). - Tests are YAML files mirroring the variable path under
tests/policy/baseline/..., asserting outputs for a described household at a period.
The absolute musts
- Never hardcode a numeric policy value in a formula. Every threshold, rate, and amount
comes from a parameter (
p.income_limit, not2000). Bare0/1/-1andMONTHS_IN_YEARare the only acceptable literals. See references/variables.md. - Vectorize everything. No
if/elif/elseorand/or/noton entity arrays — usewhere/select/&/|/~. Pythonifis allowed only on scalar parameters (if p.flat_applies:). See references/vectorization.md. adds/subtractsXORformula— never both in one variable. A pure sum uses theaddsattribute with no formula; anything else uses a formula (withadd()inside). See references/periods-and-aggregation.md.- Get the period right. From a MONTH formula, YEAR flow variables (income) use
period(auto ÷12); YEAR stocks/counts/ages/booleans useperiod.this_year(no division). See references/periods-and-aggregation.md. uv runfor everything.uv run pytest ...,uv run policyengine-core test .... Never barepytest. Format withuv run ruff(line length 88, the default — not 79). There is noblackin this toolchain.- Changelog = a towncrier fragment, never an edit to
CHANGELOG.md. Writechangelog.d/{branch-name}.{added|fixed|changed}.mdwith one line. - Branch from the PolicyEngine upstream repo, not a fork. Encoding work targets
origin/mainofPolicyEngine/policyengine-us(etc.).
What ships with it
7 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 92 lines · 202 tokens per session scan A 849a68b935c4
policyengine-model-development is a skill published in the GitHub repository PolicyEngine/policyengine-claude (31 stars, last pushed 7d ago), licensed MIT. It adds 202 tokens to every session and 1,453 once invoked, about $0.0010 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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