Content
93%Weight 40%Scale 1-5Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
An excellent, tightly written skill body: decision tables drive model selection, an executable example anchors the invocation pattern, and gotchas plus honest limitations anticipate real misuse. The only gap is the absence of an explicit post-run validation/feedback loop, which keeps workflow clarity just below top marks.
Suggestions
Add a brief post-run sanity check in Step 3 (e.g., verify the reported score lies in the expected range and that inputs were on the inhibition scale before interpreting), turning the gotchas list into a closed feedback loop.
Show one more complete `tu run` invocation for a matrix-based tool (ZIP) so the two most data-intensive workflows both have copy-paste examples with expected output.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Nearly all guidance is delivered via dense tables (model-by-data selection, null-model meanings, score thresholds) with zero filler; no background concepts Claude already knows are re-explained. The gotcha "Scale mismatch — convert first (Step 0)" cross-references instead of repeating. Every section earns its tokens, matching the lean anchor. | 5 / 5 |
Actionability | A copy-paste-ready `tu run DrugSynergy_calculate_bliss` invocation with expected output ("expected 0.58, bliss_synergy_score 0.12") demonstrates the call pattern, and the Step 0 table supplies the exact tool name and input parameter names for all five tools, making every command fully constructible. Concrete numeric interpretation thresholds (>+10 synergy, CI<1) and a real helper script (`scripts/synergy_reference.py`, present in the bundle) complete it. | 5 / 5 |
Workflow Clarity | Steps 0–4 form a clear, data-driven decision sequence with the scale-conversion checkpoint ("inhibition = 1 − viability/100") and anticipated failure modes ("or the Hill fit fails (the tools say so)"). It falls just short of the level-5 anchor because there is no explicit post-run validation/feedback loop (e.g., sanity-check the computed score against the expected range before reporting), and it is above level 3 since checkpoints like the scale-consistency check are explicit rather than implicit. | 4 / 5 |
Progressive Disclosure | The SKILL.md is a well-organized, self-contained overview whose only bundle file — `scripts/synergy_reference.py` — is real, clearly signaled, and one level deep. No content that belongs in a separate file is inlined, and the Related-skills section aids navigation without nesting. | 5 / 5 |
Total | 19 / 20 Passed |