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.
77
72%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Advisory
Suggest reviewing before use
Optimize this skill with Tessl
npx tessl skill review --optimize ./scientific-skills/Academic Writing/labarchive-integration/SKILL.mdQuality
Discovery
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.). The 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 mentions LabArchives, electronic lab notebooks, ELN exports, or needs to convert notebook data into manuscript sections.'
| 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 per the rubric caps completeness at 2. | 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 is a clear niche unlikely to conflict with general writing or general data processing skills. | 3 / 3 |
Total | 11 / 12 Passed |
Implementation
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-structured skill with a clear workflow, strong safety boundaries, and explicit validation checkpoints. Its main weaknesses are the lack of concrete input/output examples for the writing deliverables and moderate verbosity in the output contract specifications that could be condensed or offloaded to a referenced file. The refusal contract and completion checklist are strong additions.
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.
Consider moving the detailed output contracts (Outputs A–D) into a referenced file like assets/output_contracts.md to reduce the main skill's token footprint while keeping the overview lean.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is reasonably well-structured but includes some sections that could be tightened. The 'When to Use' and 'When Not to Use' sections have some overlap with the refusal contract, and the output contracts repeat structural patterns that could be condensed. However, it avoids explaining concepts Claude already knows. | 2 / 3 |
Actionability | The skill provides concrete script commands and a clear refusal template, but the core writing guidance remains somewhat abstract—it describes what outputs 'must include' without providing concrete examples of actual generated text. No example input/output pairs are given for any of the five deliverables. | 2 / 3 |
Workflow Clarity | The five-step workflow is clearly sequenced with explicit validation checkpoints: authorization check at step 1, dry-run before live execution at step 2, a final safety pass at step 5, and a well-defined refusal/recovery contract for when the workflow cannot proceed. The feedback loop for script failures is also addressed. | 3 / 3 |
Progressive Disclosure | The skill references external files (assets/writing_outputs_template.md, bundled scripts) which is good progressive disclosure, but the main file itself is quite long with detailed output contracts that could potentially be split into a referenced template or appendix. The inline detail for all five output types makes the document heavier than necessary. | 2 / 3 |
Total | 9 / 12 Passed |
Validation
90%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
f82162b
- Reviewed
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