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 repeats the skill name as its only trigger term, provides zero information about what the skill actually does, and lacks any explicit guidance on when Claude should select it. It would be nearly impossible for Claude to correctly choose this skill from a pool of data-related skills.
Suggestions
Add concrete actions describing what the skill does, e.g., 'Partitions datasets by key, date range, or hash; splits large tables into smaller segments for parallel 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, create data buckets, or distribute data across segments.'
Remove the duplicate trigger term and expand with natural language variations users would actually say, such as 'partition', 'split data', 'shard', 'bucket', 'range partition', 'hash partition'.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description provides no concrete actions whatsoever. 'Data Partitioner' is just a name, and 'Auto-activating skill for Data Pipelines' is vague with no explanation of what it actually does (e.g., split datasets, partition tables, shard data by key). | 1 / 3 |
Completeness | Neither 'what does this do' nor 'when should Claude use it' is meaningfully answered. There is no 'Use when...' clause, and the description only states the skill's name and category without explaining functionality or trigger conditions. | 1 / 3 |
Trigger Term Quality | The only trigger terms listed are 'data partitioner, data partitioner' — a duplicate of the skill name itself. No natural user language variations are included (e.g., 'partition data', 'split dataset', 'shard', 'bucket data', 'range partition'). | 1 / 3 |
Distinctiveness Conflict Risk | The description is so generic that it could overlap with any data processing or pipeline skill. 'Data Pipelines' is a broad category, and without specific actions or file types, there is high conflict risk with other data-related 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 an empty shell with no substantive content. It consists entirely of generic boilerplate that could apply to any topic by swapping the phrase 'data partitioner.' It provides zero actionable guidance on data partitioning strategies, no code examples, no tool usage, and no concrete instructions.
Suggestions
Add concrete, executable code examples showing common data partitioning patterns (e.g., hash partitioning, range partitioning, time-based partitioning) with specific tools like Spark or SQL.
Define a clear workflow for implementing data partitioning: assess data characteristics → choose partition strategy → implement → validate partition distribution → monitor skew.
Remove all boilerplate sections (Purpose, When to Use, Example Triggers, Capabilities) that describe the skill meta-information rather than teaching how to partition data.
Include specific guidance on partition key selection, handling data skew, and common pitfalls with concrete examples and validation steps.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is entirely filler and boilerplate. It explains nothing Claude doesn't already know, repeats 'data partitioner' excessively, and provides zero substantive information about how to actually partition data. | 1 / 3 |
Actionability | There are no concrete code examples, commands, configurations, or specific instructions. Every section is vague and abstract—'Provides step-by-step guidance' without actually providing any steps. | 1 / 3 |
Workflow Clarity | No workflow is defined at all. There are no steps, no sequence, no validation checkpoints—just generic claims about capabilities without any actual process. | 1 / 3 |
Progressive Disclosure | The content is a flat, shallow document with no references to detailed materials, no linked resources, and no meaningful structure beyond boilerplate section headers that contain no real content. | 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 | |
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