usage

Wren Engine CLI workflow guide for AI agents. Answer data questions end-to-end using the wren CLI: gather schema context, recall past queries, write SQL through the MDL semantic layer, execute, and learn from confirmed results. Use when: user asks a data question, requests a report or analysis, asks about metrics, revenue, customers, orders, trends, or any business data; user says 'how many', 'show me', 'what is the', 'top N', 'compare', 'trend', 'growth', 'breakdown'; user wants to explore, analyze, filter, aggregate, or summarize data from a database; agent needs to query data, connect a data source, handle errors, or manage MDL changes via the wren CLI.

Quality

91%

Does it follow best practices?

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Securityby

Passed

No findings from the security scan

Quality

Content

92%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.

A tightly structured, highly actionable agent-workflow guide with sequenced workflows, validation feedback loops, and clean progressive disclosure to real reference files. Only minor conciseness trimming would lift it further.

Dimension	Reasoning	Score
Conciseness	Mostly lean and operational — decision tables, command snippets, and terse rules — with little padding of concepts Claude already knows; a few explanatory asides (e.g., the venv rationale, the CJK token aside belong in references) keep it just below 5.	4 / 5
Actionability	Copy-paste-ready wren CLI commands throughout (memory fetch/recall/store, dry-plan, --sql, cube query with both flag and JSON forms), with concrete syntax tables mapping user phrases to flags; specific examples cover the common cases.	5 / 5
Workflow Clarity	Numbered, sequenced workflows (Preflight, Answer a data question, Error recovery, Connect source, After MDL changes) with explicit validation checkpoints (dry-plan verify before execute, context validate, profile debug) and a layered error-recovery feedback loop (dry-plan -> Layer 2A/2B -> fix one issue -> retry).	5 / 5
Progressive Disclosure	Well-signaled one-level-deep references to real bundle files (memory.md, wren-sql.md) reached via 'wren skills get usage --full', with the bulk of detail offloaded and clear navigation; verified references/memory.md and references/wren-sql.md exist.	5 / 5
	Total	19 / 20 Passed

Description

91%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.

A strong, third-person description that clearly states both what the skill does and when to use it, with rich natural trigger terms and a distinct niche around the wren CLI. Minor tightening could lift specificity from 4 to 5.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions ('gather schema context, recall past queries, write SQL through the MDL semantic layer, execute, and learn from confirmed results') plus data-source/error/MDL handling; minor gaps in coverage versus the comprehensive 5-anchor.	4 / 5
Completeness	Explicitly answers both 'what' (end-to-end data-question workflow via the wren CLI: gather, recall, write SQL, execute, learn) and 'when' (an explicit, detailed 'Use when:' clause enumerating concrete trigger conditions).	5 / 5
Trigger Term Quality	Comprehensive natural trigger phrases ('how many', 'show me', 'what is the', 'top N', 'compare', 'trend', 'growth', 'breakdown') plus business-domain terms (metrics, revenue, customers, orders) — wide coverage of phrasings users actually say.	5 / 5
Distinctiveness Conflict Risk	Scoped to the wren CLI / Wren Engine MDL semantic layer with business-data triggers, a clear niche with minimal overlap risk against other skills.	5 / 5
	Total	19 / 20 Passed

Validation

93%

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 — 15 / 16 Passed

Validation for skill structure

Criteria	Description	Result
metadata_version	'metadata.version' is missing	Warning

	Total	15 / 16 Passed

Repository: Canner/WrenAI
Path: core/wren/src/wren/skills_content/usage/SKILL.md
Commit: 7830cc7

Reviewed: about 14 hours ago

Table of Contents

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