Content
82%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.
A strong operational skill document: fully executable commands, domain-specific notation and constraints Claude could not infer, explicit pre-flight confirmation checkpoints, and a verified external reference for output-format detail. The main gaps are mild verbosity in the HTML-serving section and inline placement of reference-style material (flags, acknowledgements) that keeps the main body long.
Suggestions
Move the platform-specific HTTP-serving command blocks (Linux/macOS and Windows PowerShell) into a reference file (e.g. references/serving.md) and keep only the key rule — serve on a random high port, never a raw file:// link — in the body, trimming the conciseness gap.
Add a short validate-and-retry step for script failures (e.g. what a preprocess or inference error looks like and how to fix common causes like invalid gap markup or inputs under 25 chars) to strengthen workflow feedback loops.
Relocate the full dataset acknowledgements and citation text to references/ (linked from a brief 'include these on first results' instruction) to reduce inline length while preserving the required attribution behavior.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Nearly every section carries operational weight Claude would not already know (markup notation, charset filters, restore constraints, timing estimates, presentation rules), with no concept-teaching padding. It falls short of a 5 because the dual-platform HTML-serving blocks and some phrasing (e.g. "Run the server in the foreground of the background task process (without trailing `&`) to prevent the process from being terminated") could be tightened or moved out of the main body. | 4 / 5 |
Actionability | Commands are copy-paste ready across all common task combinations (attribution+restore, attribution+contextualize, all tasks, dashboard generation, both Linux and Windows serving), and every flag is documented with default and semantics. Presentation guidance is equally concrete ("Bold the restored characters in the top prediction", "do not dump the raw saliency array"). | 5 / 5 |
Workflow Clarity | The sequence (prerequisites → license notification → markup briefing → preprocess → pre-flight checks → inference → dashboard → serve → present) is clear, and the pre-flight checks are explicit user-confirmation checkpoints with quantified thresholds (10 `?` characters, 750-char windowing, per-`?` timing). It misses a 5 because there are no validate→fix→retry feedback loops (e.g. what to do when preprocessing or inference fails or produces invalid output). | 4 / 5 |
Progressive Disclosure | The bundle is used appropriately: output-format detail is split into a real, clearly-signaled one-level reference (references/output_format.md) and scripts are invoked by path. It is not a 5 because a ~330-line body still inlines content that could be split further — the full flags list, the citation/dataset-acknowledgement text, and the platform-specific HTTP-serving boilerplate. | 4 / 5 |
Total | 17 / 20 Passed |