fastqc-report-interpreter
Use when analyzing FASTQC quality reports from sequencing data, identifying quality issues in NGS datasets, or troubleshooting sequencing problems. Interprets quality metrics and provides actionable recommendations for RNA-seq, DNA-seq, and ChIP-seq data.
Install with Tessl CLI
npx tessl i github:aipoch/medical-research-skills --skill fastqc-report-interpreter85
Does it follow best practices?
If you maintain this skill, you can automatically optimize it using the tessl CLI to improve its score:
npx tessl skill review --optimize ./path/to/skillValidation for skill structure
Discovery
89%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 a strong skill description with excellent trigger term coverage and completeness. The 'Use when' clause is well-structured with multiple specific scenarios. The main weakness is that the capabilities could be more concrete - listing specific quality metrics or analysis actions would strengthen specificity.
Suggestions
Add specific concrete actions like 'analyze per-base quality scores, detect adapter contamination, assess GC content, identify overrepresented sequences' to improve specificity
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Names the domain (FASTQC quality reports, NGS datasets) and some actions (interprets quality metrics, provides recommendations), but doesn't list specific concrete actions like 'analyze per-base quality scores, detect adapter contamination, assess GC content distribution'. | 2 / 3 |
Completeness | Explicitly answers both what ('Interprets quality metrics and provides actionable recommendations') and when ('Use when analyzing FASTQC quality reports...identifying quality issues...troubleshooting sequencing problems'). Has clear 'Use when' clause with explicit triggers. | 3 / 3 |
Trigger Term Quality | Excellent coverage of natural terms users would say: 'FASTQC', 'quality reports', 'sequencing data', 'NGS', 'RNA-seq', 'DNA-seq', 'ChIP-seq', 'quality issues', 'sequencing problems'. These are terms bioinformaticians naturally use. | 3 / 3 |
Distinctiveness Conflict Risk | Very clear niche with distinct triggers specific to FASTQC and NGS sequencing analysis. Unlikely to conflict with other skills due to highly specialized domain terminology (FASTQC, RNA-seq, DNA-seq, ChIP-seq). | 3 / 3 |
Total | 11 / 12 Passed |
Implementation
72%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a well-organized skill with excellent conciseness and structure. The main weaknesses are the reliance on a custom module that may not be available (reducing actionability) and the lack of explicit validation steps for batch operations. The content effectively covers FASTQC interpretation with clear metrics tables and issue diagnosis.
Suggestions
Add validation steps for batch analysis (e.g., 'Verify output file exists and contains expected number of samples before proceeding')
Either provide the FASTQCInterpreter implementation or replace with executable code using standard libraries like parsing HTML/text files directly
Include a feedback loop for handling failed samples in batch processing (e.g., 'If analysis fails: check file format, verify FASTQC completed successfully, retry with verbose logging')
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is lean and efficient, presenting information in tables and code blocks without explaining basic concepts Claude would already know. Every section serves a clear purpose with no padding. | 3 / 3 |
Actionability | Provides concrete code examples and CLI commands, but the code references a custom module (FASTQCInterpreter) that may not exist or be available. The examples are illustrative rather than truly executable without the underlying implementation. | 2 / 3 |
Workflow Clarity | The skill presents clear capabilities and issue diagnosis but lacks explicit validation checkpoints or feedback loops. For batch operations, there's no guidance on verifying results or handling failures during processing. | 2 / 3 |
Progressive Disclosure | Well-structured with a quick start, organized sections for different capabilities, and appropriate reference to external documentation (troubleshooting.md) for detailed platform-specific issues. Navigation is clear and one level deep. | 3 / 3 |
Total | 10 / 12 Passed |
Validation
100%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 11 / 11 Passed
Validation for skill structure
No warnings or errors.
- Reviewed
Table of Contents
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