data-partitioner
Data Partitioner - Auto-activating skill for Data Pipelines. Triggers on: data partitioner, data partitioner Part of the Data Pipelines skill category.
31
0%
Does it follow best practices?
Impact
87%
1.03xAverage 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/data-partitioner/SKILL.mdQuality
Discovery
0%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 essentially a placeholder with no substantive content. It names the skill and its category but provides zero information about what the skill does, what specific operations it performs, or when it should be selected. It would be indistinguishable from other data-related skills in a multi-skill environment.
Suggestions
Add concrete actions describing what the skill does, e.g., 'Splits datasets into partitions by key, date range, or hash. Supports horizontal and vertical partitioning strategies for large-scale data processing.'
Add an explicit 'Use when...' clause with natural trigger terms, e.g., 'Use when the user asks to partition data, split datasets, shard tables, distribute data across buckets, or implement partitioning strategies in a data pipeline.'
Remove the duplicate trigger term ('data partitioner' listed twice) and replace with varied natural language terms users would actually use, such as 'split data', 'partition by key', 'shard', 'chunk', 'bucket data'.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description provides no concrete actions whatsoever. It only names itself ('Data Partitioner') and its category ('Data Pipelines') without describing what it actually does—no mention of partitioning strategies, data splitting, file handling, or any specific operations. | 1 / 3 |
Completeness | The description fails to answer both 'what does this do' and 'when should Claude use it'. There is no explanation of capabilities and no explicit 'Use when...' clause or equivalent trigger guidance. | 1 / 3 |
Trigger Term Quality | The trigger terms are just 'data partitioner' repeated twice. These are not natural keywords a user would say; users are more likely to say things like 'split data', 'partition table', 'shard', 'chunk data', or 'distribute data across partitions'. | 1 / 3 |
Distinctiveness Conflict Risk | The description is extremely generic—'Data Pipelines' could overlap with many data-related skills. Without specific actions or clear triggers, it would be nearly impossible to distinguish this from other data processing or pipeline skills. | 1 / 3 |
Total | 4 / 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 hollow template with no substantive content. It contains only generic boilerplate descriptions that could apply to any skill topic, with no actual information about data partitioning techniques, tools, code, or workflows. It fails on every dimension because it teaches Claude nothing and provides no actionable guidance.
Suggestions
Add concrete, executable code examples for common data partitioning strategies (e.g., hash partitioning in Spark with `df.repartition()`, range partitioning in SQL, Hive-style partitioning with `partitionBy()`)
Define a clear workflow for implementing data partitioning: assess data characteristics → choose partition strategy → implement → validate partition distribution → monitor skew
Include specific guidance on partition key selection, handling data skew, and partition pruning with concrete examples rather than abstract claims about 'best practices'
Remove all meta-content (Purpose, When to Use, Example Triggers, Capabilities sections) and replace with actual technical content about data partitioning patterns and anti-patterns
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is entirely filler and boilerplate. It explains nothing Claude doesn't already know, provides no specific technical content about data partitioning, and wastes tokens on generic meta-descriptions like 'Provides step-by-step guidance' and 'Follows industry best practices' without any actual guidance or practices. | 1 / 3 |
Actionability | There is zero actionable content—no code, no commands, no concrete steps, no examples of data partitioning strategies (hash, range, list, round-robin), no configuration snippets, no tool usage. The entire skill describes what it could do rather than instructing how to do anything. | 1 / 3 |
Workflow Clarity | No workflow is defined at all. There are no steps, no sequence, no validation checkpoints. The skill claims to provide 'step-by-step guidance' but contains none. | 1 / 3 |
Progressive Disclosure | There is no meaningful content to organize, no references to supporting files, and no bundle files exist. The sections present are superficial headers over empty platitudes rather than a structured information hierarchy. | 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
ba8561d
- Reviewed
Table of Contents
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