sanger-chromatogram-qa

Use sanger chromatogram qa for data analysis workflows that need structured execution, explicit assumptions, and clear output boundaries.

Quality

56%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide

Securityby

Passed

No findings from the security scan

Fix and improve this skill with Tessl

tessl review fix ./scientific-skills/Data Analysis/sanger-chromatogram-qa/SKILL.md

Quality

Content

48%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 dominated by generic template scaffolding (risk tables, security checklists, lifecycle status, self-referential cross-references) that crowds out the small amount of domain content (use cases, parameters, returns). The runnable commands are real but documented parameters contradict the packaged script's actual interface, and no reference files exist despite one being cited.

Suggestions

Cut the duplicated sections: the description appears twice verbatim, Audit-Ready Commands repeats Quick Check and Example Usage, and 'See ## X above' placeholders in Dependencies/Implementation Details should be replaced by the single authoritative section.

Align documentation with the actual script: document --ab1/--reference/--demo as they exist in scripts/main.py, remove the nonexistent CONFIG block reference, and add one real usage example (e.g. python scripts/main.py --ab1 sample.ab1 --reference ATCG...) once the script supports AB1 parsing.

Delete or drastically compress the generic Risk Assessment, Security Checklist, Evaluation Criteria, and Lifecycle boilerplate — none of it is skill-specific knowledge — and either ship a real requirements.txt or drop the reference to it.

Dimension	Reasoning	Score
Conciseness	The body duplicates itself repeatedly: the frontmatter description is pasted verbatim twice ("Use this skill when the task needs Use sanger chromatogram qa for..." under When to Use, and again under Key Features); "Audit-Ready Commands" repeats the exact commands already in Quick Check and Example Usage; "Dependencies" and "Implementation Details" are placeholder cross-references ("See ## Prerequisites above", "See ## Workflow above"); and the Risk Assessment, Security Checklist, Evaluation Criteria, and Lifecycle sections are generic template boilerplate. This is 'noticeably verbose; several unnecessary explanations or padded sections' (2) — not 1, since it never explains concepts Claude already knows at length, but well below the 'mostly efficient' (3) anchor.	2 / 5
Actionability	There are concrete, runnable commands ("python -m py_compile scripts/main.py", "python scripts/main.py --help") and scripts/main.py exists, but key details are wrong: the documented parameters ("ab1_file", "expected_seq", "variant_pos") do not match the script's actual CLI (--ab1, --reference, --demo); "Edit the in-file CONFIG block" points to a block that does not exist in main.py; and the example cd path ("20260318/scientific-skills/Data Analytics/sanger-chromatogram-qa") is a workspace-specific artifact. The mismatches go beyond anchor 4's 'minor gaps', matching anchor 3's 'concrete guidance but incomplete... missing key details'.	3 / 5
Workflow Clarity	The Workflow section gives a clear 5-step sequence with an explicit validation checkpoint ("Use this command to verify that the packaged script entry point can be parsed before deeper execution") and an error-recovery path ("If scripts/main.py fails, report the failure point, summarize what still can be completed safely, and provide a manual fallback"). This meets anchor 4 ('clear sequence with most checkpoints present; minor validation gaps') but not 5, because the workflow is generic process scaffolding (confirm inputs, run script, return structured result) rather than domain-specific steps with a validate-fix-retry loop over the chromatogram analysis itself.	4 / 5
Progressive Disclosure	The actual bundle is scripts/main.py only (no references/ or assets/ directories); the body's reference to scripts/main.py is accurate, but "Declared in requirements.txt" points at a file that does not exist in the bundle. All content lives inline in a ~190-line SKILL.md whose bulk is template boilerplate that serves no navigational purpose — 'content that should be separate is inline' and a dangling reference match anchor 3 rather than the 'good structure; references mostly clear' of anchor 4.	3 / 5
	Total	12 / 20 Passed

Description

45%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.

The description is a template-generated sentence that swaps in the skill name but describes a generic process (structured execution, bounded scope) rather than the actual Sanger chromatogram QA capabilities. Its only distinctive content is the domain name itself; what it does, when to use it, and its trigger vocabulary are all underspecified.

Suggestions

State the concrete capabilities in third person, e.g. 'Quality-checks Sanger sequencing chromatograms: computes per-trace quality scores, detects mixed/heterozygous peaks, and compares reads against a reference sequence to confirm variants.'

Rewrite the 'when' clause around domain triggers users would actually say: 'Use when working with .ab1 trace files, Sanger sequencing reads, mutation verification, clone confirmation, or heterozygous SNP validation.'

Include the natural file-extension and synonym triggers (.ab1, chromatogram, trace, sequencing QC) that are currently absent.

Dimension	Reasoning	Score
Specificity	The description names the domain ("sanger chromatogram qa") but the only stated action is the circular "Use sanger chromatogram qa for data analysis workflows"; phrases like "structured execution, explicit assumptions, and clear output boundaries" are pure process abstraction with zero concrete capabilities (no quality scoring, peak detection, or variant confirmation). It sits between the 'entirely vague' (1) and 'names the domain but actions are minimal or generic' (2) anchors — the clear domain label keeps it off 1, but the absence of any real action keeps it below the midpoint.	2 / 5
Completeness	The 'what' is only a domain label with no stated actions, and the 'when' is explicit ("for data analysis workflows that need structured execution, explicit assumptions, and clear output boundaries") but generic process boilerplate rather than a domain trigger. It falls between anchors 2 and 3: both elements are present (ruling out 2's 'only when without what'), but neither is specific enough for anchor 4.	3 / 5
Trigger Term Quality	"sanger" and "chromatogram" are natural terms a user doing Sanger sequencing QA would say, but common variations are missing: no "ab1"/".ab1", "trace", "sequencing", or "heterozygous peak". This matches the 'some relevant keywords but missing common variations or synonyms' anchor rather than the 'good keyword coverage' (4) anchor.	3 / 5
Distinctiveness Conflict Risk	"sanger chromatogram" carves a distinct niche, but the trigger clause "data analysis workflows that need structured execution, explicit assumptions, and clear output boundaries" would fire for almost any data-analysis request, creating real overlap risk with any sibling skill sharing this template. 'Somewhat specific but could still overlap with similar skills' fits; it lacks the 'clear niche with distinct triggers' needed for 4-5.	3 / 5
	Total	11 / 20 Passed

Validation

93%

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 — 15 / 16 Passed

Validation for skill structure

Criteria	Description	Result
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	15 / 16 Passed

Repository: aipoch/medical-research-skills
Path: scientific-skills/Data Analysis/sanger-chromatogram-qa/SKILL.md
Commit: 686e09d

Reviewed: 13 hours ago

Table of Contents

Discovery Implementation Validation

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.