Content
77%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 a clear, validated workflow, but it carries some repetition and keeps detailed reference material inline rather than splitting it into signaled separate files. Tightening duplication and extracting a reference doc would push conciseness and progressive disclosure to 3.
Suggestions
De-duplicate the task-directory path and the read-only modification-boundary rules, which are restated across the 输出, 修改边界, and 验证清单 sections.
Move the detailed per-citation AI judging rubric and report schema into a signaled one-level reference file (e.g. `references/judging_guide.md`) and link to it from the body.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is mostly efficient and assumes Claude knows LaTeX/BibTeX, but the task-directory path and read-only modification boundaries are restated multiple times, which keeps it just short of the lean score-3 anchor. | 2 / 3 |
Actionability | It provides fully executable bash commands with concrete flags pointing at the real script `scripts/run_ref_alignment.py`, plus explicit structured input/output file names — copy-paste ready guidance matching the score-3 anchor. | 3 / 3 |
Workflow Clarity | The 4-step workflow is clearly sequenced with explicit validation checkpoints — a 静态自检 checklist, forced evidence-priority rules, and P0/P1 triage — providing the feedback loop the rubric rewards. | 3 / 3 |
Progressive Disclosure | It references a real script bundle, but detail that could live in one-level-deep reference markdown (report schema, field-by-field AI judging rubric) is inline in a single SKILL.md with no clearly signaled separate reference files. | 2 / 3 |
Total | 10 / 12 Passed |