Content
65%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 with executable examples across languages, but the body is long and repetitive, lacks an explicit validation/feedback loop in its workflow, and inlines content that should be split into reference files.
Suggestions
Collapse the four near-identical language examples into one representative example plus a short per-language delta, or move them into references/python.md, references/java.md, etc.
Add an explicit validation step to the workflow, e.g. 'Run the original test suite against the instrumented code; if outputs differ, fix and re-run' (validate → fix → retry).
Merge the duplicated 'Best Practices' and 'Constraints' lists into a single section to remove redundant tokens.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Mostly concrete code with little over-explanation, but it is padded: four near-identical language examples repeat the same pattern, and 'Best Practices' and 'Constraints' duplicate the same five points (preserve semantics, minimal overhead, no side effects, exception safety, configurable). | 3 / 5 |
Actionability | Provides fully executable, copy-paste-ready code in Python/Java/JS/C, a configuration file, a probe-description template, and JSON output examples that cover the common instrumentation cases. | 5 / 5 |
Workflow Clarity | Five sequenced steps are present, but step 4 ('Ensure Semantic Preservation') is an abstract checklist rather than an explicit validate→fix→retry checkpoint; for a code-modifying operation the missing verification loop caps the score at 3. | 3 / 5 |
Progressive Disclosure | Well-headed sections give it structure, but at ~350 lines with no bundle files it is monolithic — the four language-specific patterns and the Advanced Features block are inlined when they belong in separate reference files. | 3 / 5 |
Total | 14 / 20 Passed |