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chdb-sql

In-process ClickHouse SQL engine for Python — run ClickHouse SQL queries directly on local files, remote databases, and cloud storage without a server. Use when the user wants to write SQL queries against Parquet/CSV/ JSON files, use ClickHouse table functions (mysql(), s3(), postgresql(), iceberg(), deltaLake() etc.), build stateful analytical pipelines with Session, use parametrized queries, window functions, or other advanced ClickHouse SQL features. Also use when the user explicitly mentions chdb.query(), ClickHouse SQL syntax, or wants cross-source SQL joins. Do NOT use for pandas-style DataFrame operations — use chdb-datastore instead.

76

Quality

95%

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SecuritybySnyk

High

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SKILL.md
Quality
Evals
Security

Quality

Content

90%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 lean, highly actionable reference skill: executable examples for every API surface, a clear decision tree, and well-organized one-level-deep references. The main defects are a broken link to a nonexistent examples/examples.md and the absence of explicit validation/retry loops in the Session and remote-database workflows.

Suggestions

Fix the broken reference: either create examples/examples.md with the promised "9 runnable examples with expected output" or remove the Examples entry from the References section.

Add an explicit validation checkpoint to the Session workflow (e.g., verify table row counts with a SELECT count() after each CREATE TABLE ... AS SELECT before building on it, and re-check on failure).

Tie the troubleshooting table into the workflows it supports (e.g., a one-line pointer after the mysql()/s3() examples: "On connection or FILE_NOT_FOUND errors, see Troubleshooting") so error recovery is a loop rather than a lookup.

DimensionReasoningScore

Conciseness

The body is almost entirely executable code, a decision tree, and a troubleshooting table — no explanations of concepts Claude already knows (it never explains what ClickHouse or SQL is), and comments like "# local files" / "# parametrized" are labels, not padding. Not score 4 because there is no over-explanation to trim; every section earns its tokens.

5 / 5

Actionability

Copy-paste ready code covers the common cases end-to-end: chdb.query() on local files/mysql/s3/deltaLake, a cross-source join, Python(data), DataFrame output, parametrized queries with params={...}, a Session pipeline, and DB-API 2.0 usage — plus a troubleshooting table with concrete fixes and a runnable verify script. Not score 4 because the examples are complete and executable with no gaps.

5 / 5

Workflow Clarity

The decision tree cleanly sequences API choice ("1. One-off query... 2. Multi-step analysis... 3. DB-API 2.0... 4. Pandas-style..."), and the troubleshooting table plus "Run `python scripts/verify_install.py`" provide error-recovery and setup validation. Not score 5 because there is no explicit validate→fix→retry checkpoint within the workflows themselves (e.g., after creating Session tables or hitting remote-DB errors, recovery is implied via the table rather than an explicit loop); not score 3 because the sequence is clear and checkpoints are mostly present.

4 / 5

Progressive Disclosure

Good structure: lean overview body with one-level-deep references clearly signaled both inline ("Table functions → [table-functions.md](references/table-functions.md)") and in a References section, and the three referenced files exist in the bundle. Not score 5 because the References section lists "[Examples](examples/examples.md) — 9 runnable examples with expected output" but no examples/ directory exists in the bundle — a broken reference that breaks navigation; not score 3 because the split of bulk detail into references/ is otherwise appropriate and well-signaled.

4 / 5

Total

18

/

20

Passed

Description

100%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.

An exemplary description: concrete capability list, natural trigger phrases including file formats and API names, explicit what/when guidance, and an explicit boundary against the sibling chdb-datastore skill. Third-person voice throughout with no fluff despite the density.

DimensionReasoningScore

Specificity

Multiple concrete actions are listed: "run ClickHouse SQL queries directly on local files, remote databases, and cloud storage", "use ClickHouse table functions (mysql(), s3(), postgresql(), iceberg(), deltaLake() etc.)", "build stateful analytical pipelines with Session", "use parametrized queries, window functions", and "cross-source SQL joins" — comprehensive coverage of the skill's capabilities. Not score 4 because there are no minor gaps: output formats aside, every listed action is specific and concrete, matching the level-5 anchor.

5 / 5

Completeness

Explicitly answers both: WHAT — "In-process ClickHouse SQL engine for Python — run ClickHouse SQL queries directly on local files... without a server"; WHEN — "Use when the user wants to write SQL queries against Parquet/CSV/JSON files... Also use when the user explicitly mentions chdb.query()... or wants cross-source SQL joins." Both are concrete and explicit with trigger phrases, matching the level-5 anchor exactly.

5 / 5

Trigger Term Quality

Covers natural user phrasing comprehensively: "write SQL queries against Parquet/CSV/JSON files" (includes file formats/extensions), "ClickHouse SQL syntax", "chdb.query()", "cross-source SQL joins", and "window functions" — terms users would naturally say when needing this skill. Not score 4 because synonyms and format variations are present, not just a few good keywords.

5 / 5

Distinctiveness Conflict Risk

Clear niche (in-process ClickHouse SQL) with a distinct trigger vocabulary, plus an explicit negative boundary — "Do NOT use for pandas-style DataFrame operations — use chdb-datastore instead" — that minimizes conflict risk with the closest related skill. Not score 4 because the disambiguation is explicit rather than leaving minor overlap risk.

5 / 5

Total

20

/

20

Passed

Validation

93%

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

CriteriaDescriptionResult

relative_links

Relative link issues: 1 missing

Warning

Total

15

/

16

Passed

Repository
MapleTechLabs/maple
Reviewed

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