Content
93%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 well-crafted workflow skill body: lean, fully executable, and properly split across reference files. The single notable gap is verification depth — the workflow relies on the script's per-issue reporting and exit code but never instructs re-checking milestone/issue state after the transition, which is the only thing separating it from a top workflow-clarity score.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is lean and assumes competence: exact MCP tool calls, exact uv run commands with flags, and compact templates, with no explanation of concepts Claude already knows. Every section is functional, matching the anchor for lean and efficient content. | 5 / 5 |
Actionability | Fully executable throughout: 'mcp__plugin_dh_backlog__backlog_list_milestones(state="open")' with parameters, copy-paste bash commands including a --dry-run variant, and a script that 'reports per-issue results, and exits non-zero if any issue fails'. No gaps between instruction and execution. | 5 / 5 |
Workflow Clarity | A clear 6-step sequence with real checkpoints for a batch operation: milestone resolution with fallback list, a user confirmation gate, dry-run preview, and per-issue failure reporting with an explicit error-handling section. Validation is present so the batch-operation cap does not apply, but there is no post-run verification of the final state or fix-retry feedback loop, keeping it below the anchor-5 example. | 4 / 5 |
Progressive Disclosure | The body is a concise workflow overview with well-signaled, one-level-deep references ('API references: milestones.md | projects-v2.md' up top and again in Step 5) and bulk execution delegated to the github_project_setup.py script. Content is appropriately split, matching the anchor for clear overview with one-level references. | 5 / 5 |
Total | 19 / 20 Passed |