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testland/sqlmesh-migrations

Authors and runs SQLMesh - data-transformation framework with version control, virtual data environments, automatic breaking-vs-non-breaking change classification, and downstream impact analysis; supports `sqlmesh init` / `plan` / `apply` / `run` / `audit` / `test` lifecycle; covers DuckDB, Postgres, Snowflake, BigQuery, Redshift, Databricks. Use when the user works with SQL data pipelines (warehouse + dbt-adjacent ELT), needs safer model evolution than dbt's deploy-and-pray, or wants the strongest impact-analysis story in the OSS data tooling space.

73

Quality

92%

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Overview
Quality
Evals
Security
Files

Quality

Content

80%

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

A concrete, well-sequenced, SQLMesh-specific walkthrough with executable commands and examples throughout and no generic-concept padding. Its main gaps are the absence of explicit error-recovery feedback loops for batch/destructive operations and a monolithic structure that would benefit from bundled reference files.

Suggestions

Add explicit error-recovery feedback loops for the batch/destructive operations: after `sqlmesh plan`, state what to do when a breaking change appears (accept and backfill, or revise the model and re-plan); after `sqlmesh audit` fails, fix the model and re-run plan/audit until clean.

Split detailed material out of SKILL.md into bundled reference files (e.g., references/model-kinds.md, references/audit-catalog.md, references/ci-templates.md) and link to them from a lean overview so the main file stays a high-level entry point.

Fix the AUDIT example syntax (`AUDIT ( name no_null_amounts, );` has an invalid trailing comma inside the directive call) so the example is copy-paste executable.

DimensionReasoningScore

Conciseness

The body assumes Claude's competence — it never explains what SQL, a warehouse, a cron schedule, or unit tests are — and only defines SQLMesh-specific concepts (virtual environments, breaking/non-breaking classification). Every section (Steps 1–9, Anti-patterns, Limitations) earns its place. Not a 2 because the few doc quotes are tool-specific rather than generic padding.

3 / 3

Actionability

Provides copy-paste-ready, executable commands throughout ("pip install sqlmesh", "sqlmesh init duckdb", "sqlmesh plan dev", "sqlmesh run", "sqlmesh audit", "sqlmesh test") plus complete SQL model, AUDIT, YAML test, and CI examples. Not a 2 because guidance is concrete and executable rather than pseudocode or vague direction (a minor syntax slip in the AUDIT example does not undermine the overall executability).

3 / 3

Workflow Clarity

The 9 steps are clearly sequenced and include a plan-review checkpoint ("The user reviews the plan, then confirms") plus CI test/plan/audit gates, but there is no explicit validate→fix→retry feedback loop for the batch/destructive operations this skill drives. It is not a 3 because the rubric specifically rewards error-recovery feedback loops for batch/database workflows, which are absent; it is not a 1 because the sequence and a verification gate are present.

2 / 3

Progressive Disclosure

The body is well-organized into clear sections, but it is monolithic — all content is inline in SKILL.md with no bundled reference files (references/, scripts/, assets/ are absent) to offload detail. It is not a 3 because, for a ~230-line 9-step tool, detailed material (model-kind catalog, audit catalog, CI templates) should be split into one-level-deep reference files; it is not a 1 because sections are well-structured, not a disorganized wall of text.

2 / 3

Total

10

/

12

Passed

Description

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.

A strong, third-person description that concretely enumerates SQLMesh's lifecycle actions and distinguishing features, supplies natural trigger terms, and gives an explicit Use-when clause with a clearly delineated niche versus dbt. No weaknesses worth flagging.

DimensionReasoningScore

Specificity

Quotes "Authors and runs SQLMesh", "automatic breaking-vs-non-breaking change classification, and downstream impact analysis", and the concrete "`sqlmesh init` / `plan` / `apply` / `run` / `audit` / `test` lifecycle" plus six named engines — multiple specific concrete actions, matching the score-3 anchor. It is not a 2 because it goes well beyond naming a domain and a couple of actions into a comprehensive, enumerated capability list.

3 / 3

Completeness

Explicitly answers "what" (authors/runs SQLMesh with the listed lifecycle and features) and "when" via the explicit "Use when the user works with SQL data pipelines ... needs safer model evolution ... or wants the strongest impact-analysis story" clause. Not a 2 because the trigger guidance is explicit, not merely implied.

3 / 3

Trigger Term Quality

Includes natural terms a user would actually say — "SQL data pipelines", "warehouse", "ELT", "dbt", "model evolution", "impact analysis" — giving good coverage of common variations. Not a 2 because the keywords span the realistic request vocabulary for this domain rather than a single narrow term.

3 / 3

Distinctiveness Conflict Risk

Names a clear niche (SQLMesh) and explicitly contrasts it with dbt and DDL-focused sister tools (Flyway/Atlas), giving distinct triggers unlikely to fire for the wrong skill. Not a 2 because the SQLMesh-specific framing and engine list make it clearly distinguishable, despite minor overlap risk with a dbt skill via the "dbt-adjacent ELT" trigger.

3 / 3

Total

12

/

12

Passed

Validation

100%

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

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Reviewed

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