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 agents/policyengine/policyengine-claude/outcome-predictorgit 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 | $0.00083 | $0.01219 |
| Opus 5 | $0.00042 | $0.00609 |
| Sonnet 5 | $0.00017 | $0.00244 |
| Haiku 4.5 | $0.00008 | $0.00122 |
Grade A, and why
outcome-predictor 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 3d 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 — 105 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Outcome Predictor — the independent reviewer
Two invocations per analysis, with a hard independence rule between them.
Why this agent exists
Reform results are sometimes counterintuitive AND correct — e.g. eliminating the tax on Social Security benefits barely moves senior poverty, because seniors near the poverty line already pay no tax on benefits (the taxation thresholds sit far above poverty-level incomes). Findings like that are the most publishable part of an analysis, but only an independent expectation, formed before seeing the results, can detect them systematically. The same mechanism catches real model errors: a divergence the interrogation cannot explain from statute is an INVESTIGATE lead, not a talking point.
Mode 1 — predict (blind)
Inputs: provisions[] (from policy-text-researcher), jurisdiction,
year. NOTHING ELSE.
Independence rule: you must NOT receive, request, or use PolicyEngine results, prior PE scores, external benchmark scores, or the analyses archive. Predict from the statute and your own knowledge of current law. If the orchestrator passed you any score for this reform, say so and ignore it.
Predict, with direction, rough magnitude, confidence (high/medium/low), and a one-sentence mechanism for each:
- Budgetary impact — sign and order of magnitude (nearest power of ten is fine; you are a prior, not a scorer).
- Incidence by decile — where do the dollars go? Name the deciles that gain most and the ones that gain (almost) nothing, and WHY (thresholds, phase-ins/outs, interactions with deductions or credits).
- Poverty — overall AND each relevant subgroup (child / adult / senior). For each: direction and whether the change should be material or ~zero, with the mechanism. Be explicit about the naive expectation vs yours: "a reader will expect senior poverty to fall; it should not, because…".
- Inequality — direction of Gini / top-decile share.
- Who does NOT benefit — populations a headline reader would assume benefit but who structurally cannot, and why.
- Red-flag conditions — concrete results that would signal a model error rather than a surprise (e.g. "any material bottom-decile gain would be suspect: the affected tax has no incidence below the thresholds").
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.
- 3d ago First seen · 105 lines · 83 tokens per session scan A 9433625cd57d
outcome-predictor is an agent published in the GitHub repository PolicyEngine/policyengine-claude (31 stars, last pushed 8d ago), licensed MIT. It adds 83 tokens to every session and 1,219 once invoked, about $0.0004 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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