Content
77%Weight 40%Scale 1-3Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The skill delivers highly actionable, well-sequenced operational guidance with strong error-recovery checkpoints. It is somewhat verbose in its examples and monolithic in structure, with no progressive disclosure to offload detail.
Suggestions
Move the full 7-node DAG example and the detailed DAG scheduling prose into a references file (e.g., DAG_EXAMPLES.md) and keep SKILL.md focused on the decision table and core lifecycle.
Consolidate overlapping guidance between 'File sharing', 'Fan-out rules', and the template rules into a single section to remove redundancy.
Trim the DAG example to a minimal 3-node illustration inline, linking to the full worked example for readers who need it.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is operational and free of concept-padding, but the full 7-node DAG JSON (~60 lines) and slight rule overlap across 'File sharing', 'Fan-out rules', and template rules could be tightened. | 2 / 3 |
Actionability | Provides concrete tool calls (workflow_run, workflow_update, create_temp_agent, delete_temp_agent), complete executable JSON examples, and explicit file paths — copy-paste ready. | 3 / 3 |
Workflow Clarity | A 13-step numbered lifecycle is clearly sequenced with explicit feedback loops (retry-once, mark-failed, cleanup-as-finally, DAG readyInstructions inspection, cleanup ledger) covering this batch operation. | 3 / 3 |
Progressive Disclosure | Sections are well-organized but all ~235 lines are inline with no external references; the large DAG example and detailed file-sharing/scheduling rules could be split into referenced files for easier navigation. | 2 / 3 |
Total | 10 / 12 Passed |