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Get Started Free →Automate AI inference, chat completions, audio translation, and TTS voice management through GroqCloud's high-performance API via Composio
.claude/skills/composiohq-groqcloud-automation/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-09 | ✗→✓ | ▲ Improved | -35% | 0% |
| case-06 | ✗→✓ | ▲ Improved | 7% | 0% |
| case-16 | ✗→✓ | ▲ Improved | 21% | 0% |
| case-19 | ✗→✓ | ▲ Improved | -13% | 0% |
| case-04 | ✓→✗ | ▼ Worse | -42% | 0% |
Automate AI inference workflows using GroqCloud's ultra-fast API -- chat completions, model discovery, audio translation, and TTS voice selection -- all orchestrated through the Composio MCP integration.
Toolkit docs: composio.dev/toolkits/groqcloud
https://rube.app/mcpGROQCLOUD_* tools become available for executionList all models available on GroqCloud to find valid model IDs before running inference.
Tool: GROQCLOUD_LIST_MODELS
No parameters required -- returns all available models with metadata.Use this as a prerequisite before any chat completion call to ensure you reference a valid, non-deprecated model ID.
Generate AI responses for conversational prompts using a specified GroqCloud model.
Tool: GROQCLOUD_GROQ_CREATE_CHAT_COMPLETION
| Parameter | Type | Required | Description | |-----------|------|----------|-------------| | model | string | Yes | Model ID from GROQCLOUD_LIST_MODELS | | messages | array | Yes | Ordered list of {role, content} objects (system, user, assistant) | | temperature | number | No | Sampling temperature 0-2 (default: 1) | | max_completion_tokens | integer | No | Max tokens to generate | | top_p | number | No | Nucleus sampling 0-1 (default: 1) | | stop | string/array | No | Up to 4 stop sequences | | stream | boolean | No | Enable SSE streaming (default: false) |
Retrieve detailed metadata for a specific model including context window and capabilities.
Tool: GROQCLOUD_GROQ_RETRIEVE_MODEL
| Parameter | Type | Required | Description | |-----------|------|----------|-------------| | model | string | Yes | Model identifier (e.g., groq-1-large) |
Translate non-English audio files into English text using Whisper models.
Tool: GROQCLOUD_GROQ_CREATE_AUDIO_TRANSLATION
| Parameter | Type | Required | Description | |-----------|------|----------|-------------| | file_path | string | Yes | Local path, HTTP(S) URL, or base64 data URL for audio | | model | string | No | Model ID (default: whisper-large-v3). Note: whisper-large-v3-turbo may not support translations | | response_format | string | No | json, verbose_json, or text (default: json) | | temperature | number | No | Sampling temperature 0-1 (default: 0) |
Enumerate available text-to-speech voices for Groq PlayAI models to drive voice selection UX.
Tool: GROQCLOUD_LIST_VOICES
Returns the set of supported TTS voices. Note: this is a static list maintained manually.| Pitfall | Details | |---------|---------| | Nested model list | GROQCLOUD_LIST_MODELS response may be nested at response['data']['data'] -- do not assume a flat top-level array | | Hard-coded model IDs break | Always fetch model IDs dynamically via GROQCLOUD_LIST_MODELS; hard-coded names can break when models are deprecated or renamed | | Audio format validation | GROQCLOUD_GROQ_CREATE_AUDIO_TRANSLATION rejects invalid or unsupported audio formats silently -- validate inputs before calling | | Model metadata drifts | Data from GROQCLOUD_GROQ_RETRIEVE_MODEL (context window, features) can change as models update -- do not treat it as static | | TTS voice changes | Voice sets from GROQCLOUD_LIST_VOICES may shrink or rename over time -- handle missing voices gracefully |
