Content
57%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, runnable prompt-recipe code for each sentiment task with no concept padding, but it is held back by verbatim boilerplate repeated across nine functions, a return-type/JSON-parsing mismatch, no output validation (including for the batch function), and no use of progressive disclosure to move specialized recipes into reference files. These are fixable structural issues rather than quality-of-guidance failures.
Suggestions
Show the client setup and a single request wrapper once, then present each subsequent recipe as just its prompt payload — removing the repeated `client.chat.completions.create` boilerplate would cut the file roughly in half.
Fix the return types and add output validation: either set `response_format={"type": "json_object"}` and `json.loads` the result (making `-> dict` truthful), or at minimum add a note to parse and verify the JSON before use — especially for `batch_sentiment`, where the response should be checked for complete coverage of all requested topics.
Move the financial, crypto, batch, and alert recipes into a `references/` file (e.g., `references/recipes.md`) linked from a short SKILL.md overview, keeping only Quick Start and the core functions inline.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The client setup and `client.chat.completions.create` / `model="grok-4-1-fast"` boilerplate repeats verbatim across nine nearly identical functions when the wrapper could be shown once and only the prompt payloads varied. It is mostly efficient (no concept-padding) but clearly could be tightened, matching anchor 3 rather than 4. | 3 / 5 |
Actionability | Quick Start is copy-paste runnable and every recipe is concrete executable code. Minor gaps: every function declares `-> dict` yet returns raw `response.choices[0].message.content` (a string), and despite prompts requesting JSON there is no response_format or parsing guidance, keeping it below the fully copy-paste-ready anchor 5. | 4 / 5 |
Workflow Clarity | The recipes are organized clearly by use case with a Quick Start, but there is no validation of model output anywhere (e.g., checking the returned JSON parses), and batch_sentiment is a batch operation with no verification step — the rubric's batch-operation cap applies, holding this at 3. | 3 / 5 |
Progressive Disclosure | Section headers are clear and navigation is easy, but ~250 lines of function templates are all inlined in SKILL.md with no bundle files; the financial/crypto/batch material would fit separate reference files. Structure exists but content that should be separate is inline, matching anchor 3; not 2 since the content is well-sectioned, not 4 since nothing is split out. | 3 / 5 |
Total | 13 / 20 Passed |