Content
64%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.
The content is concise and gives an executable entry point with named artifacts, but lacks explicit validation checkpoints and does not structure details into separate one-level-deep reference files.
Suggestions
Add an explicit validate-then-retry checkpoint for the writeback step (e.g., verify trace_summary.json exists and event_count > 0 before declaring success).
Extract the dependency/integration details (Qdrant MCP, event schema, query_api) into a separate reference file and link to it one level deep.
Include a short inline code or CLI snippet showing how a caller records an event, so the actionability is copy-paste ready.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is compact and uses clear section headers with mostly high-signal content; only minor phrases like the scoring-rationalization line in 复用价值 could be trimmed. | 4 / 5 |
Actionability | It gives a concrete entry script path, an exact run command, and named output artifacts, but offers no inline code snippet or API call example, leaving a small gap. | 4 / 5 |
Workflow Clarity | A rough sequence exists (call at key nodes -> write evidence chain -> query by trace_id/time), but there are no explicit validation checkpoints and failure handling is described per-case rather than as a validate-retry loop. | 3 / 5 |
Progressive Disclosure | Sections are organized, but the single inlined run_provenance.py is referenced without one-level-deep separate reference files, and the external module paths (core.runtime, memory.taishige) are not bundled or clearly signaled as references. | 3 / 5 |
Total | 14 / 20 Passed |