Conciseness

The body is mostly efficient — domain-specific rules (sequon context, S/T-density heuristic, best practices, troubleshooting) earn their tokens — but it could be tightened: the Quick Start re-implements a simplified find_n_glycosylation_sites that Core Capability 1 then defines again in fuller form, and ~250 lines of inline code largely duplicate the bundled scripts/predict_glycosylation.py. This fits 'mostly efficient but includes some unnecessary content or could be tightened' rather than the noticeably-padded anchor 2 or the lean anchor 4/5.

Actionability

The guidance is largely fully executable: complete functions with docstrings, a real EPO example, two concrete workflows, and a runnable tool catalog. It falls short of 5 on minor gaps: scan_fasta_for_n_glyc imports pandas although installation only installs biopython and numpy, and predict_o_glycosylation_hotspots returns a bare [] for sequences shorter than window_size, which breaks the tuple unpacking in analyze_glycoprotein — so copy-paste code can fail on short inputs.

Workflow Clarity

The Typical Workflows section gives clear, correctly sequenced single-step examples, and Workflow 2 (comparing predictions against UniProt annotations) plus Best Practice 3 ("Validate with UniProt") provide an explicit validation checkpoint, matching 'clear sequence with most checkpoints present'. It does not reach 5 because there is no feedback loop (what to do when comparison fails or hotspots over-predict) beyond scattered troubleshooting entries.

Progressive Disclosure

Section structure is good (When to Use, Installation, Quick Start, Core Capabilities, Workflows, Best Practices, Troubleshooting, Resources), but the actual bundle is ignored: scripts/predict_glycosylation.py (422 lines, a full CLI tool) is never mentioned anywhere in the body, while equivalent logic is re-inlined as ~250 lines of code. This matches 'some structure but content that should be separate is inline and references are not signaled'; it is above 2 only because the section organization itself is solid and easy to navigate.

Specificity

The description lists several concrete actions — "N-glycosylation motif finding", "O-glycosylation hotspot prediction", "glycan structure resources" — which match the anchor for several specific actions with minor gaps. It falls short of a 5 because "glycan structure resources" is a resource pointer rather than a concrete action, and capabilities present in the body (combined glycoprotein analysis, comparison against UniProt annotations) are omitted; it is above a 3 because more than 1-2 concrete actions are named.

Completeness

The 'what' is clearly stated (three named capabilities plus "Lightweight, pure Python"), but there is no 'Use when...' clause or equivalent trigger guidance for this skill — the routing sentences ("For protein function queries use uniprot-database; for structure analysis use alphafold-database") point away to other skills rather than stating when to use this one. Per the judging guideline, a missing explicit trigger clause caps completeness at 3 even with a clear 'what'.

Trigger Term Quality

Terms like "glycosylation site prediction", "N-glycosylation", "O-glycosylation", and "glycobiology" are exactly the natural phrases a user would say, giving good keyword coverage. A 5 would require fuller synonym coverage (e.g., "glycoprotein", "glycan analysis", "sequon"), and common variations are missing, so it sits between the 'good coverage' and 'comprehensive' anchors, closer to 4.

Distinctiveness Conflict Risk

Glycobiology is a clear niche with distinct domain triggers (glycosylation, glycan), and the explicit routing to uniprot-database and alphafold-database actively reduces overlap risk with the most closely related skills. It clearly matches the 'clear niche with distinct triggers; minimal conflict risk' anchor; nothing pushes it toward 4 since the boundary disambiguation is already explicit.