Content
100%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is an exemplary skill that maximizes signal-to-noise ratio. It provides concrete, executable guidance with clear workflows, validation checkpoints, and edge case handling. The worked example demonstrates the complete process with real commands, and the output format ensures consistent handoff to downstream skills.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Extremely lean and efficient. No explanation of what git bisect is or how regressions work conceptually—assumes Claude knows. Every table, command, and section earns its place with actionable content. | 3 / 3 |
Actionability | Fully executable commands throughout (git bisect start, git bisect run, pytest invocations). The worked example is copy-paste ready with real commands and demonstrates the complete workflow from start to fault localization. | 3 / 3 |
Workflow Clarity | Clear 3-step sequence with explicit validation checkpoints ('Verify both bounds before bisecting'). Includes feedback loops for edge cases (re-verify bounds, bisect again). The 'Do not' section reinforces critical validation steps. | 3 / 3 |
Progressive Disclosure | Well-structured with clear sections (steps, worked example, edge cases, output format). Appropriate length for a single file. References to related skills (bug-to-patch-generator, bug-localization) are one-level deep and clearly signaled. | 3 / 3 |
Total | 12 / 12 Passed |