sql-queries

Generate SQL queries from natural language descriptions. Supports BigQuery, PostgreSQL, MySQL, and other dialects. Reads database schemas from uploaded diagrams or documentation. Use when writing SQL, building data reports, exploring databases, or translating business questions into queries.

Quality

65%

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tessl review fix ./pm-data-analytics/skills/sql-queries/SKILL.md

Quality

Content

46%Weight 40%Scale 1-5

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

The body is well-structured with a clear step sequence, but it lacks concrete SQL query examples and explicit validation feedback loops, and several sections restate each other. Actionability and workflow validation are the main weaknesses.

Suggestions

Add at least one concrete example SQL query output (e.g., a SELECT with a JOIN and date filter) alongside the prompt-only Usage Examples so Claude has an executable template to follow.

Insert an explicit validation checkpoint in the workflow, such as 'Run EXPLAIN / a row-count check on a sample; if it fails or returns unexpected rows, revise and re-test' to create a validate-fix-retry feedback loop.

Remove the redundant 'Key Capabilities' and 'Output Format' sections (they restate the steps) and drop the off-topic 'Further Reading' PM article links to tighten token usage.

Dimension	Reasoning	Score
Conciseness	The step bullets are mostly efficient, but 'Key Capabilities', 'Tips for Best Results', and 'Output Format' largely restate the steps, and the 'Further Reading' links to off-topic PM analytics articles, so it could be tightened rather than reaching the lean 4-5 range.	3 / 5
Actionability	Guidance is high-level ('Write efficient SQL', 'Include comments explaining complex logic') with no actual SQL query examples or templates, and the 'Usage Examples' show user prompts rather than query outputs, leaving the specific executable detail missing for a code-output skill.	2 / 5
Workflow Clarity	A clear 4-step sequence is present and Step 4 mentions testing, but there is no explicit validate-then-fix-then-retry checkpoint or feedback loop, so checkpoints remain implicit rather than reaching the explicit-validation 4-5 anchors.	3 / 5
Progressive Disclosure	The single file is well-sectioned (Purpose, How It Works, Usage Examples, Key Capabilities, Tips, Output Format) with no bundle files needed at this size and clearly signaled external links, though minor redundancy keeps it below the cleanly-organized 5.	4 / 5
	Total	12 / 20 Passed

Description

83%Weight 40%Scale 1-5

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

The description is strong: it states concrete capabilities, names supported dialects, and provides an explicit 'Use when' trigger clause with natural phrasing. It is comprehensive on completeness and only slightly short of the top anchor on specificity, trigger coverage, and distinctiveness.

Dimension	Reasoning	Score
Specificity	Names the domain and several concrete actions ('Generate SQL queries from natural language descriptions', 'Supports BigQuery, PostgreSQL, MySQL', 'Reads database schemas from uploaded diagrams or documentation') with only minor coverage gaps, fitting the 'several specific actions' anchor rather than the fully comprehensive 5.	4 / 5
Completeness	Explicitly answers both 'what' (generate SQL queries across dialects, read schemas) and 'when' via a concrete 'Use when writing SQL, building data reports, exploring databases, or translating business questions into queries' clause, matching the top anchor.	5 / 5
Trigger Term Quality	Good natural-term coverage including synonyms ('writing SQL', 'building data reports', 'exploring databases', 'translating business questions into queries') plus dialect names, but a few common terms like '.sql' file extensions are absent, placing it just below the comprehensive 5.	4 / 5
Distinctiveness Conflict Risk	SQL query generation is a clear niche with dialect-specific triggers and low conflict risk, though it could marginally overlap with general data-analysis skills, keeping it just below the minimal-conflict 5.	4 / 5
	Total	17 / 20 Passed

Validation

100%

Warnings & errors only

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

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository: phuryn/pm-skills
Path: pm-data-analytics/skills/sql-queries/SKILL.md
Commit: 18468a9

Reviewed: 3 days ago

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