labarchive-integration

Converts LabArchives notebook data, entry metadata, and authorized ELN exports into manuscript-ready academic writing outputs such as Methods sections, data-availability statements, reproducibility appendices, experiment timelines, and submission support notes. Optional bundled scripts can be used to collect or validate source notebook data before writing.

Quality

72%

Does it follow best practices?

Impact

—

No eval scenarios have been run

Securityby

Advisory

Suggest reviewing before use

Optimize this skill with Tessl

npx tessl skill review --optimize ./scientific-skills/Academic Writing/labarchive-integration/SKILL.md

Quality

Content

62%

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 a clear workflow, strong safety boundaries, and explicit validation checkpoints. Its main weaknesses are the lack of concrete examples showing actual input-to-output transformations for the writing deliverables, and moderate verbosity in sections that could be consolidated. The references to bundle files that don't exist weaken the progressive disclosure score.

Suggestions

Add at least one concrete example showing a sample notebook input and the corresponding Methods draft or Data Availability Statement output, so Claude has a clear model to follow.

Consolidate 'Academic Writing Style Rules', 'Deterministic Rules', and the output contracts into a more compact format—consider a single table or a referenced file to reduce inline length.

Provide the referenced `assets/writing_outputs_template.md` as a bundle file, or inline a minimal template skeleton, so the skill is self-contained.

Dimension	Reasoning	Score
Conciseness	The skill is reasonably well-structured but includes some redundancy—e.g., the 'When to Use' and 'When Not to Use' sections overlap with the workflow and refusal contract, and the output contracts repeat information that could be more tightly expressed. Some sections like 'Deterministic Rules' and 'Academic Writing Style Rules' could be consolidated. However, it largely avoids explaining concepts Claude already knows.	2 / 3
Actionability	The skill provides concrete script commands and a structured workflow, but the writing outputs themselves are described as checklists of what 'must include' rather than providing executable examples or templates with sample input/output. The actual academic writing guidance is directional rather than demonstrative—no example Methods draft or Data Availability Statement is shown.	2 / 3
Workflow Clarity	The 5-step workflow is clearly sequenced with explicit validation at steps 1 and 5, a refusal/recovery contract for when the workflow cannot proceed, a dry-run recommendation before live execution, and a completion checklist. The feedback loop for script failures is also addressed. This is a well-structured multi-step process with appropriate checkpoints.	3 / 3
Progressive Disclosure	The skill references external files like `assets/writing_outputs_template.md` and bundled scripts, which is good progressive disclosure in principle. However, no bundle files are provided, so these references are unverifiable. The SKILL.md itself is fairly long (~180 lines of substantive content) and some of the output contract details could be offloaded to a referenced template file rather than being inline.	2 / 3
	Total	9 / 12 Passed

Description

82%

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 description with excellent specificity and distinctiveness, clearly naming the input sources (LabArchives, ELN exports) and multiple concrete output types (Methods sections, data-availability statements, etc.). Its main weakness is the absence of an explicit 'Use when...' clause, which would help Claude know precisely when to select this skill over others. The trigger terms are naturally phrased and domain-appropriate.

Suggestions

Add an explicit 'Use when...' clause, e.g., 'Use when the user asks to turn LabArchives or ELN notebook data into manuscript sections, or mentions writing Methods, data-availability statements, or reproducibility documentation from lab notebook exports.'

Dimension	Reasoning	Score
Specificity	Lists multiple specific concrete actions: converting notebook data into Methods sections, data-availability statements, reproducibility appendices, experiment timelines, and submission support notes. Also mentions bundled scripts for collecting/validating source data.	3 / 3
Completeness	The 'what' is thoroughly covered with specific outputs and input types. However, there is no explicit 'Use when...' clause or equivalent trigger guidance telling Claude when to select this skill, which caps this dimension at 2 per the rubric guidelines.	2 / 3
Trigger Term Quality	Includes strong natural keywords users would say: 'LabArchives', 'notebook', 'ELN', 'manuscript', 'Methods sections', 'data-availability statements', 'reproducibility', 'experiment timelines', 'submission'. These cover the domain well and match how researchers would phrase requests.	3 / 3
Distinctiveness Conflict Risk	Highly distinctive due to the specific combination of LabArchives/ELN as input source and manuscript-ready academic writing as output. This niche is unlikely to conflict with general writing or general data processing skills.	3 / 3
	Total	11 / 12 Passed

Validation

90%

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 — 10 / 11 Passed

Validation for skill structure

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

	Total	10 / 11 Passed

Repository: aipoch/medical-research-skills
Commit: d915031

Reviewed: 5 days ago

Table of Contents

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