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.
80
71%
Does it follow best practices?
Impact
98%
1.25xAverage score across 3 eval scenarios
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./tests/ext_conformance/artifacts/agents-wshobson/hr-legal-compliance/skills/gdpr-data-handling/SKILL.mdQuality
Discovery
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.
This is a strong skill description that clearly defines its scope around GDPR compliance, lists concrete capabilities, and includes an explicit 'Use when' clause with natural trigger terms. It uses proper third-person voice and covers both the what and when dimensions effectively. The description is concise yet comprehensive, making it easy for Claude to distinguish this skill from others.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: 'consent management', 'data subject rights', 'privacy by design', 'implementing privacy controls', and 'conducting GDPR compliance reviews'. These are distinct, identifiable capabilities. | 3 / 3 |
Completeness | Clearly answers both 'what' (GDPR-compliant data handling with consent management, data subject rights, privacy by design) and 'when' (explicit 'Use when' clause covering building systems processing EU personal data, implementing privacy controls, or conducting GDPR compliance reviews). | 3 / 3 |
Trigger Term Quality | Includes strong natural keywords users would say: 'GDPR', 'consent management', 'data subject rights', 'privacy by design', 'EU personal data', 'privacy controls', 'compliance reviews'. These cover the main terms someone working on GDPR compliance would naturally use. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive with a clear niche around GDPR specifically. The combination of 'GDPR', 'EU personal data', 'consent management', and 'data subject rights' creates a very specific domain that is unlikely to conflict with other skills. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
42%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The skill provides highly actionable, executable code patterns covering major GDPR implementation concerns, which is its primary strength. However, it is severely bloated—explaining GDPR concepts Claude already knows, inlining hundreds of lines of code that should be in referenced files, and lacking an overarching workflow that ties the patterns together with validation checkpoints. The content would benefit enormously from restructuring into a lean overview with references to detailed pattern files.
Suggestions
Remove the 'Core Concepts' section entirely (personal data categories, legal bases, data subject rights)—Claude already knows GDPR fundamentals. Replace with a one-line reminder of key constraints like the 30-day DSAR deadline and 72-hour breach notification.
Extract each implementation pattern into its own referenced file (e.g., CONSENT.md, DSAR.md, RETENTION.md, BREACH.md) and keep SKILL.md as a concise overview with brief descriptions and links.
Add an overarching workflow section that sequences when to apply each pattern (e.g., 'When building a new feature: 1. Identify legal basis → 2. Apply data minimization → 3. Implement consent if needed → 4. Verify with checklist') with explicit validation checkpoints.
Add validation steps to destructive operations like erasure—e.g., verify identity before processing, dry-run deletion to preview affected records, confirm completion with audit log check.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Extremely verbose at ~400+ lines. Extensively explains GDPR concepts Claude already knows (legal bases, data subject rights, personal data categories). The tables and ASCII trees for Articles 6, 9, 10, 15-21 are reference material Claude has in training data. The code examples are exhaustive where patterns would suffice. | 1 / 3 |
Actionability | Provides fully executable code examples across multiple patterns: consent management (JS + HTML), DSAR handling (Python), data retention (Python), privacy-by-design models, and breach notification. Code is concrete, copy-paste ready, and covers real implementation scenarios. | 3 / 3 |
Workflow Clarity | Individual patterns are well-structured with clear class methods and logical flows. However, there's no overarching workflow connecting the patterns (e.g., when to use which pattern), and validation/verification steps are largely absent. The DSAR and breach handlers lack explicit validation checkpoints for destructive operations like erasure. | 2 / 3 |
Progressive Disclosure | Monolithic wall of content with everything inline. Five large code patterns, a full compliance checklist, best practices, and external resources are all in a single file. The code examples for consent, DSAR, retention, privacy-by-design, and breach notification should each be in separate referenced files, with SKILL.md serving as a concise overview. | 1 / 3 |
Total | 7 / 12 Passed |
Validation
90%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 10 / 11 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
skill_md_line_count | SKILL.md is long (631 lines); consider splitting into references/ and linking | Warning |
Total | 10 / 11 Passed | |
- Repository
- Dicklesworthstone/pi_agent_rust
- Commit
47823e3
- Reviewed
Table of Contents
Is this your skill?
If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.