Content
81%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 content is highly actionable with concrete commands, a complete data model, explicit validation checkpoints and feedback loops, and effective delegation of heavy logic to helper scripts. Its main weakness is conciseness: repeated prohibition lists and restated guidance inflate the body and could be deduplicated.
Suggestions
Deduplicate the system-overview prohibition list (no PR diff links, review comments, screenshots, specs, or implementation deltas) into a single canonical statement and reference it instead of repeating it across the intro, view roles, data model, build, and validate sections.
Extract the full inline JSON data-model example and the PR-size/node-count rubric into a references/ document, keeping only the shape summary and the size table in SKILL.md.
Collapse the orientation heuristics section, which restates guidance already covered in the workflow and view-role sections, into a short bulleted checklist.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is operationally dense and free of basic-concept padding, but it is noticeably verbose: the same prohibition list (system overview must avoid PR diff links, review comments, screenshots, specs, implementation deltas) recurs verbatim across at least five sections, and PR-size guidance is restated multiple times, so it could be materially tightened. | 3 / 5 |
Actionability | Provides copy-paste-ready commands (gh/git invocations, exact pinned D3 CDN URL, helper-script CLI calls, validator command, Cloudflare deploy commands with flags), a full inline JSON data-model shape, and the GitHub diff anchor format — fully executable guidance covering the common cases. | 5 / 5 |
Workflow Clarity | An eight-step workflow with explicit validation checkpoints and a 14-item validation checklist in step 7, plus a clear fix-and-regenerate feedback loop ('debug and regenerate before saying ready', 'fix the graph or report rendering as unverified'). | 5 / 5 |
Progressive Disclosure | Structure is good: the bulk D3 rendering and validation logic is delegated to real, clearly-signaled one-level-deep helper scripts (scripts/d3_canvas_runtime.py, scripts/validate_d3_canvas.py). Minor gaps remain because the SKILL.md still inlines a full JSON data-model example and repeated size/branding rubrics that could be split into references. | 4 / 5 |
Total | 17 / 20 Passed |