Content
65%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The content is highly actionable with concrete tool calls and well-sequenced phases, but it is a dense monolithic document with some redundancy across sections and no explicit validation checkpoints or external reference files.
Suggestions
Add explicit validation/checkpoint steps (e.g., verify a returned accession exists before fetching files; confirm cCRE coordinates resolve before annotating) and a validate→fix→retry loop for risky operations to raise workflow clarity.
Move the Tool Parameter Reference table and the sequence-based deep-learning model details into separate reference files (e.g., REFERENCE.md, MODELS.md) and link to them from the body to improve progressive disclosure.
Trim redundancy by keeping each tool's parameters in one place rather than repeating them across the Key Tools table, the Workflow examples, and the Tool Parameter Reference table.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is dense and mostly efficient, but tool information is restated across the Key Tools table, the Tool Parameter Reference table, and the Workflow code blocks, and the Domain Reasoning section explains concepts Claude largely already knows; it could be tightened without losing clarity. | 2 / 3 |
Actionability | It provides concrete, executable tool calls with exact parameter values, example rsIDs/matrix_ids, documented response fields, and copy-paste-ready code blocks across every phase — fully actionable guidance. | 3 / 3 |
Workflow Clarity | Phases 1–4 are clearly sequenced and a Fallback Strategies table exists, but there are no explicit validation/checkpoint steps or validate→fix→retry feedback loops for risky operations, so checkpoints remain implicit. | 2 / 3 |
Progressive Disclosure | It is a well-organized single file with clear one-level sections and no nested references, but it is a long monolithic document (~330 lines) where the Tool Parameter Reference and deep-learning model details could be split into separate bundle files rather than kept inline. | 2 / 3 |
Total | 9 / 12 Passed |