compression-optimizer
Compression Optimizer - Auto-activating skill for Data Pipelines. Triggers on: compression optimizer, compression optimizer Part of the Data Pipelines skill category.
31
0%
Does it follow best practices?
Impact
85%
1.07xAverage 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/compression-optimizer/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 is an extremely weak description that functions more as a label than a useful skill description. It provides no concrete actions, no natural trigger terms, no 'Use when' guidance, and no distinguishing details. It would be nearly impossible for Claude to correctly select this skill from a pool of available skills.
Suggestions
Add specific concrete actions the skill performs, e.g., 'Selects optimal compression algorithms (gzip, snappy, zstd, lz4) for data pipeline stages, configures compression settings, and benchmarks compression ratios vs throughput.'
Add an explicit 'Use when...' clause with natural trigger terms, e.g., 'Use when the user asks about compressing data in pipelines, reducing storage costs, choosing compression codecs, optimizing parquet/avro file sizes, or improving pipeline throughput.'
Remove the duplicate trigger term ('compression optimizer' is listed twice) and replace with varied natural language terms users would actually use, such as 'compress', 'file size', 'codec', 'storage optimization', 'data reduction'.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description provides no concrete actions whatsoever. 'Compression Optimizer' is a label, not a description of capabilities. There are no specific actions like 'compresses files', 'selects optimal compression algorithms', or 'reduces data pipeline throughput costs'. | 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. The 'Triggers on' line is just the skill name repeated. | 1 / 3 |
Trigger Term Quality | The only trigger terms listed are 'compression optimizer' repeated twice. These are not natural keywords a user would say — users would more likely say 'compress data', 'reduce file size', 'gzip', 'optimize storage', 'pipeline performance', etc. | 1 / 3 |
Distinctiveness Conflict Risk | The description is too vague to be distinctive. 'Compression Optimizer' for 'Data Pipelines' could overlap with any number of data engineering, file management, or performance optimization skills. Nothing narrows its scope. | 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 whatsoever. It consists entirely of generic boilerplate that could apply to any topic—there is no compression-specific knowledge, no executable code, no concrete guidance, and no workflow. It provides zero value beyond what Claude already knows.
Suggestions
Add concrete, executable code examples for common compression scenarios (e.g., choosing between gzip/snappy/zstd in Spark pipelines, configuring Parquet compression codecs, benchmarking compression ratios vs speed).
Define a clear workflow for evaluating and implementing compression in a data pipeline, including steps like profiling data, selecting a codec, testing compression ratios, validating output integrity, and measuring throughput impact.
Remove all generic boilerplate sections ('When to Use', 'Example Triggers', 'Capabilities') and replace with actionable technical content such as decision matrices for compression algorithm selection and configuration snippets.
Add references to supporting files (e.g., a benchmark comparison table, codec configuration examples) or inline concrete examples with specific tools like Spark, Airflow, or Kafka.
| 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 compression optimization, and wastes tokens on generic meta-descriptions like 'Provides step-by-step guidance' without actually providing any. | 1 / 3 |
Actionability | There is zero actionable content—no code, no commands, no algorithms, no configuration examples, no concrete guidance on compression formats, ratios, trade-offs, or pipeline integration. Every section is vague and abstract. | 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 | The content is a monolithic block of generic text with no references to supporting files, no structured navigation, and no bundle files to support it. There is nothing to progressively disclose because there is no substantive 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 | |
- Commit
172d892
- Reviewed
Table of Contents
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