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biopython-structure

Use Bio.PDB to parse and analyze protein structures (PDB/mmCIF) for structural bioinformatics tasks; use when you need structure parsing, geometry calculations, or structural comparison/superposition.

62

Quality

75%

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tessl review fix ./scientific-skills/Data Analysis/biopython-structure/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

62%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 content is highly actionable with a complete runnable example and specific API guidance, but it is held back by a generic, non-concrete validation workflow and by failing to link or surface the substantial references/structure.md file that already exists. Tightening the padded template sections would also improve token efficiency.

Suggestions

Link and surface references/structure.md from SKILL.md (e.g., under Implementation Details: "For full API coverage of parsing, geometry, DSSP, superposition, and IO, see references/structure.md") so the detailed reference is one level deep and clearly signaled.

Replace the generic Recommended Workflow validation steps with concrete checkpoints (e.g., a verify command or explicit JSON-schema/field checks) and add a validate->fix->retry loop for the batch neighbor-search/structure-writing path to lift workflow_clarity above 3.

Trim the padded template sections (Validation and Safety Rules, Failure Handling, Quick Validation) and remove restatements of obvious knowledge (e.g., that distances use Euclidean norm) to improve conciseness.

DimensionReasoningScore

Conciseness

The body is mostly efficient and avoids explaining what Bio.PDB is, but several sections restate obvious knowledge (e.g., "Distances are typically computed from atomic coordinates using Euclidean norm") and the template-style Validation/Output/Failure sections add padding that could be tightened, fitting the "mostly efficient but some unnecessary explanation" 3-anchor rather than the lean 5-anchor.

3 / 5

Actionability

A complete, copy-paste-ready Python example with a concrete config JSON and run command is provided, and the Implementation Details give specific, executable API guidance (NeighborSearch(...).search(center, radius, level=...), calc_angle/calc_dihedral) covering common cases, matching the fully executable top anchor.

5 / 5

Workflow Clarity

A Recommended Workflow is listed (validate inputs -> select path -> produce output -> final validation) but validation is described generically rather than as concrete checkpoints, and there is no real validate->fix->retry feedback loop for the batch-style neighbor-search/structure-writing operation; this fits the "steps listed but validation gaps" 3-anchor and is capped there by the missing explicit feedback loop.

3 / 5

Progressive Disclosure

The body is well section-organized, but a substantial reference file exists (references/structure.md) covering parsing, geometry, DSSP, superposition, and IO that is never linked or signaled from SKILL.md, so detailed material that belongs in a referenced file is inlined/unsurfaced; this matches the 3-anchor (some structure, references present but not clearly signaled) and not the well-signaled 4/5 anchors.

3 / 5

Total

14

/

20

Passed

Description

87%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 strong: it concisely states both what the skill does and when to use it, with domain-specific trigger terms that distinguish it from other skills. Minor improvements would add a few more natural synonyms (RMSD, alignment, distances) to widen trigger coverage.

DimensionReasoningScore

Specificity

"parse and analyze protein structures (PDB/mmCIF)" plus "structure parsing, geometry calculations, or structural comparison/superposition" list several concrete actions with only minor coverage gaps (e.g., no explicit mention of extraction/saving or neighbor search), so it sits above the 3-anchor but below the comprehensive 5-anchor.

4 / 5

Completeness

It explicitly answers "what" ("Use Bio.PDB to parse and analyze protein structures...") and "when" ("use when you need structure parsing, geometry calculations, or structural comparison/superposition") with concrete trigger phrases, matching the top anchor; it cannot reasonably be the level below where the "when" is weakly implied.

5 / 5

Trigger Term Quality

Includes natural terms like "PDB/mmCIF", "protein structures", "geometry calculations", and "structural comparison/superposition" with format synonyms, but omits a few common phrases a user might say (e.g., "RMSD", "alignment", "distances"), placing it just below the comprehensive 5-anchor.

4 / 5

Distinctiveness Conflict Risk

The narrow niche of Bio.PDB-based structural bioinformatics with format-specific triggers (PDB/mmCIF, superposition) gives it a clear niche with minimal overlap against other skills, matching the top anchor.

5 / 5

Total

18

/

20

Passed

Validation

87%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation14 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

referenced_paths_exist

Referenced path issues: 2 missing

Warning

Total

14

/

16

Passed

Repository
aipoch/medical-research-skills
Reviewed

Table of Contents

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