granola-data-handling
Manage Granola data export, retention policies, GDPR/CCPA compliance, and archival workflows. Handle Subject Access Requests and Right to Erasure. Trigger: "granola export", "granola data", "granola GDPR", "granola retention", "granola delete data", "granola compliance".
84
82%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Advisory
Suggest reviewing before use
Quality
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 Granola-specific data management and compliance tasks. It lists concrete actions, includes explicit trigger terms, and is highly distinctive due to the product-specific focus. The description is concise yet comprehensive.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: data export, retention policies, GDPR/CCPA compliance, archival workflows, Subject Access Requests, and Right to Erasure. These are distinct, well-defined capabilities. | 3 / 3 |
Completeness | Clearly answers 'what' (manage data export, retention policies, GDPR/CCPA compliance, archival workflows, SARs, Right to Erasure) and 'when' via explicit trigger terms listed with a 'Trigger:' clause, which serves the same function as 'Use when...'. | 3 / 3 |
Trigger Term Quality | Includes explicit trigger terms like 'granola export', 'granola data', 'granola GDPR', 'granola retention', 'granola delete data', 'granola compliance' — these are natural phrases a user would say when needing this skill. Also includes domain terms like GDPR, CCPA, Subject Access Requests. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive due to the 'Granola' product specificity combined with the data governance/compliance niche. The trigger terms are all prefixed with 'granola', making conflicts with other skills very unlikely. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
64%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill provides genuinely useful, actionable guidance for Granola data handling with executable code examples and concrete procedures. Its main weaknesses are verbosity (explaining regulatory concepts Claude already knows, redundant tables) and lack of validation checkpoints in the export and archival workflows. The monolithic structure would benefit from splitting compliance, export, and archival into separate referenced files.
Suggestions
Add validation steps to the cache export script (check file exists, verify JSON parses, count expected vs actual exports) and the archival workflow (verify archive completeness before sending notification).
Split GDPR/CCPA compliance details, export procedures, and archival workflows into separate referenced files to improve progressive disclosure and reduce the main file's token footprint.
Remove explanatory content Claude already knows — e.g., the data types sensitivity descriptions, GDPR article explanations — and focus on Granola-specific implementation details only.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill contains useful information but is verbose in places — the data types table in Step 1 explains things Claude likely knows (what transcripts and calendar metadata are), and the GDPR rights table restates well-known regulation text. The 'Key fact' callout about audio deletion is genuinely useful, but overall the document could be tightened significantly. | 2 / 3 |
Actionability | The skill provides fully executable Python scripts for local cache export, concrete bash commands for API access and backups, specific file paths, and step-by-step procedures for SAR handling. The code examples are copy-paste ready with real paths and realistic data structures. | 3 / 3 |
Workflow Clarity | Steps are clearly sequenced and the SAR procedure includes a good checklist. However, the local cache export script lacks validation — there's no check that the cache file exists, no verification that exported files are valid/complete, and no error handling for malformed JSON. The archival workflow is described in YAML pseudocode rather than executable automation, and there's no feedback loop for verifying archive completeness. | 2 / 3 |
Progressive Disclosure | The content is a monolithic document with all details inline — the GDPR tables, CCPA details, archival workflows, and export scripts could be split into separate referenced files. The Resources section links to external docs, and there's a 'Next Steps' reference, but the body itself is ~180 lines of dense content that would benefit from being split into focused sub-documents. | 2 / 3 |
Total | 9 / 12 Passed |
Validation
81%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 9 / 11 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
allowed_tools_field | 'allowed-tools' contains unusual tool name(s) | Warning |
frontmatter_unknown_keys | Unknown frontmatter key(s) found; consider removing or moving to metadata | Warning |
Total | 9 / 11 Passed | |
- Commit
3a2d27d
- Reviewed
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