Content
77%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
A dense, highly actionable skill body with an excellent sequenced eval workflow, held back by chatty filler that inflates token use and by file references that don't match the actual bundle structure. Tightening prose and fixing the broken paths would lift both weak dimensions.
Suggestions
Remove the editorial asides (e.g. "Cool? Cool.", the plumbers/grandparents anecdote, and the "billions a year in economic value" line) and consolidate the three restatements of the core loop into one, to cut tokens without losing guidance.
Fix the broken file paths: the viewer generator lives at scripts/generate_review.py (not eval-viewer/generate_review.py), and the agents/grader.md, agents/comparator.md, and agents/analyzer.md references point to an agents/ directory that is not in the bundle — either add the files or correct the paths.
Split the large monolithic body — e.g. move the platform-specific (Claude.ai / Cowork) instructions and the Description Optimization workflow into reference files — to get comfortably under the 500-line target and improve navigation.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Mostly efficient and actionable, but padded with chatty editorializing that earns no information (e.g. "Cool? Cool.", the "plumbers... grandparents googling how to install npm" anecdote, and "we are trying to create billions a year in economic value here!") and the core loop is restated three times, keeping it short of lean. | 2 / 3 |
Actionability | Concrete executable commands (e.g. `python -m scripts.aggregate_benchmark <workspace>/iteration-N --skill-name <name>`), exact JSON structures, and explicit field-name requirements ("must use the fields `text`, `passed`, and `evidence`... the viewer depends on these exact field names") make the guidance copy-paste ready. | 3 / 3 |
Workflow Clarity | The eval process is a clearly numbered Step 1–5 sequence with explicit checkpoints (spawn all runs at once, capture timing as notifications arrive, grade → aggregate → analyst pass → viewer) and a feedback loop (read feedback.json, focus on complaints, iterate until satisfied). | 3 / 3 |
Progressive Disclosure | Sections are well-organized and real references are one level deep (references/schemas.md, assets/eval_review.html), but the ~485-line body is near its own 500-line cap and points to paths absent from the bundle (eval-viewer/generate_review.py is actually scripts/generate_review.py; the agents/ directory with grader.md/comparator.md/analyzer.md does not exist), which breaks navigation. | 2 / 3 |
Total | 10 / 12 Passed |