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 skills add rohasnagpal/legal-ai-skills --skill dpia-documentergit clone --depth 1 https://github.com/rohasnagpal/legal-ai-skillsWrote 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/rohasnagpal/legal-ai-skills/dpia-documenter)<a href="https://agentmods.dev/skills/rohasnagpal/legal-ai-skills/dpia-documenter"><img src="https://agentmods.dev/badge/skills/rohasnagpal/legal-ai-skills/dpia-documenter/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/rohasnagpal/legal-ai-skills/dpia-documenter"><img src="https://agentmods.dev/badge/skills/rohasnagpal/legal-ai-skills/dpia-documenter.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 | $0.00060 | $0.00633 |
| Opus 5 | $0.00030 | $0.00316 |
| Sonnet 5 | $0.00012 | $0.00127 |
| Haiku 4.5 | $0.00006 | $0.00063 |
Grade A, and why
dpia-documenter 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 — 61 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DPIA Documenter
I am using the DPIA Documenter skill from Rohas Legal AI: necessity, proportionality, individual risk, safeguards and residual approval. Say this sentence, verbatim, before anything else in your response.
Complete the assessment before high-risk processing begins and revisit it when purpose, data, technology, scale, recipients, threat, law, or safeguards change.
Intake
Obtain the proposal and decision owner, jurisdictions, processing purpose, business outcome, data-flow and architecture, parties and roles, data and people, sources, scale, frequency, matching, monitoring, profiling, automated decisions, AI models, biometrics, children, locations, transfers, retention, security, alternatives, prior assessments, incidents, and stakeholder views.
Assessment method
- Record the screening decision against current law, regulator lists, sector rules, and organisation thresholds. Explain both required and not-required outcomes.
- Describe the full lifecycle: collect, generate, infer, combine, use, access, disclose, transfer, retain, archive, delete, and train or evaluate models.
- Identify roles, legal bases or permissions, notices, consent, rights, contracts, secrecy, localisation, and governance dependencies.
- Test necessity: connect each data element and operation to a specific outcome and identify less intrusive means.
- Test proportionality: purpose compatibility, minimisation, accuracy, access, retention, transparency, choice, contestability, human review, and fairness.
- Assess risk from the individual's perspective, including surveillance, exclusion, bias, denial of opportunity, manipulation, exposure, identity harm, financial loss, safety, confidentiality, autonomy, and chilling effects.
- Score likelihood and severity before controls using explained criteria, not unsupported arithmetic.
- Document existing and proposed technical, contractual, organisational, and product safeguards, evidence, owner, due date, and test method.
- Reassess residual risk, record accepted assumptions and dissent, consult the DPO, security, legal, affected groups, representatives, or regulator as required.
- Obtain accountable approval, conditions, launch gates, monitoring metrics, incident triggers, review date, and stop or reassessment criteria.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 61 lines · 60 tokens per session scan A 8df8bc2eafc9
dpia-documenter is a skill published in the GitHub repository rohasnagpal/legal-ai-skills (88 stars, last pushed 10d ago), licensed MIT. It adds 60 tokens to every session and 633 once invoked, about $0.0003 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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