data-partitioner
Data Partitioner - Auto-activating skill for Data Pipelines. Triggers on: data partitioner, data partitioner Part of the Data Pipelines skill category.
31
Quality
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 severely underdeveloped and fails on all dimensions. It provides no concrete actions, no natural trigger terms, no 'Use when' guidance, and would be indistinguishable from other data-related skills. The duplicate trigger term suggests a template that wasn't properly filled out.
Suggestions
Add specific concrete actions describing what data partitioning involves (e.g., 'Splits large datasets into smaller chunks, partitions data by date/key/hash, creates sharded data files for parallel processing').
Add a 'Use when...' clause with natural trigger terms users would say (e.g., 'Use when the user needs to split data, partition datasets, shard tables, chunk large files, or distribute data across multiple outputs').
Include file types or data formats this skill handles (e.g., 'CSV, Parquet, JSON files') to distinguish it from other data pipeline skills.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description only names the skill ('Data Partitioner') without describing any concrete actions. There are no verbs or specific capabilities listed - it doesn't explain what partitioning means or what operations it performs. | 1 / 3 |
Completeness | The description fails to answer 'what does this do' (no actions described) and 'when should Claude use it' (no 'Use when...' clause or equivalent guidance). Only states it's part of 'Data Pipelines' category without explaining functionality. | 1 / 3 |
Trigger Term Quality | The only trigger terms listed are 'data partitioner, data partitioner' (duplicated). This is technical jargon that users are unlikely to naturally say. Missing natural terms like 'split data', 'partition', 'chunk', 'divide dataset', 'shard', etc. | 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, this could easily conflict with other data processing, ETL, 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 essentially a placeholder template with no substantive content. It describes what a data partitioner skill would do without providing any actual guidance, code examples, partitioning strategies, or concrete instructions. The content fails on all dimensions by being both verbose and empty of actionable information.
Suggestions
Add concrete code examples showing common data partitioning patterns (e.g., hash partitioning, range partitioning, time-based partitioning) with executable Python/Spark code
Define a clear workflow for implementing data partitioning: analyze data distribution → choose partition strategy → implement → validate partition balance
Include specific guidance on partition key selection, optimal partition sizes, and common pitfalls to avoid
Remove generic boilerplate sections ('Capabilities', 'Example Triggers') and replace with actionable technical content
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is padded with generic boilerplate that explains nothing Claude doesn't already know. Phrases like 'provides automated assistance' and 'follows industry best practices' are meaningless filler with no actionable information. | 1 / 3 |
Actionability | No concrete code, commands, or specific guidance is provided. The skill describes what it does in abstract terms but never shows how to actually partition data or provides any executable examples. | 1 / 3 |
Workflow Clarity | No workflow is defined. Despite claiming to provide 'step-by-step guidance,' there are no actual steps, sequences, or validation checkpoints for data partitioning tasks. | 1 / 3 |
Progressive Disclosure | The content is a flat, uninformative structure with no references to detailed materials, examples, or related documentation. The 'Related Skills' section mentions a category but provides no actual links or navigation. | 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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