Content
37%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The skill provides a clear vision of what analytics output should look like with well-formatted example blocks, and the data source commands are concrete. However, it critically lacks the computational logic to transform raw data into the displayed metrics—there's no code for counting corrections, computing rates, categorizing learnings, or generating heatmaps. The workflow is essentially 'gather data → [magic] → show report' with the middle step entirely missing.
Suggestions
Add executable code or detailed algorithmic steps for computing each metric (correction rate, category breakdown, stale learning detection) from the raw data sources.
Define a clear sequential workflow: 1) gather data, 2) parse/categorize, 3) compute metrics, 4) validate completeness, 5) format output—with handling for missing or incomplete data.
Specify how to identify 'corrections' programmatically—the current definition ('any instance where the user redirected, fixed, or overrode agent output') is too vague to implement without concrete parsing logic.
Consolidate the four output example blocks into a single representative example or move detailed format specifications to a separate reference file.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is moderately efficient but includes some redundancy—the 'What It Shows' section with four large example blocks is verbose, and the 'Output' section at the end largely restates what was already shown. Some trimming would help, though it doesn't over-explain concepts Claude already knows. | 2 / 3 |
Actionability | The data gathering commands are concrete and executable, but the actual computation of metrics (correction rate, heatmap generation, session duration, stale learning detection) is entirely unspecified. The skill shows desired output formats but provides no code or logic for how to derive those numbers from the raw data sources. | 2 / 3 |
Workflow Clarity | There is no clear sequenced workflow—the skill lists data sources and shows desired outputs but never specifies the steps to go from raw data to computed analytics. There are no validation checkpoints, no error handling for missing data, and no feedback loops for when data sources are incomplete or unavailable. | 1 / 3 |
Progressive Disclosure | The content is organized into logical sections with clear headers, which is good. However, the four large output example blocks make the file quite long and could be split into a reference file. No bundle files are provided, so there's no progressive disclosure to external references, though for a skill of this complexity some separation would be beneficial. | 2 / 3 |
Total | 7 / 12 Passed |