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 commands/policyengine/policyengine-claude/prior-scoresgit clone --depth 1 https://github.com/PolicyEngine/policyengine-claudeWrote 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.
[](https://agentmods.dev/commands/policyengine/policyengine-claude/prior-scores)<a href="https://agentmods.dev/commands/policyengine/policyengine-claude/prior-scores"><img src="https://agentmods.dev/badge/commands/policyengine/policyengine-claude/prior-scores.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00036 | $0.00620 |
| Opus 5 | $0.00018 | $0.00310 |
| Sonnet 5 | $0.00007 | $0.00124 |
| Haiku 4.5 | $0.00004 | $0.00062 |
Grade A, and why
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 5d 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 — 47 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prior scores lookup
Standalone entry for external-benchmark research. Runs prior-scores-finder (Stage 3 of /analyze-policy) without the microsim or comparison stages.
When to use
- "What has JCT / CBO scored on this reform shape?"
- "Have any think-tanks published estimates for a similar SALT cap change?"
- "I already have a rough sense of my number; I just want to see whose external estimates I should benchmark against."
Do NOT use this to substitute for a microsim — this just aggregates what OTHER organizations have scored. /analyze-policy compares PE's own model against them.
Arguments
$ARGUMENTS — a reform description or bill reference. Same argument shape as /analyze-policy.
Flags:
--country {us|uk|ca}— determines which scorekeepers to consult (frompresets/scorekeepers.yaml)--tier {2|3|all}— Tier 2 = official fiscal offices (JCT, CBO, OBR, PBO); Tier 3 = think-tanks (CRFB, TPC, IFS, etc.);all= both. Defaultall.--domain <tag>— restrict to scorekeepers marked as covering this domain (tax,benefits,healthcare,distributional, etc.)
What this command does
- Loads the scorekeepers registry (
presets/scorekeepers.yaml) filtered by country + tier + domain. - Also consults Tier 0 (local
analyses/archive) viascripts/analyses_kb.py— surfaces prior PE runs on the same parameter family. - Also consults Tier 1 (PolicyEngine published research) via the
policyengine-prior-scoresskill. - Iterates through each scorekeeper's
search_hints, runs WebSearch queries, and extracts headline magnitudes. - Returns a structured benchmark cluster ready to feed into a research writeup — same shape as
benchmark_sources[]in an archived analysis.
Output
Ranked list of external scores with:
- Source name + URL
- Their estimate (10-year cost, per-year cost, poverty change, or whatever they published)
- Reform shape they scored (may differ from yours — note structural distance)
- Methodology notes (static / dynamic, dataset, baseline)
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.
- 5d ago First seen · 47 lines · 36 tokens per session scan A f6a4dd8a7e55
prior-scores is a command published in the GitHub repository PolicyEngine/policyengine-claude (32 stars, last pushed 3d ago), licensed MIT. It adds 36 tokens to every session and 620 once invoked, about $0.0002 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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