beam-pipeline-builder
Beam Pipeline Builder - Auto-activating skill for Data Pipelines. Triggers on: beam pipeline builder, beam pipeline builder Part of the Data Pipelines skill category.
36
Quality
3%
Does it follow best practices?
Impact
100%
1.02xAverage score across 3 eval scenarios
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./planned-skills/generated/11-data-pipelines/beam-pipeline-builder/SKILL.mdQuality
Discovery
7%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
This description is severely underdeveloped, essentially just restating the skill name without providing any actionable information. It lacks concrete capabilities, natural trigger terms, and explicit usage guidance. Claude would struggle to know when to select this skill over others or what specific tasks it enables.
Suggestions
Add specific concrete actions like 'Creates Apache Beam pipelines for batch and streaming data processing, defines PTransforms, configures runners (Dataflow, Flink, Spark)'
Add a 'Use when...' clause with natural trigger terms: 'Use when the user mentions Apache Beam, Dataflow pipelines, PCollections, streaming ETL, or batch data processing'
Include file type triggers if applicable (e.g., '.py pipeline files', 'beam YAML configs') to improve distinctiveness from other data pipeline tools
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description only says 'Beam Pipeline Builder' and 'Data Pipelines' without describing any concrete actions. There are no verbs or specific capabilities listed - no mention of what building a pipeline actually involves. | 1 / 3 |
Completeness | The description fails to answer 'what does this do' beyond the name itself, and there is no 'Use when...' clause or equivalent guidance for when Claude should select this skill. Both components are essentially missing. | 1 / 3 |
Trigger Term Quality | The trigger terms are just 'beam pipeline builder' repeated twice. Missing natural variations users might say like 'Apache Beam', 'dataflow', 'ETL pipeline', 'streaming pipeline', 'batch processing', or 'PCollection'. | 1 / 3 |
Distinctiveness Conflict Risk | While 'Beam' is somewhat specific to Apache Beam, 'Data Pipelines' is generic and could overlap with other pipeline tools (Airflow, Luigi, Prefect). The lack of specific triggers increases conflict risk with other data engineering skills. | 2 / 3 |
Total | 5 / 12 Passed |
Implementation
0%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill is a placeholder template with no actual content about Apache Beam pipeline building. It contains only generic boilerplate describing what a skill should do without providing any concrete guidance, code examples, or actionable instructions. The content would be completely unhelpful for actually building Beam pipelines.
Suggestions
Add concrete, executable Python code examples showing basic Beam pipeline construction (e.g., ReadFromText -> ParDo -> WriteToText)
Include specific Beam concepts like PCollections, transforms, runners, and windowing with actual code snippets
Define a clear workflow for pipeline development: create pipeline -> add transforms -> configure runner -> execute -> validate output
Remove all generic boilerplate ('provides automated assistance', 'follows best practices') and replace with actual Beam-specific guidance
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is padded with generic boilerplate that explains nothing specific about Beam pipelines. Phrases like 'provides automated assistance' and 'follows industry best practices' are filler that Claude doesn't need. | 1 / 3 |
Actionability | No concrete code, commands, or specific guidance is provided. The skill describes what it does abstractly ('provides step-by-step guidance') but never actually provides any guidance, examples, or executable content. | 1 / 3 |
Workflow Clarity | No workflow, steps, or process is defined. The content only describes trigger conditions and vague capabilities without any actual procedural guidance for building Beam pipelines. | 1 / 3 |
Progressive Disclosure | No structure beyond generic headings. No references to detailed documentation, no links to examples or API references, and no organization of content by complexity or use case. | 1 / 3 |
Total | 4 / 12 Passed |
Validation
81%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 9 / 11 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
allowed_tools_field | 'allowed-tools' contains unusual tool name(s) | Warning |
frontmatter_unknown_keys | Unknown frontmatter key(s) found; consider removing or moving to metadata | Warning |
Total | 9 / 11 Passed | |
- Commit
0c08951
- Reviewed
Table of Contents
Is this your skill?
If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.