groq-core-workflow-a
Execute Groq primary workflow: chat completions with tool use and JSON mode. Use when implementing chat interfaces, function calling, structured output, or building AI features with Groq's fast inference. Trigger with phrases like "groq chat completion", "groq tool use", "groq function calling", "groq JSON mode".
67
82%
Does it follow best practices?
Impact
—
No eval scenarios have been run
Passed
No known issues
Quality
Discovery
100%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
This is a strong skill description that clearly identifies the Groq-specific domain, lists concrete capabilities, and provides explicit trigger guidance with both a 'Use when' clause and specific trigger phrases. It is well-structured, concise, and distinctive enough to avoid conflicts with other AI/LLM-related skills.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: chat completions, tool use, JSON mode, function calling, and structured output. These are distinct, well-defined capabilities rather than vague abstractions. | 3 / 3 |
Completeness | Clearly answers both 'what' (execute Groq primary workflow: chat completions with tool use and JSON mode) and 'when' (explicit 'Use when' clause with scenarios plus a 'Trigger with phrases' section listing specific trigger terms). | 3 / 3 |
Trigger Term Quality | Includes explicit natural trigger phrases like 'groq chat completion', 'groq tool use', 'groq function calling', 'groq JSON mode', plus broader terms like 'chat interfaces', 'structured output', and 'fast inference'. Good coverage of terms users would naturally say. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive due to the Groq-specific branding and the combination of specific features (chat completions, tool use, JSON mode). The 'groq' prefix on trigger terms makes it very unlikely to conflict with generic chat or LLM skills. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
64%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a solid, actionable skill with complete, executable TypeScript examples covering the core Groq chat/tools/JSON workflow. Its main weaknesses are the lack of inline validation/error recovery steps within the workflows and the monolithic structure that could benefit from splitting detailed examples into separate referenced files. The error handling table is helpful but should be integrated into the workflow steps for critical failure points like malformed tool call JSON.
Suggestions
Integrate validation/retry logic directly into the tool use workflow (e.g., wrap JSON.parse of tool_calls arguments in try/catch with retry) rather than only listing errors in a separate table.
Consider extracting the full tool use example and multi-turn conversation class into separate referenced files to reduce the main skill's token footprint and improve progressive disclosure.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is mostly efficient with executable code examples, but includes some unnecessary elements like the 'Understanding of Groq model capabilities' prerequisite, the overview paragraph about LPU latency, and the multi-turn conversation class (Step 5) which adds bulk without being core to the stated workflow. The model selection table is useful but could be more compact. | 2 / 3 |
Actionability | All code examples are fully executable TypeScript with proper imports, typed parameters, and complete implementations. The tool use example includes the full round-trip (define tools, call API, execute functions, send results back). The error handling table provides specific causes and solutions. | 3 / 3 |
Workflow Clarity | Steps are clearly sequenced and labeled, and the tool use flow has explicit sub-steps (A, B, C). However, there are no validation checkpoints or error recovery loops within the workflows themselves — error handling is relegated to a separate table rather than integrated into the steps. For API operations that can fail (malformed JSON from tool calls, context length issues), inline validation/retry logic would improve robustness. | 2 / 3 |
Progressive Disclosure | The content has good section structure and references external docs and a companion skill file (groq-core-workflow-b). However, at ~180 lines of inline code, some patterns (like the full tool use example or the conversation class) could be split into referenced files. No bundle files exist to support progressive disclosure, and all content is monolithically inline. | 2 / 3 |
Total | 9 / 12 Passed |
Validation
81%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 9 / 11 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
allowed_tools_field | 'allowed-tools' contains unusual tool name(s) | Warning |
frontmatter_unknown_keys | Unknown frontmatter key(s) found; consider removing or moving to metadata | Warning |
Total | 9 / 11 Passed | |
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