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 is a well-organized but monolithic prompt-template library: concrete and mostly executable, yet it inlines eight near-duplicate function templates that belong in reference files, omits JSON-response validation despite the whole skill depending on structured output, and gives no guidance for choosing among the overlapping functions. The result is usable but heavier and less robust than the structure warrants.
Suggestions
Move the per-asset prompt templates (bitcoin, altcoin, token, DeFi, NFT, whale, FOMO/FUD, dashboard) into references/ files (e.g., references/templates.md) and keep in SKILL.md one canonical call pattern plus a short table of available templates — this cuts the duplicated client-call boilerplate and improves both conciseness and progressive disclosure.
Add a JSON validation/feedback step: either request response_format={"type": "json_object"} on the API call or parse with json.loads and retry/re-prompt on failure, and fix the "-> dict" annotations to match actual return values — this supplies the missing workflow checkpoints for the skill's core dependency on structured output.
Add brief selection guidance (e.g., 'use crypto_market_dashboard for an overview, analyze_token for a specific coin, monitor_whale_alerts for flow events') so the sequence between the eight overlapping functions is explicit.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body avoids concept over-explanation, but roughly 300 lines of eight prompt-template functions ("def bitcoin_sentiment()", "def detect_altseason()", "def analyze_token()", etc.) each repeat the identical client-call boilerplate and JSON-skeleton pattern that could be shown once and then varied. This is the 'mostly efficient but could be tightened' anchor — not a 2, since there is no padded prose or explanation of things Claude already knows. | 3 / 5 |
Actionability | The Quick Start is executable copy-paste code (env key, OpenAI client with base_url="https://api.x.ai/v1", a complete call), and each function carries a concrete prompt with a specific JSON schema. It misses anchor 5 on real gaps: every function is annotated "-> dict" yet returns the raw string from response.choices[0].message.content with no JSON parsing or response_format enforcement, and the Best Practices entries ("Focus on accounts older than 6 months...") are dangling prompt strings in comments rather than integrated, runnable guidance. | 4 / 5 |
Workflow Clarity | The setup→call sequence (XAI_API_KEY, client, function call) is clear and unambiguous per function, hitting the 'sequence present' bar of anchor 3. It stays at 3 rather than 4 because checkpoints are absent: no validation that the model returned well-formed JSON, no error-handling guidance, and no direction on when to use which of the eight overlapping functions (dashboard vs. token vs. whale monitoring). | 3 / 5 |
Progressive Disclosure | Section headers (Quick Start, Sentiment Functions, Best Practices, Related Skills, References) give real structure, but 300+ lines of per-asset prompt templates are inlined in SKILL.md with no references/, scripts/, or assets/ bundle — content that belongs in separate one-level-deep reference files. This matches anchor 3 ('content that should be separate is inline' but with decent organization), not 2, because navigation within the file is clear. | 3 / 5 |
Total | 13 / 20 Passed |