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/text-to-reformgit 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/text-to-reform)<a href="https://agentmods.dev/commands/policyengine/policyengine-claude/text-to-reform"><img src="https://agentmods.dev/badge/commands/policyengine/policyengine-claude/text-to-reform.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 | $0.00031 | $0.00622 |
| Opus 5 | $0.00015 | $0.00311 |
| Sonnet 5 | $0.00006 | $0.00124 |
| Haiku 4.5 | $0.00003 | $0.00062 |
Grade A, and why
text-to-reform 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 6d 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 — 51 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Text to reform-dict
Standalone entry for bill → reform-dict translation. Runs the first two stages of /analyze-policy (understand + classify) and stops before the microsim.
When to use
- "I have a bill (or link, or description) — what parameters does it touch?"
- "Would this even be modelable in PolicyEngine, before I invest in scoring it?"
- "Can I get a validated reform-dict I can then submit to
api.policyengine.orgmyself?"
Do NOT use if you want the impact numbers — that's /analyze-policy end-to-end.
Arguments
Same argument shape as /analyze-policy:
- State + bill number:
UT SB60,RI H7127 - Federal bill:
US HR1234,US S5678 - URL: a bill / proposal / order URL
- Description:
"ARPA-style federal CTC expansion: $3,600 ages 0-5, $3,000 ages 6-17, fully refundable" - Preset:
preset:<name>— load frompresets/reforms/<name>.yaml
Flags:
--country {us|uk|ca}(defaultus)
What this command does
policy-text-researcherfetches the bill/URL/description and extracts structured provisions with baseline_value / reform_value pairs.parameter-locator(per provision, in parallel) maps each provision to PolicyEngine YAML paths. Runs the 5 pre-flight checks: master existence → deployed API existence → date coverage → formula liveness → reform-family toggles → per-year row coverage.reform-classifieraggregates the per-provision verdicts and returns one of:parametric— emits a full reform-dict + confidencedeployed-model-lag— parameter exists on master but not yet on deployed API; includes fallback advicestructural— needs model extension; emits parameter family + formula edit estimatenot-possible— outside PE's scope
Output
The final reform-dict (JSON) ready to submit to POST /{country}/policy on the PE API. Plus the pre-flight check log so the analyst can see what was verified.
If the verdict is structural or not-possible, the output is the classification rationale rather than a reform-dict.
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.
- 6d ago First seen · 51 lines · 31 tokens per session scan A 7d60f0f49a2f
text-to-reform is a command published in the GitHub repository PolicyEngine/policyengine-claude (32 stars, last pushed 4d ago), licensed MIT. It adds 31 tokens to every session and 622 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.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
constitution
Create or update the project constitution from interactive or provided principle inputs.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.