reform-classifier

A decision step for PolicyEngine, a tool that models tax and benefit policies. It checks whether a proposed policy change can use existing model settings, needs new model logic, or cannot be represented by the model.

In plain words
What is it for?
Use it in policy analysis to decide whether to proceed with a reform simulation, record model-development work, or write a note explaining why the reform cannot be modeled.
Why use it?
It prevents a microsimulation, meaning a computer estimate of policy effects, from running when the policy is not ready or not supported. It also distinguishes missing deployed releases from genuinely structural work.

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/reform-classifier
Clone the repo
git clone --depth 1 https://github.com/PolicyEngine/policyengine-claude
Per session 64 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,479 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 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 $0.00064 $0.01479
Opus 5 $0.00032 $0.00740
Sonnet 5 $0.00013 $0.00296
Haiku 4.5 $0.00006 $0.00148

Measured 2d ago against content hash fd9c95faa00b, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

reform-classifier 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.

agents/reform-classifier.md · 118 lines

How it starts

The opening of the file, as written. The whole thing — 118 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Reform Classifier

The Stage-2 gate of /analyze-policy. Takes the provisions + parameter-locator verdicts and returns one of three outcomes:

  • parametric — every provision maps to an existing PolicyEngine parameter. The microsim runner can execute the reform-dict as-is.
  • structural — at least one provision requires new variable logic, formula changes, or a new sub-program. The reform needs model changes before it can be scored; emit a backlog note.
  • not-possible — the reform falls outside PolicyEngine's modeling scope (e.g., a non-tax-benefit policy, a change to the tax administration process, a behavioral mandate).

Inputs

  • provisions (from policy-text-researcher)
  • parameter_locator_verdicts (one per provision, from parameter-locator)
  • jurisdiction

Decision tree

For each provision:

  1. Did parameter-locator return verdict: "parametric" with confidence: "high"? → parametric.
  2. Did it return verdict: "deployed-model-lag"? → deployed-model-lag for this provision (the parameter exists on master but not on the deployed API release; nothing structural to fix here — just wait for the next release).
  3. Did it return verdict: "no-parameter" with a structural_hint? → structural for this provision.
  4. Does the provision describe something not in scope? → not-possible for this provision.
    • Out of scope includes: tax administration, audit policies, IRS staffing, enforcement, behavioral mandates without dollar consequences, non-tax-benefit programs (housing zoning, education curriculum), retroactive provisions that PolicyEngine cannot back-date.

Aggregate across provisions (order matters — first match wins):

  • If any deployed-model-lag and no structural/not-possible → reform is deployed-model-lag (overall). Emit missing_paths and next_action: "Wait for next PE-{country} release, or re-run with --skip-microsim for process-test mode." Stop the pipeline.
  • If any not-possible → reform is not-possible (overall). Emit rationale; stop.
  • If any structural → reform is structural (overall). Emit which provisions need model changes with model_change_estimate; stop the pipeline.
  • If all parametric → reform is parametric. Proceed to Stage 3/4.

Read the full file on GitHub · 118 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 · 118 lines · 64 tokens per session scan A fd9c95faa00b

Subscribe to this mod's changes

reform-classifier is an agent published in the GitHub repository PolicyEngine/policyengine-claude (31 stars, last pushed 7d ago), licensed MIT. It adds 64 tokens to every session and 1,479 once invoked, about $0.0003 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.