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".
64
77%
Does it follow best practices?
Impact
—
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 description with clear 'what' and 'when' clauses and excellent trigger term coverage specific to Groq. The main weakness is that the capability description is somewhat thin — it only describes creating a minimal example rather than listing specific concrete actions involved. Overall it performs well for skill selection purposes.
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. | 3 / 3 |
Trigger Term Quality | Includes explicit natural trigger phrases like 'groq hello world', 'groq example', 'groq quick start', 'simple groq code', plus contextual terms like 'Groq integration', 'Groq API patterns'. These are terms users would naturally say. | 3 / 3 |
Distinctiveness Conflict Risk | Very specific niche — Groq API chat completion examples. The trigger terms are highly specific to Groq and unlikely to conflict with other skills unless there are multiple Groq-related skills. | 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, actionable hello-world skill with excellent executable code examples in both TypeScript and Python. Its main weakness is that it tries to be both a quick-start guide and a reference document, including detailed model tables, full response type definitions, and comprehensive error tables that inflate the token cost beyond what a 'minimal working example' skill needs. Trimming reference material into separate files would significantly improve both conciseness and progressive disclosure.
Suggestions
Move the model table, response structure interface, and error handling table into a separate REFERENCE.md file, keeping only the recommended default model inline.
Choose one language (TypeScript, given the SDK focus) for the main example and relegate the Python equivalent to a separate file or a brief 'Python: see PYTHON.md' reference.
Add a brief verification step after Step 1, e.g., 'Expected output: a text response followed by token count. If you see a 401 error, check your GROQ_API_KEY.'
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill includes some unnecessary content like the overview explanation of LPU speed ('10-50x faster than GPU-based providers'), the full response structure interface, and providing both TypeScript and Python equivalents for the same basic task. The model table with speed benchmarks and the response structure could be in a reference file. However, most content is useful and not egregiously padded. | 2 / 3 |
Actionability | All code examples are fully executable and copy-paste ready across TypeScript and Python. The error handling table provides specific error codes with concrete solutions. Model IDs are exact strings ready to use. | 3 / 3 |
Workflow Clarity | Steps are clearly sequenced from basic to streaming to different models, but this is a simple hello-world skill that doesn't involve destructive operations. However, there's no validation checkpoint — no mention of verifying the API key works, checking the response is valid, or what success looks like before proceeding to the next step. | 2 / 3 |
Progressive Disclosure | The skill includes a lot of inline reference material (full response interface, model table, error table) that would be better in separate reference files. It does reference external resources and a next-step skill ('groq-local-dev-loop'), but the main file is quite long for a 'hello world' skill. The model table and response structure especially should be in a reference file. | 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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