Conciseness

Dense and purposeful — tables and formulas carry the load with little padding — but a few parenthetical asides ('the SynergyFinder default', 'classic isobologram-style') could be trimmed.

Actionability

Gives exact tool names, full input parameter lists per model, a copy-paste bash example with expected output, and a real helper script with usage — fully executable and covers the common single-dose case.

Workflow Clarity

Clear Step 0→4 sequence (pick model, understand additivity, run, interpret, gotchas) with a scale-consistency check and a cross-model agreement check via the script; minor validation gaps but the task is read-only analysis so destructive/batch caps do not apply.

Progressive Disclosure

Well-organized with clear section headers and a clearly signaled one-level-deep reference to scripts/synergy_reference.py (verified to exist) plus a Related skills section; appropriately self-contained for its size with no nested references.

Specificity

Names the domain (drug-combination synergy) and concrete actions across five named reference models (Bliss, HSA, Loewe, ZIP, Chou-Talalay CI), plus classification into synergistic/additive/antagonistic — comprehensive coverage.

Completeness

Explicitly answers both 'what' (quantify synergy using reference models; explains model choice, data needs, score reading) and 'when' ('Use when you have measured single-drug and combination effects ... and need a synergy score') with concrete trigger phrases.

Trigger Term Quality

Strong natural keyword coverage with synonyms (synergy/synergistic, additive/additivity, antagonistic, combination index, inhibition/viability), though a few common variants a user might say (e.g. 'drug interaction', 'isobologram') are absent.

Distinctiveness Conflict Risk

Clear niche with distinct triggers, reinforced by an explicit negative boundary ('NOT for looking up pre-computed synergy ... use the SYNERGxDB tool / cell-line-profiling skill') that minimizes overlap with sibling skills.