| Tool Slug | Purpose | |-----------|---------| | GROQCLOUD_LIST_MODELS | List all available models and metadata | | GROQCLOUD_GROQ_CREATE_CHAT_COMPLETION | Generate chat-based AI completions | | GROQCLOUD_GROQ_RETRIEVE_MODEL | Get detailed info for a specific model | | GROQCLOUD_GROQ_CREATE_AUDIO_TRANSLATION | Translate non-English audio to English text | | GROQCLOUD_LIST_VOICES | Retrieve available TTS voices for PlayAI |
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| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 13,330 | 1,109 | -92% | 1 | 1 | 0% | 2,616 | 1,341 | -49% | 0 | 0 | — |
case-02 | fail→fail | 3,528 | 5,792 | +64% | 1 | 1 | 0% | 516 | 1,510 | +193% | 0 | 0 | — |
case-09 | fail→pass | 11,664 | 2,224 | -81% | 1 | 1 | 0% | 2,363 | 1,546 | -35% | 0 | 0 | — |
case-03 | fail→fail | 8,721 | 3,307 | -62% | 1 | 1 | 0% | 1,708 | 1,220 | -29% | 0 | 0 | — |
case-04 | pass→fail | 10,894 | 1,937 | -82% | 1 | 1 | 0% | 2,465 | 1,437 | -42% | 0 | 0 | — |
case-05 | pass→fail | 3,208 | 5,691 | +77% | 1 | 1 | 0% | 684 | 1,529 | +124% | 0 | 0 | — |
case-06 | fail→pass | 7,681 | 2,825 | -63% | 1 | 1 | 0% | 1,626 | 1,735 | +7% | 0 | 0 | — |
case-07 | pass→fail | 7,218 | 4,358 | -40% | 1 | 1 | 0% | 1,468 | 1,465 | -0% | 0 | 0 | — |
case-08 | fail→fail | 10,434 | 5,156 | -51% | 1 | 1 | 0% | 2,166 | 1,584 | -27% | 0 | 0 | — |
case-10 | fail→fail | 10,752 | 2,290 | -79% | 1 | 1 | 0% | 2,435 | 1,316 | -46% | 0 | 0 | — |
case-11 | fail→fail | 2,352 | 4,753 | +102% | 1 | 1 | 0% | 454 | 1,541 | +239% | 0 | 0 | — |
case-12 | pass→fail | 8,738 | 4,784 | -45% | 1 | 1 | 0% | 1,982 | 1,537 | -22% | 0 | 0 | — |
case-13 | pass→fail | 6,613 | 4,689 | -29% | 1 | 1 | 0% | 1,329 | 1,497 | +13% | 0 | 0 | — |
case-14 | fail→fail | 12,717 | 5,343 | -58% | 1 | 1 | 0% | 2,263 | 1,568 | -31% | 0 | 0 | — |
case-15 | fail→fail | 8,015 | 2,791 | -65% | 1 | 1 | 0% | 1,548 | 1,311 | -15% | 0 | 0 | — |
case-16 | fail→pass | 6,693 | 1,711 | -74% | 1 | 1 | 0% | 1,200 | 1,453 | +21% | 0 | 0 | — |
case-17 | pass→fail | 5,947 | 5,218 | -12% | 1 | 1 | 0% | 1,379 | 1,570 | +14% | 0 | 0 | — |
case-18 | fail→fail | 6,940 | 5,146 | -26% | 1 | 1 | 0% | 1,276 | 1,569 | +23% | 0 | 0 | — |
case-19 | fail→pass | 9,396 | 2,466 | -74% | 1 | 1 | 0% | 1,857 | 1,617 | -13% | 0 | 0 | — |
case-20 | pass→pass | 4,999 | 4,961 | -1% | 1 | 1 | 0% | 852 | 2,011 | +136% | 0 | 0 | — |
case-21 | pass→pass | 5,702 | 4,268 | -25% | 1 | 1 | 0% | 1,031 | 1,982 | +92% | 0 | 0 | — |
case-22 | pass→pass | 4,279 | 6,959 | +63% | 1 | 1 | 0% | 879 | 2,518 | +186% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted, and 9 counted toward the lift figure. The other 13 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of -9 percentage points is the difference between those two pass rates over the 9 comparable cases. 7 cases got worse with the skill loaded, and they are included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.