Content
100%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The body is a tight, actionable workflow that respects Claude's intelligence while supplying exactly the domain-specific knowledge it lacks — exact tool names, a baseline-stats script, decision tables, and iteration caps with feedback loops. Bundle references are real, one level deep, and clearly signaled.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Lean and efficient — opens with 'Authoring an alert is a measurement problem, not a guessing problem' and assumes Claude's competence; no padding or explanation of basic concepts, every section earns its place. | 3 / 3 |
Actionability | Provides a fully executable command ('echo ... | python3 scripts/baseline_stats.py --window-minutes 5'), concrete JSON examples, exact tool names, and specific parameter values — copy-paste ready guidance. | 3 / 3 |
Workflow Clarity | A six-step numbered workflow with explicit validation checkpoints: simulate fire_count must land in the 0–3 band, a health-flag decision table, and a hard 'three rounds then ship or skip' feedback loop for error recovery. | 3 / 3 |
Progressive Disclosure | The body is an overview with well-signaled, one-level-deep references (threshold-defaults.md, volume-floor-alerts.md) and a helper script (baseline_stats.py) — all verified to exist as real bundle files, appropriately split out. | 3 / 3 |
Total | 12 / 12 Passed |