tietosuoja-arviointi

tietosuoja-arviointi is a skill for Claude Code, Codex from akunikkola/claude-for-legal-finland. It costs 202 tokens per session (2,069 once invoked), scanned A, original, MIT.

A privacy-assessment skill for personal-data processing under the GDPR and Finnish data-protection law. It reviews the legal basis, processing principles, and whether a Data Protection Impact Assessment, or DPIA, is needed.

In plain words
What is it for?
Use it to assess planned personal-data processing, check its legal basis and principles, and decide whether a DPIA is required.
Why use it?
It helps uncover privacy risks before a new system, service, or data process is introduced. It also highlights cases that need review by a data-protection officer or lawyer.

Skill for Claude CodeCodex

Written for Claude Code and Codex: shipped in a Claude Code plugin, but also agents/openai.yaml present. Also seen: mentions CLAUDE.md.

Part of the tietosuoja plugin — 3 skills shipped together

Good fit Use it to assess planned personal-data processing, check its legal basis and principles, and decide whether a DPIA is required.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/akunikkola/claude-for-legal-finland/tietosuoja-arviointi
Install

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.

Any agent
npx skills add akunikkola/claude-for-legal-finland --skill tietosuoja-arviointi
Clone the repo
git clone --depth 1 https://github.com/akunikkola/claude-for-legal-finland

Made for: Claude Code, Codex.

Or install tietosuoja, the plugin that ships this one along with the rest of its 3 skills.

Wrote 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.

agentmods badge for tietosuoja-arviointi

README.md
[![agentmods](https://agentmods.dev/badge/skills/akunikkola/claude-for-legal-finland/tietosuoja-arviointi/github.svg)](https://agentmods.dev/skills/akunikkola/claude-for-legal-finland/tietosuoja-arviointi)
Your own site
<a href="https://agentmods.dev/skills/akunikkola/claude-for-legal-finland/tietosuoja-arviointi"><img src="https://agentmods.dev/badge/skills/akunikkola/claude-for-legal-finland/tietosuoja-arviointi/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.

agentmods 80×15 button for tietosuoja-arviointi

Your own site · 80×15
<a href="https://agentmods.dev/skills/akunikkola/claude-for-legal-finland/tietosuoja-arviointi"><img src="https://agentmods.dev/badge/skills/akunikkola/claude-for-legal-finland/tietosuoja-arviointi.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 202 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,069 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00202 $0.02069
Opus 5 $0.00101 $0.01035
Sonnet 5 $0.00040 $0.00414
Haiku 4.5 $0.00020 $0.00207

Measured 9d ago against content hash 25093bc4cedf, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

tietosuoja-arviointi 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 9d 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.

tietosuoja/skills/tietosuoja-arviointi/SKILL.md · 92 lines

How it starts

The opening of the file, as written. The whole thing — 92 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Tietosuoja-arviointi — käsittelyperuste ja DPIA

Tämä skill arvioi henkilötietojen käsittelyn tietosuojavaatimukset: onko käsittelylle laillinen peruste, noudatetaanko käsittelyn periaatteita ja tarvitaanko vaikutustenarviointi. Sovellettava sääntely on EU:n yleinen tietosuoja-asetus (EU) 2016/679 (GDPR), jota tietosuojalaki (1050/2018) täsmentää ja täydentää kansallisesti. Työelämän käsittelyyn sovelletaan lisäksi lakia yksityisyyden suojasta työelämässä (759/2004).

Vastuuvapaus: tämä on tarkistettava arvio, ei oikeudellista neuvontaa. Korkean riskin käsittely, arkaluonteiset tiedot ja DPIA:t kuuluvat tietosuojavastaavan ja tarvittaessa juristin arvioitaviksi. Katso tietosuoja/CLAUDE.md.

Perusteet ja artiklat tiivistettynä: lue references/tietosuoja-perusteet.md. Tarkista kansallisen lain pykälät juristi-plugarin oikeustutkimus-skillillä; GDPR-artiklat EUR-Lexistä.


Vaihe 1: Kuvaa käsittely

Selvitä:

  1. Mitä henkilötietoja käsitellään ja keneltä (rekisteröityjen ryhmät)?
  2. Mihin tarkoitukseen ja mikä on käsittelyn konteksti?
  3. Kuka on rekisterinpitäjä ja onko käsittelijöitä (alihankkijat, pilvipalvelut)?
  4. Siirretäänkö tietoja EU/ETA:n ulkopuolelle?
  5. Onko kyse arkaluonteisista (erityisistä) tiedoista (terveys, etninen alkuperä, vakaumus, ay-jäsenyys, biometriset/geneettiset tiedot, seksuaalinen suuntautuminen) tai rikostiedoista?

Vaihe 2: Määritä käsittelyperuste (GDPR 6 art)

Jokaisella käsittelyllä on oltava vähintään yksi peruste:

  • Suostumus (6(1)(a)) — vapaaehtoinen, yksilöity, tietoinen, peruutettavissa. Heikko peruste työsuhteessa (epätasapaino).
  • Sopimus (6(1)(b)) — käsittely tarpeen sopimuksen täyttämiseksi.
  • Lakisääteinen velvoite (6(1)(c)).
  • Elintärkeä etu (6(1)(d)).
  • Yleinen etu / julkinen valta (6(1)(e)) — viranomaistoiminta.
  • Oikeutettu etu (6(1)(f)) — edellyttää tasapainotestiä; ei sovellu viranomaisen tehtävien hoitoon.

Arkaluonteiset tiedot (9 art): käsittely lähtökohtaisesti kielletty, ellei jokin 9(2) poikkeus sovellu (esim. nimenomainen suostumus, työoikeuden velvoitteet, tärkeä yleinen etu). Tarkista myös tietosuojalain (1050/2018) kansalliset täsmennykset oikeustutkimus-skillillä.

Read the full file on GitHub · 92 lines

Files

What ships with it

2 files 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.

Changes

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.

  1. 9d ago First seen · 92 lines · 202 tokens per session scan A 25093bc4cedf

Subscribe to this mod's changes

tietosuoja-arviointi is a skill published in the GitHub repository akunikkola/claude-for-legal-finland (103 stars, last pushed 2mo ago), licensed MIT. It adds 202 tokens to every session and 2,069 once invoked, about $0.0010 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 2026-09-03.

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