Content
67%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 body is well-structured, actionable, and workflow-oriented with strong feedback loops and a properly signaled reference file. Its main weakness is conciseness — section-to-section redundancy and some over-explanation of basics inflate the token budget.
Suggestions
Collapse the overlap between Core Concepts and Detailed Topics so each technique is described once; defer deeper detail to optimization_techniques.md.
Trim basic explanations Claude already knows (e.g. what KV-cache tensors are) to lift conciseness.
Link the existing compaction.py script from the body (e.g. in the Compaction or Examples section) so the bundle file is discoverable rather than orphaned.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Mostly efficient with concrete thresholds and tight prose, but Core Concepts and Detailed Topics overlap (compaction, masking, KV-cache each described twice) and some basic explanations (e.g. what KV-cache stores) pad the text. | 3 / 5 |
Actionability | Provides executable code examples, concrete thresholds (70%, 80%), an explicit prompt ordering, and a copy-pasteable reference format, with only minor gaps in example completeness. | 4 / 5 |
Workflow Clarity | A clear priority-ordered strategy sequence and decision-framework table are present, plus a measure-optimize-remeasure feedback loop ('If an optimization technique does not measurably improve the target metric, remove it'); minor validation-checkpoint gaps remain. | 4 / 5 |
Progressive Disclosure | Good section structure with a clearly signaled one-level reference ('Read when:' guidance to optimization_techniques.md); minor gaps are the dense inline Detailed Topics and the unreferenced compaction.py script. | 4 / 5 |
Total | 15 / 20 Passed |