groq-hello-world
Create a minimal working Groq chat completion example. Use when starting a new Groq integration, testing your setup, or learning basic Groq API patterns. Trigger with phrases like "groq hello world", "groq example", "groq quick start", "simple groq code".
80
77%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./plugins/saas-packs/groq-pack/skills/groq-hello-world/SKILL.mdQuality
Discovery
89%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 solid skill description with excellent trigger terms and completeness. It clearly identifies when to use the skill and provides natural language triggers. The main weakness is that the specificity of capabilities could be improved by listing more concrete actions beyond just 'create a minimal working example'.
Suggestions
Add more specific concrete actions, e.g., 'Sets up API client, sends a chat completion request, and parses the response' to improve specificity.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | It names the domain (Groq chat completion) and one action (create a minimal working example), but doesn't list multiple concrete actions like 'set up API keys, send chat requests, parse responses'. | 2 / 3 |
Completeness | Clearly answers both 'what' (create a minimal working Groq chat completion example) and 'when' (starting a new Groq integration, testing setup, learning basic patterns), with explicit trigger phrases provided. | 3 / 3 |
Trigger Term Quality | Includes explicit natural trigger phrases like 'groq hello world', 'groq example', 'groq quick start', 'simple groq code' as well as contextual terms like 'Groq integration', 'Groq API patterns'. These are terms users would naturally say. | 3 / 3 |
Distinctiveness Conflict Risk | The description is clearly scoped to Groq API specifically, with distinct trigger terms that are unlikely to conflict with other skills (e.g., OpenAI, general coding). The 'groq' keyword throughout makes it highly distinctive. | 3 / 3 |
Total | 11 / 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 hello-world skill with excellent actionability — all code is executable and covers multiple use cases. However, it's overly verbose for a 'minimal working example' skill, including redundant Python equivalents, a full TypeScript interface definition, and reference tables that would be better placed in separate files. Trimming to just the core TypeScript example with a model table and linking out to references would better serve its stated purpose.
Suggestions
Remove the Python equivalent or move it to a separate reference file — a hello world skill should pick one language and be minimal
Move the Response Structure interface and Available Models table to a linked reference file (e.g., GROQ-REFERENCE.md) to keep the skill focused on the quick start
Add a brief expected output after Step 1 so users can verify their setup works correctly (e.g., 'You should see a text response followed by a token count')
Remove the overview sentence about '10-50x faster than GPU-based providers' — this is marketing context Claude doesn't need to perform the task
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill includes some unnecessary content like the overview sentence explaining what Groq is and speed comparisons, the Python equivalent (which is largely redundant given the TypeScript example), and the full response structure interface which Claude already understands. The model table with speed benchmarks and the error handling table add value but the overall content could be tightened significantly. | 2 / 3 |
Actionability | All code examples are fully executable and copy-paste ready across TypeScript and Python. The examples cover basic completion, streaming, and model selection with concrete model IDs and realistic messages. Error handling table provides specific error codes with solutions. | 3 / 3 |
Workflow Clarity | Steps are clearly numbered and sequenced, but for a 'hello world' skill this is essentially a single-task skill presented as multiple independent examples rather than a true multi-step workflow. There are no validation checkpoints — no step to verify the API key works, no expected output shown to confirm success, and no error recovery flow beyond the error table. | 2 / 3 |
Progressive Disclosure | The skill references external resources and a next-step skill (`groq-local-dev-loop`), which is good. However, the response structure interface, full model table, and error handling table could be split into reference files. The content is somewhat monolithic at ~120 lines when much of it is reference material rather than quick-start guidance. | 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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