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mongodb-expert

Use when working with MongoDB — designing a schema or collection (embed vs reference, data modeling, BSON types, schema validation), optimizing performance (indexes, ESR rule, explain() / slow query tuning, aggregation pipelines, covered queries), benchmarking or monitoring (profiler, mongostat, working set, executionStats), or applying standard practices (write/read concern, read preference, transactions, replication, sharding/shard keys, security, connection pooling). Covers MongoDB 8.0 with version-gated notes back to 4.x.

73

Quality

90%

Does it follow best practices?

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SKILL.md
Quality
Evals
Security

Quality

Content

80%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 well-structured reference overview: lean and high-signal with excellent progressive disclosure (concise body, four real one-level-deep reference files, clean nav table). Its weaknesses are actionability (full code examples live in the references rather than the body) and workflow clarity (clear sequences but no explicit validate→fix→retry feedback loops for database operations).

Suggestions

Add an explicit validate→fix→retry feedback loop for database workflows — e.g., for slow-query tuning: run `explain("executionStats")` → check `nReturned ≈ totalKeysExamined ≈ totalDocsExamined` → if a COLLSCAN or blocking SORT appears, add/fix the index per ESR → re-run explain — framing the existing performance diagnostics as a sequenced loop rather than standalone rules.

Add a verification checkpoint to the 'Keeping this skill current' maintenance workflow (e.g., after updating version-gated claims, re-grep to confirm every `(≥X.Y)` tag still carries an IA/DG citation, or re-check each silently-changed default against the release notes) so the batch update has an explicit pass/fail step.

Inline one or two complete copy-paste examples in the body (e.g., a canonical `explain("executionStats")` invocation and an ESR-ordered `createIndex()` call) to raise actionability from concrete-commands to fully-executable common-case examples.

DimensionReasoningScore

Conciseness

The body is lean and high-signal — no padding explaining what MongoDB, an index, or BSON is; every section (version baseline, nav table, highest-leverage rules, maintenance) earns its place with MongoDB-specific, version-gated facts. Only one or two trivial known-concept clauses ('Normalizing speeds writes; denormalizing speeds reads') are borderline, keeping it at the top anchor rather than 4.

5 / 5

Actionability

Provides concrete commands and config values — `grep -rn '(≥' …`, `explain("executionStats")`, `writeConcern:{w:"majority"}`, 16 MB limit, 3-member replica set — but complete copy-paste code examples covering common cases are delegated to the reference files rather than inlined, so it sits above the pseudocode/incomplete anchor but below fully-executable.

4 / 5

Workflow Clarity

The 'Which reference to load' table and the numbered 1–5 maintenance sequence give clear sequencing, and step 4 ('Re-verify defaults that silently change') plus the explain() diagnostic ('A COLLSCAN or a blocking SORT stage means a missing/wrong index') are implicit checkpoints, but there is no explicit validate→fix→retry feedback loop — which the rubric stresses for database operations — so it caps at 3 rather than 4.

3 / 5

Progressive Disclosure

Verified against the bundle: all four referenced files (schema-design.md, performance.md, benchmarking.md, standard-practices.md) exist, are standalone, and are one level deep with no nested cross-references; the body is a concise overview with a well-signaled symptom→file navigation table, matching the clear-overview / easy-navigation anchor.

5 / 5

Total

17

/

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 excellent description: it explicitly states both what the skill covers and when to use it, names comprehensive concrete actions across the four MongoDB task domains, and uses natural trigger phrases scoped tightly to MongoDB, minimizing overlap with other skills.

DimensionReasoningScore

Specificity

Lists multiple specific concrete actions across four categories — 'embed vs reference, data modeling, BSON types, schema validation', 'indexes, ESR rule, explain() / slow query tuning, aggregation pipelines, covered queries', 'profiler, mongostat, working set, executionStats', 'write/read concern, read preference, transactions, replication, sharding/shard keys' — exceeding the comprehensive-coverage anchor.

5 / 5

Completeness

Opens with an explicit 'Use when working with MongoDB —' trigger clause and pairs it with a concrete 'what' list of task areas, matching the anchor that 'clearly and explicitly answers both what AND when with concrete trigger phrases'.

5 / 5

Trigger Term Quality

Rich natural phrases a MongoDB practitioner would actually say — 'embed vs reference', 'slow query tuning', 'aggregation pipelines', 'benchmarking or monitoring', 'sharding/shard keys' — with coverage well beyond the score-4 example; not the lower anchor because keyword coverage is comprehensive, not merely 'good'.

5 / 5

Distinctiveness Conflict Risk

Carves a clear MongoDB-only niche with an unambiguous 'Use when working with MongoDB' trigger and MongoDB-specific terms (WiredTiger, mongostat, ESR rule, shard keys), giving minimal conflict risk with other database skills.

5 / 5

Total

20

/

20

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.

Repository
sayed-moin-ahmed/mongodb-expert-skill
Reviewed

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