lab-inventory-predictor
Predict depletion time of critical lab reagents based on experimental usage frequency and automatically generate purchase alerts for laboratory inventory management.
Install with Tessl CLI
npx tessl i github:aipoch/medical-research-skills --skill lab-inventory-predictor56
Does it follow best practices?
If you maintain this skill, you can automatically optimize it using the tessl CLI to improve its score:
npx tessl skill review --optimize ./path/to/skillValidation for skill structure
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.
This description excels at specificity and carves out a clear, distinctive niche in laboratory inventory management. However, it lacks an explicit 'Use when...' clause that would help Claude know exactly when to select this skill, and could benefit from additional natural trigger terms users might employ when needing this functionality.
Suggestions
Add an explicit 'Use when...' clause, e.g., 'Use when tracking lab supplies, predicting when reagents will run out, or managing chemical inventory reorders.'
Include additional natural trigger terms users might say: 'reorder', 'running low', 'stock levels', 'chemical supplies', 'when to buy more'.
| 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 reagents), but lacks an explicit 'Use when...' clause. The 'when' is only implied through the domain context rather than explicitly stated. | 2 / 3 |
Trigger Term Quality | Contains relevant domain terms like 'lab reagents', 'experimental usage', 'purchase alerts', 'laboratory inventory management', but missing common variations users might say like 'reorder', 'stock', 'supplies running low', 'chemical inventory', or 'when to buy'. | 2 / 3 |
Distinctiveness Conflict Risk | Highly specific niche combining laboratory context, reagent tracking, depletion prediction, and purchase alerts. Unlikely to conflict with general inventory or alerting skills due to the specialized 'lab reagents' and 'experimental usage' terminology. | 3 / 3 |
Total | 10 / 12 Passed |
Implementation
35%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill is heavily padded with boilerplate sections (Risk Assessment, Security Checklist, Evaluation Criteria, Lifecycle Status) that consume significant tokens without providing actionable guidance. While it includes concrete code examples and clear parameter documentation, the referenced tools/modules don't appear to exist, making the examples theoretical rather than executable. The skill would benefit from dramatic trimming and focusing on what Claude actually needs to help users with inventory prediction.
Suggestions
Remove boilerplate sections (Risk Assessment, Security Checklist, Evaluation Criteria, Lifecycle Status, Version History, Author/License) that don't help Claude perform the task
Either provide actual implementation code or reframe as guidance for Claude to build this functionality, rather than documenting a non-existent tool
Add a clear workflow section showing the typical sequence: initialize → add reagents → record usage over time → check predictions/alerts
Include validation guidance: what to do when data is missing, how to handle edge cases like zero usage history or negative stock
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Extremely verbose with extensive boilerplate sections (Risk Assessment, Security Checklist, Evaluation Criteria, Lifecycle Status) that add no actionable value. Explains obvious concepts and includes redundant information like version history and author attribution that Claude doesn't need. | 1 / 3 |
Actionability | Provides concrete CLI commands and Python API examples with executable code snippets, 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 look executable but aren't practically usable. | 2 / 3 |
Workflow Clarity | Individual commands are clear, but there's no explicit workflow showing the sequence of operations (add reagent → record usage → check alerts). Missing validation steps for data integrity - what happens if you record usage for a non-existent reagent? No error recovery guidance. | 2 / 3 |
Progressive Disclosure | Content is organized into sections but everything is in one monolithic file. The detailed parameter tables, data structures, and algorithm explanations could be split into reference files. No links to external documentation for advanced usage. | 2 / 3 |
Total | 7 / 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 | |
- Reviewed
Table of Contents
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