Content
100%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
A lean, highly actionable operations skill body with executable CLI/Python examples, explicit sequencing and validation checkpoints for risky lifecycle operations, and well-signaled one-level-deep references. It assumes Claude's competence and reserves detail for genuine gotchas.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is dense, operational guidance — CLI commands, complete JSON specs, jq filters, and gotchas — and largely avoids explaining concepts Claude already knows; the brief intro establishes the name-keyed model used throughout, and nearly every token earns its place. | 3 / 3 |
Actionability | Provides fully executable, copy-paste-ready guidance: complete `create`/`update-config` JSON specs, a working Python zero-downtime-swap snippet, a runtime Foundation Model discovery jq filter, and a concrete error-to-command troubleshooting table. | 3 / 3 |
Workflow Clarity | Multi-step processes are explicitly sequenced with validation checkpoints — 'ALWAYS Do This First' discovery, the alias-plus-update_endpoint swap with its 'missing either half is the common bug' warning, and the readiness feedback loop requiring both `state.ready == READY` AND `state.config_update == NOT_UPDATING` before querying. | 3 / 3 |
Progressive Disclosure | SKILL.md serves as a clear overview with one-level-deep, well-signaled references to a real `references/off-platform-streaming.md` bundle file and cross-skill pointers (`databricks-apps`, `databricks-ml-training`); the bundle reference exists and is appropriately split off rather than nested. | 3 / 3 |
Total | 12 / 12 Passed |