Content
100%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
A well-structured, actionable skill body that uses tables and concrete Honeycomb expressions, sequences SLO design with explicit checkpoints, and cleanly defers depth to verified one-level-deep reference files.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is lean — decision tables, concrete thresholds ('1h burn rate > 10x', 'Budget remaining > 50%'), and Honeycomb-specific calculated-field semantics rather than generic background — so most tokens earn their place; it is not score 2 because the few best-practice bullets are concise one-liners rather than padded explanation. | 3 / 3 |
Actionability | Provides concrete, copy-paste-ready Honeycomb expressions throughout — 'IF(<qualifying-condition>, <success-condition>)', 'AND(EQUALS($http.route, "/checkout"), NOT(EXISTS($trace.parent_id)))', 'LTE(duration_ms, 500)', 'COUNT WHERE error = true, threshold > N in 5 min', and a URL path template — matching the executable, copy-paste anchor; it is not score 2 because the guidance is complete rather than pseudocode. | 3 / 3 |
Workflow Clarity | Sequences SLO design (Define SLI → Set Target → Exhaustion alerts → Burn alerts) with an explicit pre-create checkpoint ('Before executing the create tool, check in with the user… ask for confirmation') and a misconfiguration feedback loop ('Compliance at 0%: Likely misconfigured SLI… check the SLI definition'); it is not score 2 because validation/checkpoint steps are explicit rather than implicit. | 3 / 3 |
Progressive Disclosure | The body is a well-organized overview that points to three clearly-signaled, one-level-deep reference files (slo-design-guide.md, trigger-examples.md, alerting-strategy.md — all present on disk) plus peer-skill cross-references; it is not score 2 because references are clearly signaled and shallow rather than nested or buried. | 3 / 3 |
Total | 12 / 12 Passed |