Borrowing it
Nothing to install: this file belongs to dogwood-policy/dogwood. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/dogwood-policy/dogwood/main/.claude/skills/autoformalize-policies/SKILL.mdgit clone --depth 1 https://github.com/dogwood-policy/dogwoodWrote 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/skills/dogwood-policy/dogwood/autoformalize-policies)<a href="https://agentmods.dev/skills/dogwood-policy/dogwood/autoformalize-policies"><img src="https://agentmods.dev/badge/skills/dogwood-policy/dogwood/autoformalize-policies/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.
<a href="https://agentmods.dev/skills/dogwood-policy/dogwood/autoformalize-policies"><img src="https://agentmods.dev/badge/skills/dogwood-policy/dogwood/autoformalize-policies.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00140 | $0.04610 |
| Opus 5 | $0.00070 | $0.02305 |
| Sonnet 5 | $0.00028 | $0.00922 |
| Haiku 4.5 | $0.00014 | $0.00461 |
Grade A, and why
autoformalize-policies 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 12d 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 — 347 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Autoformalizing natural language into Dogwood policies
Your job: turn a natural-language authorization requirement into a Dogwood
.dw policy that parses, validates against a schema, and means what the user
actually intended. This is a formalization task — the hard part is not the
syntax (that is fully documented; see Ground truth)
but pinning down ambiguous intent and mapping it onto the right Dogwood
construct.
Do not guess at intent when a requirement is underspecified, and do not return a policy you have not validated. Follow the loop below in order:
- Disambiguate the requirement (resolve every gap that changes the output).
- Formalize it into a
.dwpolicy. - Validate it — run it through the
dogwoodCLI and fix until it is clean. This step is mandatory (see Step 3); a policy that has not been validated is not a finished answer. - Round-trip the intent and present the result.
The ground truth (read before authoring)
Dogwood's syntax and, crucially, its legality rules are precisely documented.
Treat these as authoritative; do not invent syntax from memory. These paths are
relative to this skill directory (.claude/skills/autoformalize-policies/); the
guide lives in the dogwood-docs crate:
../../../dogwood-docs/guide/02-policy-language.md— core policy syntax:permit/forbid, the(principal, action, resource)scope,when/unless, and the complete Cedar expression language (operators, literals, methods,has/like/is, sets/records, entity refs). 100% of the core syntax.../../../dogwood-docs/guide/04-temporal-expressions.md— thetemporal { … }sublanguage:formerly/previous/since, windows,exists/tp,count/sum, predicates, and the acceptance rules (range restriction, conjunct ordering, tp-dependence). Read this in full before writing any history-dependent policy.../../../dogwood-docs/guide/02-policy-language.md(the "action schema" section) — the action schema and thecontext.input/context.outputconvention.../../../dogwood-docs/guide/03-event-schema.md— the event-schema DSL and decision vs history event kinds (needed only when customizing the default).../../../dogwood-docs/guide/05-information-providers.md— computed facts viaProvider::Name(args)calls in an ordinarywhen { … }; see../../../dogwood-docs/guide/10-provider-schema.mdfor declaring providers (providers.json, the Rhai contract).../../../dogwood-docs/guide/09-calling-macros.md— calling macros; and../../../dogwood-docs/guide/06-macros.md— definingdef cedar/def temporal(rarely needed; reach for it only for a genuinely reusable pattern).
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.
- 12d ago First seen · 347 lines · 140 tokens per session scan A be3b7b4d670a
autoformalize-policies is a skill published in the GitHub repository dogwood-policy/dogwood (384 stars, last pushed today), licensed Apache-2.0. It adds 140 tokens to every session and 4,610 once invoked, about $0.0007 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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