pharmacology-wetlab

Computational analysis of pharmacology wet-lab experiments. Western blot densitometry, xenograft tumor growth inhibition, pharmaceutical stability modeling (Arrhenius), radiolabeled antibody biodistribution, MIRD dosimetry, and adverse event grading. For drug databases use chembl-database or fda-database; for molecular docking use diffdock.

Quality

70%

Does it follow best practices?

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Securityby

Passed

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tessl review fix ./backend/cli/skills/biology/pharmacology-wetlab/SKILL.md

Quality

Content

70%Weight 40%Scale 1-3

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

The content is highly actionable with complete executable code throughout, but it is verbose because it duplicates the bundled scripts inline without referencing them, and workflows lack explicit validation checkpoints despite operating on fragile fits and destructive/batch-style operations.

Suggestions

Replace the inline full implementations with concise snippets and point to the corresponding ./scripts/*.py files (e.g., 'See scripts/western_blot.py for the full pipeline') to improve both conciseness and progressive disclosure.

Add explicit validation checkpoints into the example workflows (e.g., 'Check R² > 0.95 before accepting the IC50; if the fit is poor, add points around the inflection') to lift workflow clarity to 3.

Dimension	Reasoning	Score
Conciseness	The body is largely efficient executable code with little concept-explanation padding, but at ~600 lines it inlines five large full-implementation functions that duplicate scripts already present as bundle files, so it could be tightened significantly.	2 / 3
Actionability	Every section provides complete, copy-paste-ready executable Python with realistic inputs (e.g., the four_pl curve_fit example with sample concentrations), matching the anchor for fully executable code and specific examples.	3 / 3
Workflow Clarity	Workflows are sequenced and troubleshooting/best-practices sections exist, but the example workflows omit explicit validation checkpoints (e.g., verifying R², checking fit reasonableness before reporting IC50) that the troubleshooting section implies are needed, capping clarity at 2.	2 / 3
Progressive Disclosure	Five implementation scripts exist in ./scripts/ but the body never references or links to them, instead inlining the full code; the bundle content that should be one level deep is duplicated inline, so structure could be much better organized.	2 / 3
	Total	9 / 12 Passed

Description

70%Weight 40%Scale 1-3

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 specific and well-differentiated with strong conflict-avoidance routing, but it lacks an explicit 'Use when...' trigger clause and relies on technical jargon over natural user phrasing. This caps completeness and trigger term quality at 2.

Suggestions

Add an explicit 'Use when...' trigger clause (e.g., 'Use when analyzing western blot images, xenograft tumor data, or pharmaceutical stability studies') to lift completeness to 3.

Weave in natural conversational terms a user would actually say ('IC50', 'shelf life', 'dose-response curve', 'tumor growth') alongside the technical ones to improve trigger term quality.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions ('Western blot densitometry, xenograft tumor growth inhibition, pharmaceutical stability modeling (Arrhenius), radiolabeled antibody biodistribution, MIRD dosimetry, and adverse event grading'), matching the anchor for listing several specific concrete actions.	3 / 3
Completeness	It clearly states what the skill does, but the when-to-use guidance is limited to routing away to other skills ('For drug databases use chembl-database... for molecular docking use diffdock') with no explicit 'Use when...' trigger for this skill, capping completeness at 2.	2 / 3
Trigger Term Quality	It includes domain-relevant terms but largely technical jargon ('MIRD dosimetry', 'Arrhenius', '%ID/g'); it lacks the plain conversational phrasings a user would naturally say and has no 'Use when...' trigger clause, so it does not reach good natural coverage.	2 / 3
Distinctiveness Conflict Risk	The niche is highly specific (pharmacology wet-lab assays) and the explicit routing to chembl-database, fda-database, and diffdock makes it clearly distinguishable and unlikely to trigger for the wrong skill.	3 / 3
	Total	10 / 12 Passed

Validation

75%

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 — 12 / 16 Passed

Validation for skill structure

Criteria	Description	Result
skill_md_line_count	SKILL.md is long (605 lines); consider splitting into references/ and linking	Warning
metadata_version	'metadata.version' is missing	Warning
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning
relative_links	Relative link issues: 1 missing	Warning

	Total	12 / 16 Passed

Repository: synthetic-sciences/openscience
Path: backend/cli/skills/biology/pharmacology-wetlab/SKILL.md
Commit: dc00e07

Reviewed: 7 days ago

Table of Contents

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