Content
77%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a well-structured process skill with highly actionable templates and a clear three-phase workflow with proper validation gates. Its main weakness is verbosity in the supporting sections — the Entire CLI integration, learning signal pipeline explanation, and detailed interoperability discussion with context-surfing add significant token cost without proportional value for execution. The core workflow is excellent but is diluted by contextual information that could be externalized.
Suggestions
Move the 'How intent frames become learning signals' section and the detailed 'Relationship with context-surfing' subsection into separate reference files to reduce inline token cost.
Trim the Entire CLI integration section to just the detection command and the two behavioral branches (available vs. unavailable), removing the explanation of what learning-aggregator does with the data.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The core workflow sections are reasonably lean, but the skill includes substantial sections on Entire CLI integration, learning signal explanations, and detailed interoperability descriptions (especially the context-surfing relationship) that add significant token overhead. The explanation of how intent frames become learning signals tells Claude things it doesn't need to know to execute the skill. | 2 / 3 |
Actionability | The skill provides concrete, copy-paste-ready markdown templates for Intent Frame, Intent Check, and Intent Resolution blocks with exact field names and formats. Trigger conditions, drift examples, and confirmation prompts are all specific and directly executable. | 3 / 3 |
Workflow Clarity | The three-phase workflow (Capture → Monitor → Resolution) is clearly sequenced with explicit validation checkpoints — notably the mandatory user confirmation before proceeding ('Do not proceed until the user confirms or adjusts') and the drift detection feedback loop (detect → ask → pivot or return). Multi-intent session rules add clear sequencing for complex scenarios. | 3 / 3 |
Progressive Disclosure | The content is a single monolithic file with no bundle files or external references for detailed content. The Entire CLI integration details, the full interoperability section with context-surfing, and the learning signals explanation could be split into separate reference files. The skill is well-organized with clear headers but is longer than it needs to be inline. | 2 / 3 |
Total | 10 / 12 Passed |