lab-budget-forecaster

Use lab budget forecaster for data analysis workflows that need structured execution, explicit assumptions, and clear output boundaries.

Quality

43%

Does it follow best practices?

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Adds up to 20 points to the overall score

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Securityby

Passed

No findings from the security scan

Fix and improve this skill with Tessl

tessl review fix ./scientific-skills/Data Analysis/lab-budget-forecaster/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 skill body offers a clear, validated workflow and a real executable entry point, but is weighed down by heavy templated padding and a parameter table that contradicts the actual script's CLI. Consolidating redundant command sections and reconciling the parameter documentation would materially improve it.

Suggestions

Reconcile the Parameters table with the actual `scripts/main.py` arguments (`--budget/--start/--end/--expenses`) so documented commands are copy-paste accurate.

Collapse the redundant Example Usage, Quick Check, and Audit-Ready Commands sections into a single concise verification block.

Remove generic boilerplate (Lifecycle Status, Evaluation Criteria, Security Checklist) that does not add skill-specific guidance.

Dimension	Reasoning	Score
Conciseness	The body is noticeably padded with redundant templated sections — Example Usage, Quick Check, and Audit-Ready Commands repeat `python -m py_compile scripts/main.py` and `--help` three times, and Dependencies/Implementation Details add little beyond 'See ## above' pointers.	2 / 5
Actionability	Concrete commands and a parameter table are present, but the documented Parameters (`--current-balance`, `--monthly-burn`, `--upcoming-costs`) do not match the actual script arguments (`--budget`, `--start`, `--end`, `--expenses`), making the guidance partly inaccurate.	3 / 5
Workflow Clarity	A clear numbered workflow with validation checkpoints is present (validate scope early in step 2, fallback path in step 5), with only minor gaps; the destructive-operation cap does not apply to this read-only forecaster.	4 / 5
Progressive Disclosure	The single bundle file `scripts/main.py` is real and one-level deep, but most reference material is inlined in the monolithic SKILL.md rather than split into separate files, leaving organization only partially effective.	3 / 5
	Total	12 / 20 Passed

Description

37%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 domain-named but capability-vague, relying on generic process language rather than concrete forecasting actions. It provides a weak 'when' but a circular 'what', limiting its trigger usefulness.

Suggestions

Replace abstract phrases with concrete actions, e.g. 'Forecasts lab grant runway from balance and monthly burn rate, flags depletion dates and cost-cutting scenarios.'

Add natural user trigger terms such as 'budget', 'runway', 'burn rate', 'grant', or 'fund depletion'.

Remove the circular 'Use lab budget forecaster' restatement and state what the skill actually produces.

Dimension	Reasoning	Score
Specificity	Names the domain ('lab budget forecaster') but relies on abstract phrases like 'structured execution, explicit assumptions, and clear output boundaries' with no concrete forecasting actions (e.g., burn rate, runway, depletion).	2 / 5
Completeness	A 'when' clause exists ('for data analysis workflows that need...') but the 'what' is circular ('Use lab budget forecaster'), restating the skill name rather than describing concrete capability.	3 / 5
Trigger Term Quality	Uses generic terms ('data analysis workflows', 'output boundaries') and omits natural user phrases such as budget, runway, grant, or burn rate that would actually trigger this skill.	2 / 5
Distinctiveness Conflict Risk	The phrasing around 'data analysis workflows' and 'clear output boundaries' is broad and could overlap with many analytics skills, though the 'lab budget' niche adds some distinction.	3 / 5
	Total	10 / 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/lab-budget-forecaster/SKILL.md
Commit: f5ef65b

Reviewed: ~3 hours ago

Table of Contents

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