aws-aurora
AWS Aurora Serverless v2, RDS Proxy, Data API, connection pooling
35
32%
Does it follow best practices?
Impact
—
No eval scenarios have been run
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./skills/aws-aurora/SKILL.mdQuality
Discovery
22%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 description is essentially a keyword list of AWS technologies with no verbs, actions, or usage guidance. It fails to communicate what the skill actually does or when it should be selected. While the specific AWS service names provide some natural trigger terms, the lack of any functional description or 'Use when...' clause severely limits its effectiveness for skill selection.
Suggestions
Add concrete action verbs describing what the skill does, e.g., 'Configures and troubleshoots AWS Aurora Serverless v2 clusters, sets up RDS Proxy for connection management, and implements Data API access patterns.'
Add an explicit 'Use when...' clause, e.g., 'Use when the user needs help with Aurora Serverless v2 setup, RDS Proxy configuration, Data API queries, or database connection pooling on AWS.'
Include natural language variations users might say, such as 'serverless database', 'database scaling', 'AWS database connections', or 'reduce database connection overhead'.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description is a comma-separated list of AWS technology names with no concrete actions described. It doesn't say what the skill does—no verbs like 'configure', 'deploy', 'troubleshoot', or 'optimize' are present. | 1 / 3 |
Completeness | The description answers neither 'what does this do' nor 'when should Claude use it'. It is merely a list of technology terms with no explanation of capabilities and no 'Use when...' clause or equivalent trigger guidance. | 1 / 3 |
Trigger Term Quality | It includes relevant technical keywords like 'Aurora Serverless v2', 'RDS Proxy', 'Data API', and 'connection pooling' that users might mention. However, it misses common variations and natural phrases like 'database scaling', 'serverless database', 'AWS database setup', or 'database connections'. | 2 / 3 |
Distinctiveness Conflict Risk | The specific AWS service names (Aurora Serverless v2, RDS Proxy, Data API) provide some distinctiveness, but without actions or context, it could overlap with broader AWS or database skills. The term 'connection pooling' is generic enough to conflict with non-AWS database skills. | 2 / 3 |
Total | 6 / 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.
This skill is highly actionable with excellent, executable code examples across multiple languages and deployment patterns. However, it is severely over-long and monolithic—it tries to be a comprehensive Aurora reference rather than a focused skill document. The content would benefit enormously from splitting into focused sub-files and trimming explanations of concepts Claude already knows (basic library usage, what Aurora is, standard SQL patterns).
Suggestions
Split into sub-files: move Data API (Python/TS), Prisma setup, PgBouncer config, CLI reference, and monitoring into separate referenced files, keeping SKILL.md as a concise overview with connection strategy decision guidance.
Remove explanations of concepts Claude already knows: the Aurora definition sentence, basic pg Pool usage patterns, standard Prisma schema setup, and docker-compose syntax are all well-known and waste tokens.
Add explicit validation checkpoints to the migration workflow (e.g., verify migration status after deploy, check for failed migrations, rollback procedure).
Trim duplicate patterns—the Data API is shown in both TypeScript and Python with full transaction examples; pick one primary language or move the second to a reference file.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is extremely verbose at ~400+ lines, covering many topics Claude already knows well (pg Pool usage, basic Prisma setup, boto3 client creation, docker-compose syntax, basic SQL). The introductory sentence explaining what Aurora is wastes tokens. Multiple complete code examples for similar patterns (TypeScript and Python Data API) add bulk without proportional value. | 1 / 3 |
Actionability | The skill provides fully executable, copy-paste ready code examples across multiple languages and tools (CDK, TypeScript with pg, Python with boto3, Prisma, CLI commands). Connection strings, environment variables, and configuration values are all concrete and specific. | 3 / 3 |
Workflow Clarity | Connection strategies are clearly laid out with decision guidance, and the migration workflow has sequenced steps. However, there are no explicit validation checkpoints for destructive operations like migrations or cluster deletion, and the migration Lambda lacks error recovery/rollback guidance beyond a basic catch-and-rethrow. | 2 / 3 |
Progressive Disclosure | The entire skill is a monolithic wall of content with no references to supporting files. Topics like Prisma setup, Data API Python examples, PgBouncer configuration, and CLI reference could easily be split into separate files. Everything is inline, making the skill far too long for a single SKILL.md. | 1 / 3 |
Total | 7 / 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 |
|---|---|---|
skill_md_line_count | SKILL.md is long (645 lines); consider splitting into references/ and linking | Warning |
frontmatter_unknown_keys | Unknown frontmatter key(s) found; consider removing or moving to metadata | Warning |
Total | 9 / 11 Passed | |
- Repository
- alinaqi/claude-bootstrap
- Commit
65efb33
- Reviewed
Table of Contents
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