lab-inventory-predictor

Predict depletion time of critical lab reagents based on experimental usage frequency and automatically generate purchase alerts for laboratory inventory management.

1.04x

Quality

47%

Does it follow best practices?

Impact

95%

1.04x

Average score across 3 eval scenarios

Securityby

Passed

No known issues

Optimize this skill with Tessl

npx tessl skill review --optimize ./scientific-skills/Data analysis/lab-inventory-predictor/SKILL.md

Quality

Discovery

67%

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 excels at specificity and carves out a clear, distinctive niche in laboratory inventory management. However, it lacks explicit trigger guidance ('Use when...') and could benefit from additional natural keywords users might employ when needing this functionality.

Suggestions

Add a 'Use when...' clause with trigger scenarios like 'when tracking lab supplies', 'when reagents are running low', or 'when planning chemical purchases'

Include additional natural trigger terms users might say: 'reorder', 'stock levels', 'consumables', 'chemical inventory', 'supplies running out'

Dimension	Reasoning	Score
Specificity	Lists multiple specific concrete actions: 'predict depletion time', 'based on experimental usage frequency', and 'automatically generate purchase alerts'. These are clear, actionable capabilities.	3 / 3
Completeness	Clearly answers 'what' (predict depletion, generate alerts for lab inventory), but lacks an explicit 'Use when...' clause or trigger guidance. The when is only implied through the domain context.	2 / 3
Trigger Term Quality	Contains relevant domain terms like 'reagents', 'lab', 'laboratory inventory management', 'purchase alerts', but missing common variations users might say like 'reorder', 'stock', 'supplies running low', 'chemical inventory', or 'consumables'.	2 / 3
Distinctiveness Conflict Risk	Highly specific niche combining lab reagents, depletion prediction, and purchase alerts. Unlikely to conflict with generic inventory or alerting skills due to the specialized laboratory context.	3 / 3
	Total	10 / 12 Passed

Implementation

27%

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

This skill content is heavily padded with boilerplate sections (Risk Assessment, Security Checklist, Evaluation Criteria, Lifecycle Status, Prerequisites) that provide no actionable guidance for Claude. While it includes concrete code examples and parameter documentation, the referenced tools/modules don't appear to exist, making the examples illustrative rather than executable. The content would benefit significantly from removing template sections and focusing on actual implementation guidance.

Suggestions

Remove boilerplate sections (Risk Assessment, Security Checklist, Evaluation Criteria, Lifecycle Status, Version History, Author/License) that don't help Claude execute the skill

Either provide actual implementation code for the InventoryPredictor class or clarify that Claude should create this functionality from scratch with a minimal working example

Add a clear end-to-end workflow showing the typical sequence: initialize → add reagents → record usage over time → check alerts → generate report

Move detailed parameter tables and data structure documentation to a separate REFERENCE.md file, keeping only essential quick-start content in the main skill

Dimension	Reasoning	Score
Conciseness	Extremely verbose with extensive boilerplate sections (Risk Assessment, Security Checklist, Evaluation Criteria, Lifecycle Status) that add no instructional value. Includes unnecessary metadata like version history, author, and license that Claude doesn't need to execute the skill.	1 / 3
Actionability	Provides concrete CLI commands and Python API examples with executable code, but the code references a non-existent module ('skills.lab_inventory_predictor') and CLI tool ('openclaw skill') without explaining how to actually implement or access them. The examples are illustrative rather than truly executable.	2 / 3
Workflow Clarity	Individual actions are documented but there's no clear end-to-end workflow showing how to set up inventory tracking from scratch. Missing validation steps - no guidance on what to do if predictions seem wrong or how to verify data integrity.	2 / 3
Progressive Disclosure	Monolithic wall of text with no references to external files. All content is inline including detailed parameter tables, data structures, and algorithms that could be split into reference documents. The document is over 150 lines with no clear hierarchy for quick scanning.	1 / 3
	Total	6 / 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: ca9aaa4

Reviewed: 27 days ago

Table of Contents

Discovery Implementation Validation

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