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gdpr-data-handling

Implement GDPR-compliant data handling with consent management, data subject rights, and privacy by design. Use when building systems that process EU personal data, implementing privacy controls, or conducting GDPR compliance reviews.

87

1.25x
Quality

Does it follow best practices?

Impact

98%

1.25x

Average score across 3 eval scenarios

SecuritybySnyk

Passed

No known issues

SKILL.md
Quality
Evals
Security

Quality

Content

65%

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The skill delivers highly actionable, executable code across the core GDPR patterns, but it is monolithic and lacks sequenced workflows with validation checkpoints for its destructive operations. Splitting detailed patterns into reference files and adding explicit feedback loops would raise the weaker dimensions.

Suggestions

Add explicit validation/retry checkpoints to destructive and batch workflows (erasure, retention enforcement, breach notification) — e.g., verify identity before erasure, confirm downstream propagation, and re-check status before closing a breach.

Move the full implementation patterns into separate reference files (e.g., references/consent.md, references/dsar.md) and keep SKILL.md as a concise overview with one-level-deep, clearly signaled links.

Tighten narrative sections and trim the inline opening paragraph to reduce token usage where the code already conveys the guidance.

DimensionReasoningScore

Conciseness

Mostly efficient with substantial, mostly-earning code, but the body runs ~625 lines with minor narrative padding ('Practical implementation guide for...') and some sections that could be tightened.

2 / 3

Actionability

Provides fully executable, copy-paste-ready code (JS consent manager, Python DSAR/retention/breach handlers, HTML consent UI) with concrete schemas and real class implementations.

3 / 3

Workflow Clarity

Patterns are presented as discrete modules rather than sequenced workflows, and destructive/batch operations (erasure, breach notification) lack explicit validate->fix->retry feedback checkpoints, capping this dimension at 2.

2 / 3

Progressive Disclosure

Content is a single monolithic SKILL.md with no bundle files or one-level-deep references; section organization is reasonable but material that could be split (e.g., the full implementation patterns) is inline.

2 / 3

Total

9

/

12

Passed

Description

100%

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A strong, third-person description that states concrete capabilities and pairs them with explicit, natural trigger phrases. It answers both what and when without fluff or over-claims.

DimensionReasoningScore

Specificity

Lists multiple concrete actions — 'consent management, data subject rights, and privacy by design' — rather than vague language.

3 / 3

Completeness

Explicitly answers both what ('Implement GDPR-compliant data handling with consent management, data subject rights, and privacy by design') and when via an explicit 'Use when...' clause with multiple triggers.

3 / 3

Trigger Term Quality

Covers natural terms a user would say: 'GDPR-compliant', 'consent management', 'data subject rights', 'privacy by design', 'GDPR compliance reviews'.

3 / 3

Distinctiveness Conflict Risk

Clear GDPR/EU personal data niche with distinct triggers, unlikely to fire for unrelated skills.

3 / 3

Total

12

/

12

Passed

Validation

93%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

skill_md_line_count

SKILL.md is long (631 lines); consider splitting into references/ and linking

Warning

Total

15

/

16

Passed

Repository
Dicklesworthstone/pi_agent_rust
Reviewed

Table of Contents

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