Content
71%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.
Highly actionable content with concrete MCP tool calls and real identifiers, and well-structured sections. Its main weaknesses are conciseness and progressive disclosure: a large body of reference/topology data is inlined in SKILL.md instead of being offloaded to one-level-deep reference files like the playbooks already are.
Suggestions
Move the stable topology tables (deployment roles, metric prefixes, Redis/Kafka/Postgres/ClickHouse topology, Pyroscope services, dashboard UIDs) into separate reference files under references/ (e.g. references/topology.md, references/metric-prefixes.md) and keep SKILL.md as a concise overview that links to them one level deep, mirroring how the playbooks are already externalized.
Trim SKILL.md to the discovery-workflow essentials and the most-used identifiers; rely on the live-discovery approach the skill itself advocates rather than enumerating every deployment, topic, and label value inline.
Add explicit validation/feedback checkpoints to the discovery workflows (e.g. 'if list_prometheus_metric_names returns no matches, broaden the regex / confirm the datasourceUid') so each sequence has a verify-then-proceed loop.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | It avoids explaining concepts Claude already knows (no 'Kafka is...' padding) and stays domain-specific, but the ~36KB body inlines a large volume of reference/topology data that could be tightened or moved out of the always-loaded SKILL.md context. | 3 / 5 |
Actionability | Provides copy-paste-ready MCP tool invocations with real parameter values (datasource UIDs, regexes, PromQL like the kminion lag query) and concrete dashboard/cluster identifiers covering the common investigation cases. | 5 / 5 |
Workflow Clarity | Discovery workflows are clearly numbered and sequenced with concrete commands per datasource, but explicit validation/feedback checkpoints are only implicit; the destructive/batch cap does not apply since these are read-only investigation queries. | 4 / 5 |
Progressive Disclosure | Headers and tables are well-organized and the playbooks reference is clearly signaled one level deep, but the bulk of the topology/reference material (deployment roles, metric prefixes, Redis/Kafka/Postgres/ClickHouse topology, dashboards) is inlined in SKILL.md rather than split into separate reference files. | 3 / 5 |
Total | 15 / 20 Passed |