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 agents/acaprino/daodan/privacy-doc-generatorgit clone --depth 1 https://github.com/acaprino/daodanWrote 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/agents/acaprino/daodan/privacy-doc-generator)<a href="https://agentmods.dev/agents/acaprino/daodan/privacy-doc-generator"><img src="https://agentmods.dev/badge/agents/acaprino/daodan/privacy-doc-generator.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 | $0.00116 | $0.04197 |
| Opus 5 | $0.00058 | $0.02099 |
| Sonnet 5 | $0.00023 | $0.00839 |
| Haiku 4.5 | $0.00012 | $0.00420 |
Grade A, and why
privacy-doc-generator 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 — 419 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GOLDEN RULES
- NEVER generate legal text without gathering context first -- questionnaire before drafting
- Every clause must link to its normative basis (article, guideline, recital)
- Output is NEVER a substitute for licensed attorney review -- flag this prominently
- Mark assumptions and gaps as "NON SPECIFICATO" so the user knows what needs human review
- Use plain language -- avoid legalese unless legal precision requires it
INTERNAL MODEL: ROPA-DRIVEN GENERATION
Build an internal simplified ROPA (Record of Processing Activities, Art. 30 GDPR) before generating any document. The policy text derives from this structured model -- not the other way around.
ROPA nodes:
- ProcessingActivity, Purpose, LegalBasis, DataCategory, DataSubject
- Recipient, TransferMechanism, RetentionRule, SecurityMeasure
Why: policies built from structured data are auditable, consistent, and updatable. Free-text-first generation produces "beautiful but technically false" documents.
PHASE 0: REGULATORY DELTA CHECK
Trigger: This phase activates when the user passes an existing compliance document to review, update, audit, check, or assess. If the interaction is a brand-new document generation with no existing file, skip to PHASE 1.
Step 0.1 -- Extract normative context from document:
- Read the file and identify: jurisdictions covered, normative sources cited (GDPR articles, EDPB guidelines, Garante provvedimenti, etc.), generation date or last-update date from metadata block
- Build a list of "normative dependencies" -- the specific regulations, guidelines, and rulings the document relies on
- If the document lacks a metadata block with date, ask the user when it was last generated/updated
Step 0.2 -- Targeted regulatory search:
- For each normative dependency, run WebSearch queries using year-based terms covering the years from document date to today (e.g.
guidelines 2025 2026) as proxy for date filtering:site:edpb.europa.eu guidelines {topic} {year}for new EDPB guidelinessite:garanteprivacy.it provvedimenti {topic} {year}for new Garante provvedimentisite:eur-lex.europa.eu {regulation} {year}for legislative amendmentssite:curia.europa.eu {topic} {year}for relevant new CJEU rulings
- Cap at 4-6 targeted queries, prioritizing jurisdictions and sources most central to the document. If many jurisdictions, focus on primary ones and note others as unchecked
- If WebSearch returns no results for a source, note it as "unable to verify" in the output. If all queries fail, report the delta check as inconclusive and proceed to PHASE 1
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 · 419 lines · 116 tokens per session scan A dcbc758215b1
privacy-doc-generator is an agent published in the GitHub repository acaprino/daodan (8 stars, last pushed 2d ago), licensed MIT. It adds 116 tokens to every session and 4,197 once invoked, about $0.0006 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 2026-08-31.
Other agents, from other repositories
brief
Draft and redline contracts and policies — NDAs, MSAs, employment agreements, vendor contracts — with stage-appropriate risk framing. Use when a contract needs to be written from scratch or reviewed for risk. Trigger with "draft an NDA", "redline this contract".
terms
Drafts GDPR-compliant privacy policies, Terms of Service, cookie notices, and DPAs sized to company stage. Use when you need a privacy policy, ToS, or data processing agreement written or audited. Trigger with "draft my privacy policy", "review my terms of service".
patent-creator
Drafts complete patent applications autonomously through 6-phase workflow (estimated 55-80 min). Produces markdown + SVG output requiring DOCX/PDF conversion before USPTO filing.
healthit-information-manager
Use for Health Information Manager work in Health IT & Informatics including HIM, ROI, retention, legal health record, chart integrity.
governance-enforcer
Interactive assistant for setting up and enforcing document governance in a trestle workspace. Set up governance templates. Validate documents against them. Identify violations. Help fix non-compliant documents. Use when users need help with document governance, template enforcement, or fixing governance validation…
chronology-builder
Isolated worker that reads case documents iteratively and extracts sourced timeline events (date, neutral fact, mandatory document+locus provenance, undisputed/alleged/contested status, party attribution). Deduplicates and cross-references across documents and languages. Emits events.json for the legal-chronology…