Content
50%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 delivers concrete, ready-to-adapt Python examples for every sentiment use case, but it pays for breadth with heavy repetition: the same API call scaffolding is duplicated seven times instead of factored out, and everything lives inline in SKILL.md with no progressive disclosure to reference files. There is also no response validation or error-handling guidance, leaving workflows without checkpoints.
Suggestions
Show the OpenAI-compatible client setup and the chat.completions.create call pattern once, then define each analysis type (single stock, comparison, earnings, sector, unusual activity, watchlist) as just its prompt template — eliminating the sevenfold duplicated scaffolding and cutting the body to a fraction of its length.
Move the per-use-case prompt/JSON schema library into a references/ file (e.g., references/prompts.md) and keep only the Quick Start example plus a one-line-per-function index in SKILL.md, adding real one-level-deep progressive disclosure.
Add response handling guidance: request JSON via response_format, parse and validate the returned structure (e.g., check sentiment score ranges and required keys), and define a fallback when the API returns malformed output.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Seven near-identical client.chat.completions.create blocks (Quick Start, single stock, multi-stock, earnings, sector, unusual activity, watchlist) repeat the same Python scaffolding with only the embedded prompt JSON varying. This is noticeable verbosity/padding — the call pattern could be shown once with per-function prompt variations — matching anchor 2 (several unnecessary padded sections) rather than anchor 3's merely occasional looseness. | 2 / 5 |
Actionability | Concrete, mostly executable Python with a real model ID ("grok-4-1-fast"), env-based key setup, and specific prompt schemas per use case. Minor gaps keep it below 5: functions annotated "-> dict" return the raw response string with no response_format/JSON parsing, later blocks implicitly depend on the client defined in Quick Start, and prompt schemas use bare "..." and "n" placeholders. | 4 / 5 |
Workflow Clarity | The body is a function cookbook with clear section headers but no sequenced process and no checkpoints: there is no validation of API responses, no error handling, and no guidance on which function to choose when. This matches anchor 3 (sequence present but checkpoints missing or implicit); it is not 4 because validation is entirely absent rather than a minor gap, and not 2 because each function is individually well-defined and unambiguous. | 3 / 5 |
Progressive Disclosure | Section headers are clear, but all ~385 lines — including seven repetitive API prompt templates — are inlined in SKILL.md with no bundle files; the function library is content that clearly belongs in a separate reference file. The "References" section links to external URLs rather than organized local files, fitting anchor 3 (some structure but content that should be separate is inline) rather than anchor 4. | 3 / 5 |
Total | 12 / 20 Passed |