Name: qdrant
Rating: 56.00000000000001 (1 reviews)
Author: membranedev

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qdrant

Qdrant integration. Manage Collections, Snapshots. Use when the user wants to interact with Qdrant data.

Quality

63%

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tessl review fix ./skills/qdrant/SKILL.md

Quality

Content

61%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.

The content is a competent CLI-wrapper guide with strong executable commands and a clear connection state machine, but it spends tokens explaining Qdrant basics Claude already knows and omits validation steps for destructive collection/snapshot operations. Action examples would benefit from one fully worked real Qdrant call.

Suggestions

Delete the opening paragraph defining Qdrant ("Qdrant is a vector similarity search engine...") and similar padding; Claude already knows what Qdrant is.

Add a worked example with real values for a common Qdrant action, e.g. creating a collection or upserting points via `membrane action run`, instead of only placeholder-driven `--intent "QUERY"`.

Add a verification/feedback step for destructive operations (e.g. confirm a collection exists before deleting, or verify a snapshot completed) so destructive workflows include explicit validation checkpoints.

Dimension	Reasoning	Score
Conciseness	The body is mostly tight CLI commands, but it opens by explaining what Qdrant is ("Qdrant is a vector similarity search engine and vector database. It's used by developers and data scientists...") and includes padding like "This is the fastest way to get a connection" that Claude does not need.	3 / 5
Actionability	It provides many concrete, runnable commands (login, connection ensure/get, action list/run, request with a flags table), but the action examples rely on placeholders (QUERY, CONNECTION_ID) with no worked real Qdrant example such as creating a collection, leaving a minor gap.	4 / 5
Workflow Clarity	The connect-to-poll-to-ready flow is well sequenced with a state-machine checkpoint, but the skill manages Collections and Snapshots (destructive/batch operations) with no validation or verification steps for those operations, which caps workflow clarity at 3.	3 / 5
Progressive Disclosure	No bundle files exist and the single SKILL.md is well organized into clear sections (Overview, Install, Authentication, Connecting, Searching, Running, Proxy, Best practices); it is over 50 lines so it does not hit the simple-skill 5, but structure is good with only minor organization gaps.	4 / 5
	Total	14 / 20 Passed

Description

66%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 cleanly identifies the Qdrant niche and includes an explicit Use-when clause, but its action list is generic ("Manage") and it lacks synonym coverage like "vector database/search". It is distinct and unlikely to misfire, just not maximally specific or trigger-rich.

Suggestions

Replace the generic verb "Manage" with concrete operations, e.g. "Create and delete Collections, upsert and search Points, create and restore Snapshots".

Expand the Use-when clause with natural trigger phrases users actually say, e.g. "Use when the user wants to query a vector database, search embeddings, or manage Qdrant collections, points, or snapshots."

Add the synonyms "vector database" and "vector search" so the description matches how users refer to Qdrant's purpose.

Dimension	Reasoning	Score
Specificity	"Manage Collections, Snapshots" names the Qdrant domain plus two actions, but "Manage" is generic and coverage is not comprehensive, matching the 1-2 concrete actions anchor rather than the several-actions anchor at 4.	3 / 5
Completeness	It states both a what ("Manage Collections, Snapshots") and an explicit when ("Use when the user wants to interact with Qdrant data"), but the when-clause is a single generic trigger rather than concrete multiple trigger phrases, so it sits below the 5 anchor.	4 / 5
Trigger Term Quality	It includes relevant keywords ("Qdrant", "Collections", "Snapshots", "interact with Qdrant data") but omits common synonyms a user would naturally say such as "vector database" or "vector search", landing at the some-keywords-but-missing-variations anchor.	3 / 5
Distinctiveness Conflict Risk	Qdrant is a specific named product and the triggers ("Qdrant integration", "interact with Qdrant data") are tightly scoped to it, giving a clear niche with minimal conflict risk.	5 / 5
	Total	15 / 20 Passed

Validation

93%

Warnings & errors only

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

Criteria	Description	Result
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	15 / 16 Passed

Repository: membranedev/application-skills
Path: skills/qdrant/SKILL.md
Commit: f484c82

Reviewed: 5 days ago

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