Name: google-cloud-dataflow
Rating: 55.2 (1 reviews)
Author: membranedev

google-cloud-dataflow

Google Cloud Dataflow integration. Manage data, records, and automate workflows. Use when the user wants to interact with Google Cloud Dataflow data.

Quality

62%

Does it follow best practices?

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Securityby

Passed

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tessl review fix ./skills/google-cloud-dataflow/SKILL.md

Quality

Content

67%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 solid, actionable CLI integration guide with a well-handled connection state-machine and feedback loop. Its main weaknesses are a concept-explaining intro paragraph that Claude does not need and a step-numbering inconsistency that slightly muddies navigation.

Suggestions

Remove or condense the opening paragraph explaining what Google Cloud Dataflow is — Claude already knows the service; keep only the official docs link.

Fix the step labeling so 'skip to Step 2' resolves to an actual labeled step, replacing the orphaned '1b' subsection with a consistent Step 1 / Step 2 scheme.

Trim promotional phrasing in Best practices ('burn less tokens and make communication more secure') to neutral, concrete rationale.

Dimension	Reasoning	Score
Conciseness	The body is mostly efficient command reference, but the opening paragraph explains what Google Cloud Dataflow is ('fully managed, serverless stream and batch data processing service... often used for ETL') — a concept Claude already knows — and Best practices contains promotional phrasing.	3 / 5
Actionability	Concrete, copy-paste-ready commands are provided throughout (install, login, connection ensure, action list/run, request) with a clear flags table; minor gaps are the unfilled placeholders (CONNECTION_ID, QUERY, /path/to/endpoint) and no fully-worked end-to-end example.	4 / 5
Workflow Clarity	There is a clear install → authenticate → connect → search → run → proxy sequence with a state-machine feedback loop (poll --wait, handle clientAction, re-poll), but the 'skip to Step 2' reference points to a label that does not exist alongside the '1b' subsection.	4 / 5
Progressive Disclosure	No bundle files exist, so all content lives inline in a single well-sectioned SKILL.md; organization is good with clear headers, but the 'Popular actions' section partially repeats 'Searching for actions' and the step numbering is inconsistent.	4 / 5
	Total	15 / 20 Passed

Description

57%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 correctly names a specific service and includes an explicit 'Use when' trigger, but its action verbs are generic and it lacks natural trigger synonyms. It is functional but would benefit from concrete Dataflow operations and richer trigger phrasing.

Suggestions

Replace generic verbs ('Manage data, records, and automate workflows') with concrete Dataflow actions such as 'launch and monitor stream/batch pipelines, inspect job metrics, and manage job templates'.

Add natural trigger synonyms users actually say, e.g. 'Use when the user wants to run or monitor Dataflow pipelines, ETL jobs, or stream/batch processing on GCP'.

Dimension	Reasoning	Score
Specificity	The description names the domain ('Google Cloud Dataflow integration') but its actions are generic rather than concrete: 'Manage data, records, and automate workflows' uses abstract verbs with no specific Dataflow operations.	2 / 5
Completeness	Both 'what' ('Manage data, records, and automate workflows') and 'when' ('Use when the user wants to interact with Google Cloud Dataflow data') are present, though the trigger could be more specific.	4 / 5
Trigger Term Quality	The primary natural term 'Google Cloud Dataflow' is present, but common variations users would say (Dataflow pipelines, ETL jobs, stream/batch processing) are missing.	3 / 5
Distinctiveness Conflict Risk	Naming the specific service 'Google Cloud Dataflow' gives a clear niche with minimal conflict risk, though the generic 'manage data, records, automate workflows' actions could overlap with other data skills.	4 / 5
	Total	13 / 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/google-cloud-dataflow/SKILL.md
Commit: f484c82

Reviewed: 5 days ago

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