Content
70%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.
The body excels at actionability and workflow design — concrete tool calls, a validated-codes table, explicit validation steps, and fallback chains make it easy to execute correctly. Its weaknesses are token efficiency and structure: a long inline domain primer explains oncology concepts Claude already knows and would be better split into a reference file.
Suggestions
Move the 'Tumor Classification Reasoning (CRITICAL)' domain background (staging vs grading, histological vs molecular axes, biomarker interpretation, actionability levels) into a references/ file such as references/tumor-classification.md, keeping in SKILL.md only the tool-relevant guidance (e.g., 'pass the validated OncoTree code to OncoKB') plus a one-line pointer — this fixes both conciseness and progressive disclosure.
Delete or compress the passages that restate textbook knowledge, e.g. the TNM definition ('T=tumor size, N=lymph nodes, M=metastasis') and the Grade 1-3 explanation, down to a single line each since they drive no tool calls.
Document the actual response envelope (e.g., whether results come back as `results["data"]` and whether `OncoTree_get_type` returns a `status` field) in the Phase 1/2 field lists so the Python patterns are copy-paste consistent with the documented schema.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The tool sections are lean (param tables, verified codes, fallback chains), but the "Tumor Classification Reasoning (CRITICAL)" section spends roughly 70 lines on textbook oncology Claude already knows — "Stage (TNM): How far has it spread? T=tumor size, N=lymph nodes, M=metastasis", "Grade: How abnormal do the cells look?", and the histological-vs-molecular primer — which goes beyond the 'minor trims' of the anchor above. | 3 / 5 |
Actionability | Concrete executable calls with parameters appear throughout — `OncoTree_search(query="breast cancer")`, `OncoKB_annotate_variant(gene="EGFR", variant="L858R", tumor_type="LUAD")`, a verified-codes table, and fallback chains — covering the common cases. The minor gap is that the Python patterns rely on a response shape (`results["data"][0]["code"]`, `detail["status"] == "success"`) that is not documented in the Phase 1/2 response-field descriptions. | 4 / 5 |
Workflow Clarity | The four-phase workflow is clearly sequenced with an explicit validation checkpoint ("Always validate via `OncoTree_get_type` before using in downstream tools"), an evidence-grading table, and explicit error-recovery feedback loops (404 on an alias falls back to `OncoTree_search`; no results falls back to tissue-level search), matching the top anchor. No destructive or batch operations, so no cap applies. | 5 / 5 |
Progressive Disclosure | No bundle files exist (no references/, scripts/, or assets/), and everything — including the separable oncology domain primer (staging vs grading, biomarker interpretation, actionability levels) — is inlined in a single ~190-line SKILL.md. Internal section headers are well organized, but content that clearly belongs in a reference file is inline with no references to signal, matching the 'some structure' anchor rather than the 'most content appropriately placed' anchor. | 3 / 5 |
Total | 15 / 20 Passed |