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-user-guidenpx skills add PolicyEngine/policyengine-claude --skill policyengine-user-guidegit 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 | $0.00103 | $0.01370 |
| Opus 5 | $0.00051 | $0.00685 |
| Sonnet 5 | $0.00021 | $0.00274 |
| Haiku 4.5 | $0.00010 | $0.00137 |
Grade A, and why
policyengine-user-guide 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 — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PolicyEngine user guide
Help someone use policyengine.org to compute how a policy affects a specific household or the population. The app has three stable surfaces: a household calculator, a policy editor, and society-wide impact results. Exact labels and layout evolve — describe the flow by these concepts rather than memorized button text.
What PolicyEngine does
PolicyEngine computes the impact of tax and benefit policy on households and society. Users can calculate their own taxes and benefits, design a custom reform, see population-wide impacts, and share a reform by link. It is a nonpartisan nonprofit; the site is free.
Countries: United States (policyengine.org/us), United Kingdom (/uk), and Canada (/ca).
Household calculator
The household flow answers "how does this policy affect this household?"
- Describe the household. Enter the people (ages, marital status, dependents), their income by type (employment, self-employment, capital gains, pensions, benefits received), the location (US state or UK region, plus any local options the model supports), and any deductions/expenses the model uses (e.g. charitable giving, mortgage interest, SALT, medical).
- Read the results. Net income after taxes and benefits, broken into total tax, total benefits, and effective and marginal tax rates, with charts of net income and marginal rate across an earnings range.
- Apply a reform. Layer a policy reform on top to compare baseline vs. reform for the same household.
Policy editor
The policy flow answers "what does this reform do to the population?"
- Pick parameters to change. Browse the parameter tree by government department and program (e.g. IRS credits, SNAP, Universal Credit), select a parameter, and set a new value or schedule. Verify the parameter's current baseline before changing it — post-OBBBA US law differs from older summaries (e.g. the CTC baseline is $2,200 in 2026, not $2,000).
- Run the society-wide impact. The app computes the reform against microdata and returns:
- Budgetary impact — total cost or revenue, broken down by program.
- Poverty impact — change in poverty rate, by age group and for deep poverty.
- Distributional impact — average change and winners/losers by income decile.
- Inequality impact — Gini index and top-income shares.
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 · 110 lines · 103 tokens per session scan A a81c4738524a
policyengine-user-guide is a skill published in the GitHub repository PolicyEngine/policyengine-claude (31 stars, last pushed 7d ago), licensed MIT. It adds 103 tokens to every session and 1,370 once invoked, about $0.0005 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.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
agent-host-chat-contributions
Build and review cross-cutting agent-host chat behavior through lifecycle contributions. Use when adding turn lifecycle side effects, prompt or context injection, restored-history transformation, protocol-action observation, or when reviewing changes that add code to AgentSideEffects or AgentService.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.