Content
87%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 body is tight, highly actionable, and well-structured, but its batch-operation workflow lacks the validation checkpoints and feedback loops the rubric requires for batch/destructive operations, capping workflow clarity.
Suggestions
Add validation checkpoints after extraction (e.g., verify the temp txt files are non-empty before batching) and after subagents run (e.g., confirm every batch was covered before aggregating).
Include a feedback loop for failures: what to do if jq parsing fails on a malformed jsonl, or if a subagent returns empty/no findings.
Specify how to handle the current-conversation exclusion concretely (the snippet comments 'Skip current conversation if known' but does not implement it), to make the batch step fully executable.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is lean and procedural with no padding explaining concepts Claude already knows; every line (bash snippets, prompt template, batching rules) earns its place, matching the lean-and-efficient anchor. | 5 / 5 |
Actionability | Provides copy-paste-ready bash (sed/jq pipeline), a concrete subagent prompt template, and specific size-based batching thresholds, matching the fully-executable anchor covering common cases. | 5 / 5 |
Workflow Clarity | Four steps are clearly sequenced, but this is a batch operation (parallel subagents over many conversations) with no validation checkpoints or error-recovery feedback loops (e.g., verifying temp files are non-empty, checking subagent output coverage), which per the scoring notes caps workflow clarity at 3. | 3 / 5 |
Progressive Disclosure | The skill is under 50 lines, self-contained with no external references needed, and organized into clear Step sections, matching the simple-skill exception that allows a 5 with well-organized sections. | 5 / 5 |
Total | 18 / 20 Passed |