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.

Quality

61%

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Securityby

Passed

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tessl review fix ./scientific-skills/Data Analysis/fastqc-report-interpreter/SKILL.md

Quality

Content

31%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The body is padded with redundant restatements of the description and generic process boilerplate, and its code/CLI examples do not match the actual bundled script, making the actionable guidance misleading. Workflow and disclosure are undermined by broken file references and abstract rather than concrete steps.

Suggestions

Replace the Quick Start, Core Capabilities, and CLI Usage examples with the real API from scripts/main.py (class FastQCInterpreter, methods interpret_report/interpret_module/get_recommendations, flags --report/--demo) so examples are copy-paste executable.

Remove the verbatim description repeats in 'When to Use' and 'Key Features' and collapse the overlapping Implementation Details / Workflow / Output Requirements / Error Handling / Input Validation / Response Template sections into a single concise workflow.

Fix or remove broken references: either add references/troubleshooting.md and scripts/fastqc_interpreter.py to the bundle, or drop the pointers and inline only what exists.

Dimension	Reasoning	Score
Conciseness	Noticeably verbose: the description is repeated verbatim inside 'When to Use' and 'Key Features', and multiple overlapping sections (Implementation Details, Workflow, Output Requirements, Error Handling, Input Validation, Response Template) restate the same generic process boilerplate.	2 / 5
Actionability	While `python -m py_compile scripts/main.py` and `--help` are real, the substantive code examples reference things that do not exist in the bundle: `FASTQCInterpreter` (actual class is `FastQCInterpreter`), `analyze()`/`parse_metrics()`/`diagnose_issues()`/`analyze_batch()`, `scripts/fastqc_interpreter.py`, and CLI flags `--input`/`--batch` (actual flags are `--report`/`--demo`).	2 / 5
Workflow Clarity	A 5-step Workflow with explicit stop-early and fallback checkpoints exists, but the steps are abstract process guidance rather than tied to the actual script, and the batch-analysis path lacks concrete validation of batch inputs.	3 / 5
Progressive Disclosure	The only bundle file is scripts/main.py, yet the body references missing files (references/troubleshooting.md, scripts/fastqc_interpreter.py); structure exists via headers but navigation points to non-existent paths, and inlined reference-style content could be split out.	2 / 5
	Total	9 / 20 Passed

Description

92%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A strong, third-person description that clearly states both capabilities and trigger conditions with concrete domain-specific language. Minor improvement possible by adding file-extension triggers like .html or fastqc_data.txt.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions (analyzing FASTQC reports, identifying quality issues, troubleshooting, interpreting metrics, providing recommendations) across RNA-seq/DNA-seq/ChIP-seq, giving comprehensive coverage.	5 / 5
Completeness	Explicitly answers both 'what' ('Interprets quality metrics and provides actionable recommendations') and 'when' ('Use when analyzing FASTQC quality reports...') with concrete trigger phrases.	5 / 5
Trigger Term Quality	Strong natural keywords ('FASTQC quality reports', 'sequencing data', 'NGS datasets', 'RNA-seq/DNA-seq/ChIP-seq') but missing file extensions (.html, fastqc_data.txt) and common synonyms a user might say.	4 / 5
Distinctiveness Conflict Risk	Clear niche (FASTQC/NGS quality-control report interpretation) with distinct triggers and minimal overlap risk with other skills.	5 / 5
	Total	19 / 20 Passed

Validation

87%

Warnings & errors only

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 14 / 16 Passed

Validation for skill structure

Criteria	Description	Result
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning
referenced_paths_exist	Referenced path issues: 4 missing	Warning

	Total	14 / 16 Passed

Repository: aipoch/medical-research-skills
Path: scientific-skills/Data Analysis/fastqc-report-interpreter/SKILL.md
Commit: 63c61d3

Reviewed: 8 hours ago

Table of Contents

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