archiving-databases
Process use when you need to archive historical database records to reduce primary database size. This skill automates moving old data to archive tables or cold storage (S3, Azure Blob, GCS). Trigger with phrases like "archive old database records", "implement data retention policy", "move historical data to cold storage", or "reduce database size with archival".
68
85%
Does it follow best practices?
Impact
—
No eval scenarios have been run
Passed
No known issues
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 purpose, lists concrete actions and storage targets (S3, Azure Blob, GCS), and provides explicit trigger phrases. The description is well-scoped to a specific niche (database archival) making it highly distinguishable. The only minor issue is the slightly awkward phrasing at the start ('Process use when') which appears to be a grammatical error but doesn't significantly impact comprehension.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: archiving historical database records, moving old data to archive tables or cold storage (S3, Azure Blob, GCS), reducing primary database size, and implementing data retention policies. | 3 / 3 |
Completeness | Clearly answers both 'what' (automates moving old data to archive tables or cold storage) and 'when' (explicit trigger phrases and a 'Use when' equivalent at the start specifying the scenario of archiving historical database records). | 3 / 3 |
Trigger Term Quality | Includes highly natural trigger phrases users would say: 'archive old database records', 'implement data retention policy', 'move historical data to cold storage', 'reduce database size with archival'. These cover multiple natural variations of how a user might express this need. | 3 / 3 |
Distinctiveness Conflict Risk | Targets a very specific niche—database record archival and data retention—with distinct triggers like 'cold storage', 'archive tables', 'data retention policy' that are unlikely to conflict with general database or file management skills. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
70%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a highly actionable and well-structured skill with concrete SQL commands and clear workflow sequencing including validation checkpoints. Its main weakness is that it's a monolithic document covering too many concerns (database archival, cloud storage, compliance, retrieval, scheduling) without splitting content into referenced sub-files. Some verbosity in the examples and prerequisites sections could be trimmed.
Suggestions
Split cloud storage archival (step 6), retrieval procedures (step 9), and compliance/GDPR examples into separate referenced files to improve progressive disclosure and reduce the monolithic structure.
Trim the examples section to focus on concrete input/output patterns rather than narrative descriptions of hypothetical business outcomes (e.g., 'improves by 40%' is not actionable).
Remove prerequisite items that Claude already knows (e.g., what psql, aws s3, az storage CLIs are) and focus only on project-specific requirements like credentials and permissions.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is fairly comprehensive but includes some unnecessary verbosity. The examples section describes hypothetical scenarios with specific percentages and business outcomes that aren't actionable. The prerequisites section explains things Claude would know (like what CLI tools are). However, the core instructions are reasonably dense with useful SQL commands. | 2 / 3 |
Actionability | The skill provides concrete, executable SQL commands and shell commands throughout. Steps include specific queries for identifying candidates, creating archive tables, performing batch operations, and cloud storage uploads. The commands are copy-paste ready with clear placeholders. | 3 / 3 |
Workflow Clarity | The 10-step workflow is clearly sequenced with explicit validation checkpoints: verifying row counts match before deletion (step 5), wrapping in transactions for atomicity, checking for active foreign key references before archiving (step 3), and running VACUUM after completion (step 8). The error handling table provides clear feedback loops for recovery scenarios. | 3 / 3 |
Progressive Disclosure | The skill is a monolithic wall of text with no references to external files. At ~150+ lines covering archive table creation, cloud storage, batch processing, GDPR compliance, retrieval procedures, and cron scheduling, much of this content (cloud storage specifics, error handling, examples, retrieval procedures) should be split into separate referenced files for better organization. | 1 / 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 | |
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