What is policyengine-prior-scores?

A curated reference for previously published PolicyEngine reform scores and the repository tools that store or find them. PolicyEngine is a project that models the effects of public-policy changes.

Use it to find comparable reform estimates, inspect named reform presets, support research writing, or run prior-score lookups.

It gives new analyses a real earlier result to compare with instead of choosing a benchmark without context.

policyengine-prior-scores — Skill for Claude Code by PolicyEngine

Install

Getting it into your agent

It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.

Clone the repo

git clone --depth 1 https://github.com/PolicyEngine/policyengine-claude

agentmods

npx agentmods add skills/policyengine/policyengine-claude/policyengine-prior-scores

Made for: Claude Code.

Or install analysis-tools, the plugin that ships this one along with the rest of its 11 skills.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

README.md

[![agentmods](https://agentmods.dev/badge/skills/policyengine/policyengine-claude/policyengine-prior-scores/github.svg)](https://agentmods.dev/skills/policyengine/policyengine-claude/policyengine-prior-scores)

Your own site

<a href="https://agentmods.dev/skills/policyengine/policyengine-claude/policyengine-prior-scores"><img src="https://agentmods.dev/badge/skills/policyengine/policyengine-claude/policyengine-prior-scores/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

Your own site · 80×15

<a href="https://agentmods.dev/skills/policyengine/policyengine-claude/policyengine-prior-scores"><img src="https://agentmods.dev/badge/skills/policyengine/policyengine-claude/policyengine-prior-scores.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 217 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 2,367 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits

NVIDIA SkillSpector pass 7 Sept 2026

How audits are shown

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.

Model	Per session	Once invoked
Fable 5.1	$0.00217	$0.02367
Opus 5.5	$0.00087	$0.00947
Sonnet 5.5	$0.00043	$0.00473
Haiku 4.5	$0.00022	$0.00237

Measured 2mo ago against content hash 26d0a3606f1b, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

policyengine-prior-scores 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 2mo 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.

skills/policyengine-prior-scores/SKILL.md · 173 lines

How it starts

The opening of the file, as written. The whole thing — 173 lines — stays where its author put it; the contents beside it link to each section on GitHub.

PolicyEngine prior scores

Finding a prior score to anchor a new analysis has two parts: a curated anchor list of notable PE reforms (below, with URLs) and the real infrastructure in this repo that enumerates presets, external scorekeepers, and local archived runs. Earlier versions of this skill described a data/prior-scores.json file with get_priors_by_program() query functions — those never existed. The sections below point only at things that are actually on disk or live.

When to use

/analyze-policy Stage 3 (find prior scores) and Stage 5 (compare the microsim to them).
The /prior-scores command (external-benchmark research without running the microsim).
The prior-scores-finder agent's Tier-1 lookup.
Writing a research post and citing PE's prior work on a similar reform.

Real infrastructure in this repo

Presets — named reform-dicts with published scores

presets/reforms/*.yaml and presets/baselines/*.yaml are callable-by-name reform/baseline dicts. A preset may carry a published_scores block recording what external sources scored that exact shape — which is what makes external corroboration turn-key.

python3 scripts/presets.py list                 # all presets
python3 scripts/presets.py list --category reform --country us
python3 scripts/presets.py show arpa-ctc-restoration   # prints the reform_dict + published_scores

from scripts.presets import load_preset
preset = load_preset("arpa-ctc-restoration")
preset["reform_dict"]        # ready to pass as a reform
preset["published_scores"]   # [{source: JCT, ten_year_billion: 1100, ...}, ...]

Presets on disk today (verify with python3 scripts/presets.py list — load_preset raises on names that don't exist): reforms arpa-ctc-restoration, obbba-salt-bump; baseline tcja-extension. A pre-obbba baseline is wanted but not yet authored. Add a preset by copying a nearby file and, if any external source scored the shape, filling in published_scores — see presets/README.md.

Read the full file on GitHub · 173 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.

2mo ago First seen · 173 lines · 217 tokens per session scan A 26d0a3606f1b

Subscribe to this mod's changes

policyengine-prior-scores is a skill published in the GitHub repository PolicyEngine/policyengine-claude (31 stars, last pushed yesterday), licensed MIT. It adds 217 tokens to every session and 2,367 once invoked, about $0.0009 per session on Opus 5.5. 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.

Analytics and metrics

policyengine-prior-scores next to 6 skills from other repositories
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policyengine-prior-scoresPolicyEngine/policyengine-claude	Skill	31	yesterday	MIT	A	217	Claude Code
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local-ai-agentsmicrosoft/ai-agents-for-beginners	Skill	79k	2d ago	MIT	A	200	Claude Code, Codex
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