Content
92%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The skill body is lean, highly actionable, and clearly sequenced with validation and error-recovery checkpoints. Its main weakness is the absence of progressive disclosure: a single monolithic file holds reference material that could be split out.
Suggestions
Move the detailed parameter reference (model, data_interval_start/end, file.format/compression/max_size_mb, include/exclude) into a references/PARAMETERS.md and link to it from step 1, keeping only the must-ask clarifications inline.
Extract the poll status-action table into a references/POLLING.md (or a clearly signaled collapsed section) so the main workflow stays scannable.
Pull the REST download endpoint syntax and filename conventions into a references/DOWNLOAD.md to reduce inline reference bulk while keeping the workflow steps lean.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body assumes Claude's competence—no explanation of what PostHog, Parquet, or JSONLines are—and every section earns its place, using compact tables and short steps. | 3 / 3 |
Actionability | It provides concrete MCP tool names, a full example JSON request, exact REST endpoint paths, a status-to-action table, and filename patterns—effectively copy-paste ready. | 3 / 3 |
Workflow Clarity | A numbered six-step sequence with explicit polling validation, per-status actions, and error-recovery guidance (re-poll on transient Running, report the error field on failures) gives clear checkpoints and feedback loops. | 3 / 3 |
Progressive Disclosure | The file is well organized into sections, but at ~120 lines it keeps all reference material inline (full parameter enumeration, status table, endpoint syntax) with no one-level-deep references that could split the bulk. | 2 / 3 |
Total | 11 / 12 Passed |