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mongodb-query-optimizer

Help with MongoDB query optimization and indexing. Use only when the user asks for optimization or performance: "How do I optimize this query?", "How do I index this?", "Why is this query slow?", "Can you fix my slow queries?", "What are the slow queries on my cluster?", etc. Do not invoke for general MongoDB query writing unless user asks for performance or index help. Prefer indexing as optimization strategy. Use MongoDB MCP when available.

Quality

86%

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SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

Quality

Content

78%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.

Highly actionable content: exact MCP tool invocations with full argument shapes and two concrete worked examples make the guidance immediately executable, and reference files are well-organized with clear conditional loading. The main weakness is repetition — the tool usage flow is restated across the workflow, MCP-tools, and example sections — which inflates token cost without adding guidance.

Suggestions

Consolidate the tool-usage narrative: state the collection-indexes → explain → find and atlas-get-performance-advisor flows once (in the MCP tools section or the High Level Workflow), and let the example workflows reference them instead of re-listing each call and its arguments.

Tighten the fallback prose, e.g. compress 'If Atlas MCP Server for Atlas is not configured or you don't have enough information to run atlas-get-performance-advisor against the correct cluster, tell the user…' into a single crisp fallback rule.

Add a post-change validation step (re-run explain with executionStats after an index is created to confirm the plan improved) and clarify Example workflow 1 step 2, which ambiguously says 'verbosity=queryPlanner and executionStats' rather than 'run twice: queryPlanner first, then executionStats'.

DimensionReasoningScore

Conciseness

The MCP tool argument tables and invocation gotchas are genuinely new information Claude would not know, but the same tool flow is narrated three times (High Level Workflow, the '1./2.' connection-mode subsections, and both example workflows) and several sentences are wordy ('If Atlas MCP Server for Atlas is not configured or you don't have enough information…'), so it is mostly efficient but could be tightened. Not 2: there is no padding with concepts Claude already knows; not 4: the cross-section repetition is noticeable rather than minor.

3 / 5

Actionability

Exact MCP tool names with copy-paste-ready JSON argument objects (e.g. the full explain method/verbosity shape and atlas-get-performance-advisor required/optional fields), plus two worked examples with real filters, explain results, selectivity math, and a concrete ESR-based compound index recommendation — specific examples cover both common cases (specific query, general cluster performance).

5 / 5

Workflow Clarity

Clear sequences with explicit conditional branching ('If MCP db connection is configured… run steps 1–3. Otherwise skip to step 4') and a checkpoint (run explain in queryPlanner first to check for COLLSCAN, then executionStats only if needed). Not 5: there is no post-change verification loop (e.g. re-running explain after index creation) and Example workflow 1 step 2 ambiguously says 'verbosity=queryPlanner and executionStats'; not 3: checkpoints are present and explicit, and index creation is gated on user approval so no destructive-operation cap applies.

4 / 5

Progressive Disclosure

The body is a workflow overview with a 'Load references' section that clearly signals one-level-deep files — always load core-indexing-principles.md and antipattern-examples.md, conditionally load aggregation-optimization.md and update-query-examples.md with explicit 'If diagnosing X → load Y' conditions — and all four referenced files exist in the bundle. The inline MCP tool table is per-invocation operational detail that appropriately stays in the overview.

5 / 5

Total

17

/

20

Passed

Description

87%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 description with an explicit 'Use only when' trigger clause, five concrete quoted user phrases, and a negative boundary that sharply scopes it against general MongoDB query help. Slightly generic 'Help with' phrasing and a few missing natural synonyms ('speed up', 'faster') keep specificity and trigger quality just below the top anchors.

DimensionReasoningScore

Specificity

Names the domain and several concrete actions — 'query optimization and indexing', 'Prefer indexing as optimization strategy', 'Use MongoDB MCP' — but 'Help with' is generic and coverage has minor gaps (no mention of explain-plan analysis or aggregation tuning), matching the 'several specific actions with minor gaps' anchor rather than comprehensive coverage.

4 / 5

Completeness

Explicitly answers both 'what' ('Help with MongoDB query optimization and indexing') and 'when' ('Use only when the user asks for optimization or performance') with concrete quoted trigger phrases and a negative boundary ('Do not invoke for general MongoDB query writing'), matching the top anchor; score 4 would require the 'when' to be less explicit, which it is not.

5 / 5

Trigger Term Quality

Includes multiple natural user utterances ('How do I optimize this query?', 'Why is this query slow?', 'Can you fix my slow queries?', 'What are the slow queries on my cluster?') plus keywords 'optimization', 'performance', 'index', but misses common synonyms such as 'speed up' or 'make my query faster', fitting 'good keyword coverage; a few natural terms missing'.

4 / 5

Distinctiveness Conflict Risk

Clear niche (MongoDB performance/indexing) with distinct trigger phrases, and the exclusion clause 'Do not invoke for general MongoDB query writing unless user asks for performance or index help' explicitly disambiguates it from a general MongoDB query-writing skill, giving minimal conflict risk.

5 / 5

Total

18

/

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.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository

boranfurkan/reproducer-hackathon

Path

skills/.agents/skills/mongodb-query-optimizer/SKILL.md

Reviewed

Table of Contents

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