Conciseness

Core workflow, parameter, and edge-field sections are efficient, but extended Domain Reasoning sections on multimeric valency, statistical binding factors, Hill/Scatchard plots, coiled-coil packing, and detergent effects read as textbook exposition partly tangential to the stated PPI-network-analysis purpose.

Actionability

Provides concrete tool function names, a parameter table with defaults, edge-field definitions, and one executable code snippet (IntAct metadata extraction); gaps include only a single code example and a referenced runnable-examples file that does not exist.

Workflow Clarity

The 4-phase workflow (identifier mapping, network retrieval, enrichment, optional structural) is clearly sequenced with a named tool per phase, though explicit validation checkpoints are absent (acceptable here since queries are read-only).

Progressive Disclosure

In-file section structure is reasonable, but references to python_implementation.py and KNOWN_ISSUES.md are unlinked backtick spans pointing to non-existent bundle files, and extensive domain-reasoning material is inlined rather than split into referenced files.

Specificity

Names multiple concrete actions — 'Distinguishes physical interactions (binding) from functional associations' plus 'interactome queries, complex partner identification, and pathway-level interaction analysis' — with comprehensive coverage of the domain.

Completeness

Explicitly answers both what (PPI network analysis across three databases, distinguishes interaction types) and when ('Use for interactome queries, complex partner identification, and pathway-level interaction analysis') with concrete trigger phrases.

Trigger Term Quality

Includes natural domain terms users would say ('Protein-protein interaction', 'PPI', 'interactome', 'binding') with synonyms and named databases (STRING, BioGRID, SASBDB).

Distinctiveness Conflict Risk

Clear niche — PPI network analysis with named databases — and distinct triggers yield minimal conflict risk with other skills.