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CC privacy-policy-stephane-boghossian

A zero-hallucination privacy-policy generator that takes anyone — non-lawyer founder to lawyer — from a guided intake to a publishable, jurisdiction-aware privacy policy. Jurisdiction-first: it detects which laws apply from where your users are, then drafts only the required clauses — GDPR/EU + UK, US (CCPA/CPRA, ~20 state laws, COPPA, sector overlays), and global/MENA (LGPD, Quebec Law 25, India DPDP, China PIPL, UAE/DIFC, Saudi PDPL), plus app-store, cookies, and AI/EU AI Act disclosures. Its rule: state only what you confirm; never invent a statute, citation, fine, or date — every claim is source-cited and QA-gated. Not legal advice.

lawve-ai/awesome-legal-skills Agent Skills author: lawve-ai 11 files body ≈ 1 870 tokens Open the sourcegithub.com↗ analyzed 90 min ago

A zero-hallucination privacy-policy generator that takes anyone — non-lawyer founder to lawyer — from a guided intake to a publishable, jurisdiction-aware…

As a process C 54/100 · Has gaps — weak spots: when it triggers, inputs and preconditions, failures and branches

GeneratorLegaltype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
C
89/100
safety, quality, tests
Safety 60%
100
Quality 40%
73
Run on models
none yet
Process rating
C
54/100
Has gaps
Inputs and preconditions w 11
0
Failures and branches w 10
0
When it triggers w 12
20
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
For the model run — optional
  • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
  • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

Guard findings · 0

✓ No critical or high findings

Files scanned: 11. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • note frontmatter-key unknown frontmatter key "keywords"
  • note frontmatter-key unknown frontmatter key "language"
  • note frontmatter-key unknown frontmatter key "triggers"

Process rating: all ten parameters 54/100

  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Failures and branches. Linear process with no failure handling
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 2 mutating operations with no state check
  • 60Tools and files. Uses tools (web) that frontmatter does not declare
  • 60Result and completion. Output format stated, no completion criterion
  • 100Steps. 17 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1870 tokens
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

Quality signals

  • +5Description has no quoted example phrases that should trigger the skill
  • +4Description does not say when NOT to use the skill (false activations)
  • +4No input/output examples
  • +2Single-language instructions
  • +3Description length 644: enough signal without eating the budget
  • +4Structure: 12 headings
  • +3Step-by-step instructions: 17 items
  • +3Output format is stated explicitly
  • +4Reference files are cited in the instructions (8 of 8)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 73.