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mongodb-search-and-ai

Covers everything about search in MongoDB: Atlas Search (full-text), Vector Search (semantic), and Hybrid Search. Use this skill for ANY question about MongoDB search, including open-ended and explanatory ones asked before the user has any data or use case — 'how does search work in MongoDB?', 'I'm new to MongoDB search, where do I start?', 'what's the difference between Atlas Search and Vector Search?', 'which search type should I use?', 'what is vector search?', 'how does automated embedding work?', 'can you explain hybrid search?'. Do not answer these from general knowledge; use this skill. Also use to build search features: full-text queries, autocomplete, fuzzy matching, faceted search, semantic similarity, embeddings, RAG, text containment or substring matching ('contains', 'includes'), case-insensitive or multi-field text search, and filtering across many fields. Provides workflows for choosing a search type, creating indexes, writing queries, and optimizing performance via the MongoDB MCP server.

Quality

94%

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SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

Quality

Content

85%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 orchestrating SKILL.md: a clearly sequenced workflow with approval gates and error-recovery loops, routing to real, well-scoped bundle files with an explicitly gated quick-start tour. The main room for improvement is tightening the quickstart-disambiguation section and including one small inline example (index JSON or pipeline skeleton) so common cases don't require loading a reference file.

Suggestions

Conciseness: condense the Step 0 quickstart gate — the eight-example prompt list and the AskUserQuestion routing rationale overlap; a compact rule ("offer the tour for newcomer-style or 'show me' prompts unless the user names their own data") with 2-3 example prompts would cut ~10 lines with no loss.

Actionability: include one minimal inline example (e.g., a skeleton Atlas Search index JSON or a $rankFusion hybrid pipeline) so the most common request can be handled without first loading a reference file, keeping full detail in references/.

DimensionReasoningScore

Conciseness

The body is efficient and assumes competence — it never explains what MongoDB or search is, and defers detail to references — but the quickstart-gate section runs long: the eight-item prompt trigger list ("I'm new to MongoDB, how does search work?"... "I want to try hybrid search") plus the AskUserQuestion routing explanation overlap and could be condensed. This is 'efficient with minor instances that could be trimmed' (anchor 4), not the fully lean anchor 5.

4 / 5

Actionability

Concrete, executable guidance throughout: named MCP tools ("list-databases", "collection-schema", "collection-indexes", "atlas-inspect-cluster", "create-index", "aggregate"), an exact approval script ("Get explicit approval: 'Should I create this index?'") and a ready AskUserQuestion prompt. It falls short of anchor 5 because no index JSON or aggregation pipeline example appears inline — all copy-paste material is delegated to the reference files, leaving minor gaps for the common case of a user who wants to see one example immediately.

4 / 5

Workflow Clarity

The 0→3 phase sequence is explicit with validation checkpoints and feedback loops: "Always inspect first - Check existing indexes and schema", an approval gate before index creation ("Explain before executing... require explicit approval"), re-validation when refining ("Run the revised query with 'aggregate' to validate the results"), and error recovery in Handling Edge Cases ("Query fails or index missing — verify with 'collection-indexes'; if absent, the index must be created first"). Database write operations are gated by explicit approval, matching the top anchor including error-recovery loops.

5 / 5

Progressive Disclosure

The body is a genuine overview that routes to one-level-deep, well-signaled references ("→ Consult both 'references/lexical-search-indexing.md' (index) and 'references/lexical-search-querying.md' (query)"), and every referenced file exists in the bundle (all six references/*.md plus scripts/quickstart_complete.py reachable via quick-start.md). The quick-start reference is even load-gated ("Do not read 'references/quick-start.md' until the user has chosen the tour"), matching the clear-overview top anchor.

5 / 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.

The description is exemplary: it names the domain's three sub-features, lists comprehensive concrete capabilities, and provides an explicit 'use this skill for...' clause stocked with verbatim natural-language trigger phrases for both explanatory and build requests. It also guards the pre-data/explainer case explicitly, which is rare. No changes needed.

DimensionReasoningScore

Specificity

Lists multiple concrete, comprehensive actions: "full-text queries, autocomplete, fuzzy matching, faceted search, semantic similarity, embeddings, RAG, text containment or substring matching ('contains', 'includes')", plus "creating indexes, writing queries, and optimizing performance via the MongoDB MCP server". This matches the anchor for comprehensive concrete-action coverage; it is not score 4 because there are no noticeable coverage gaps across the three search types.

5 / 5

Completeness

Explicitly answers "what" ("Covers everything about search in MongoDB: Atlas Search (full-text), Vector Search (semantic), and Hybrid Search"; "Provides workflows for choosing a search type, creating indexes, writing queries, and optimizing performance") and "when" ("Use this skill for ANY question about MongoDB search... Also use to build search features...") with concrete trigger phrases. Both halves are explicit and concrete, matching the top anchor; a missing 'Use when...' clause would cap it at 3, but that clause is present.

5 / 5

Trigger Term Quality

Quotes natural user phrasings verbatim — "'how does search work in MongoDB?'", "'I'm new to MongoDB search, where do I start?'", "'which search type should I use?'", "'can you explain hybrid search?'" — and includes synonym variants like "'contains', 'includes'" and "autocomplete", "fuzzy matching". Coverage of natural terms including synonyms matches the top anchor, not score 4, since both beginner-style questions and feature-building keywords are present.

5 / 5

Distinctiveness Conflict Risk

The niche is unambiguous — "search in MongoDB" with the three named sub-features — and triggers like "what's the difference between Atlas Search and Vector Search?" or "how does automated embedding work?" would not naturally fire a different skill. Minor scope breadth from "filtering across many fields" keeps it just at the clear-niche anchor rather than suggesting overlap; it fits anchor 5 better than anchor 4's "minor overlap risk with closely related 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.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository

boranfurkan/reproducer-hackathon

Path

skills/.agents/skills/mongodb-search-and-ai/SKILL.md

Reviewed

Table of Contents

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