veto-policy-runtime

name	veto-policy-runtime
description	Create and apply new Veto policies safely for AI agents using deterministic rules first, with optional LLM-assisted generation when needed. Use this when you must add guardrails without editing or deleting existing policies.
license	Apache-2.0
compatibility	Requires `veto-cli` and a project-local `veto/` config directory.

Veto Policy Runtime

This skill makes Veto policy authoring agent-native and safe-by-default.

Use This Skill When

• You need to add a new policy for one or more tools.
• You need deterministic enforcement in local runtime.
• You want optional LLM-assisted policy generation with CLI commands.
• You must avoid destructive policy operations.

Hard Safety Rules

1. Never delete policy files.
2. Never modify existing policy files in place.
3. Create new policy files under veto/rules/ only.
4. Do not call delete/update policy APIs in cloud workflows.
5. If asked to "change" a policy, create a new superseding policy file and explain precedence.

Default Workflow (Create-Only)

1. Ensure project is initialized:

npx -y veto-cli@latest init

2. Generate policy from natural language (deterministic bias):

npx -y veto-cli@latest policy generate \
  --tool approve_invoice \
  --prompt "do not approve invoices above 50 dollars" \
  --mode-hint deterministic \
  --save ./veto/rules/approve-invoice-over-50.generated.yaml \
  --json

Keyless local generation tries configured cloud/self-hosted/kernel endpoints first, then uses local deterministic template fallback with warnings. In fallback, no prompt or policy data leaves the machine. Use --no-template-fallback if strict endpoint-only behavior is required.

3. If semantics are too broad for strict constraints, use LLM generation explicitly:

npx -y veto-cli@latest policy generate \
  --tool outbound_email \
  --prompt "deny sending customer data outside approved domains" \
  --mode-hint llm \
  --save ./veto/rules/outbound-email-sensitive.generated.yaml \
  --json

4. Apply generated file (local deterministic runtime):

npx -y veto-cli@latest policy apply \
  --file ./veto/rules/approve-invoice-over-50.generated.yaml \
  --target local \
  --json

5. Validate behavior with explicit test calls:

npx -y veto-cli@latest guard check \
  --tool approve_invoice \
  --args '{"amount":40}' \
  --mode local \
  --json

npx -y veto-cli@latest guard check \
  --tool approve_invoice \
  --args '{"amount":75}' \
  --mode local \
  --json

Deterministic-First Decision Rule

• Use deterministic when policy can be expressed as thresholds, enums, allowlists, deny-lists, ranges, presence checks, or regex-like constraints.
• Use llm only for semantic intent that cannot be captured reliably with deterministic constraints.

Cloud Workflow (Create Drafts Only)

Use cloud target for draft creation and review flows without destructive changes:

npx -y veto-cli@latest policy generate \
  --tool approve_invoice \
  --prompt "require approval for invoices over 1000" \
  --target cloud \
  --mode-hint deterministic \
  --json

npx -y veto-cli@latest policy apply \
  --file ./veto/rules/approve-invoice-over-1000.generated.yaml \
  --target cloud \
  --json

Runtime Integration (TypeScript)

import { Veto } from "veto-sdk";

const veto = await Veto.init();
const guardedTools = veto.wrap(tools);

Runtime Integration (Python)

from veto import Veto

veto = await Veto.init()
guarded_tools = veto.wrap(tools)

Output Contract For Agents

When this skill runs successfully, return:

1. Created policy file path(s)
2. Tool names covered
3. guard check results for allow and deny scenarios
4. Whether deterministic or llm mode was used
5. Any warnings from CLI generation

References

• references/headless-command-recipes.md
• references/policy-patterns.md