| name | ai-tools-platform-stinger |
| description | The vibe coder's AI toolbox: AI gateways (Portkey, OpenRouter), cloud providers (Bedrock, Vertex AI), frontier model selection (Claude, GPT, Gemini), cheap-fallback routes (Haiku, Mini, Flash), local LLMs (Ollama, LM Studio), GPU cloud (Runpod, Modal, Together, Fireworks), and must-have MCPs and IDE plugins. Use when the user says \\\\\\\"which AI provider should I use\\\\\\\", \\\\\\\"set up Portkey\\\\\\\", \\\\\\\"Ollama for local dev\\\\\\\", \\\\\\\"Runpod vs Modal\\\\\\\", \\\\\\\"which MCP servers do I need\\\\\\\", or asks to optimize AI spend. Do NOT use for cognitive-layer architecture (mind-worker-bee), API key security (security-worker-bee), or PRD authorship (library-worker-bee). |
ai-tools-platform Stinger
You are the playbook for ai-tools-platform-worker-bee. Every invocation produces one concrete artifact: a recommendation, a comparison matrix, a configuration snippet, or a setup guide. Every claim is backed by the research in research/.
Invocation modes (routing table)
Read the user's request and match to one mode. Most requests match one primary mode with one supporting mode.
| Mode | Trigger phrases | Primary guide |
|---|
gateway-setup | "set up Portkey", "configure OpenRouter", "AI gateway", "virtual keys", "budget cap on LLM spend" | guides/01-ai-gateways.md |
provider-selection | "Bedrock vs Vertex", "which cloud AI provider", "Azure OpenAI", "enterprise AI", "private VPC AI" | guides/02-cloud-providers.md |
model-selection | "which model should I use", "Claude vs GPT vs Gemini", "best model for code", "context window comparison" | guides/03-model-selection.md |
cost-optimization | "LLM spend too high", "prompt caching", "batch API", "cheap model fallback", "token cost" | guides/04-cost-optimization.md |
local-llm-workflow | "Ollama", "LM Studio", "local LLM", "offline dev", "privacy-first AI", "llama.cpp" | guides/05-local-llms.md |
gpu-cloud-selection | "Runpod", "Modal", "Together AI", "Fireworks", "Groq", "GPU inference", "serverless GPU" | guides/06-gpu-cloud.md |
mcp-plugin-setup | "MCP server", "which MCPs", "IDE plugin", "Cursor plugin", "tool use setup", "agent toolbox" | guides/07-mcp-and-ide-plugins.md |
First action on every invocation
- Read
guides/00-principles.md: the non-negotiables that govern every output.
- Match the request to the routing table above.
- Open the relevant guide(s) before producing any output.
Folder layout
ai-tools-platform-stinger/
├── SKILL.md (this file — master index)
├── guides/
│ ├── 00-principles.md (non-negotiables: pricing, privacy, fallback discipline)
│ ├── 01-ai-gateways.md (Portkey vs OpenRouter vs LiteLLM; virtual keys; fallback chains)
│ ├── 02-cloud-providers.md (Bedrock vs Vertex AI vs Azure OpenAI vs direct; when to use each)
│ ├── 03-model-selection.md (2026 frontier landscape; capability tiers; cheap-fallback table)
│ ├── 04-cost-optimization.md (prompt caching; batch API; tiering strategy; spend telemetry)
│ ├── 05-local-llms.md (Ollama; LM Studio; llama.cpp; model selection; OpenAI-compat wiring)
│ ├── 06-gpu-cloud.md (Runpod vs Modal vs Together vs Fireworks vs Groq; price table)
│ └── 07-mcp-and-ide-plugins.md (must-have MCPs; Cursor plugin setup; IDE extension picks)
├── examples/
│ ├── gateway-setup-portkey.md (Portkey virtual keys + fallback + budget cap end-to-end)
│ ├── model-selection-matrix.md (filled-in comparison for a SaaS product)
│ └── local-llm-vibe-coding-workflow.md (Ollama + Cursor offline workflow)
├── templates/
│ ├── provider-comparison.md (canonical comparison table skeleton)
│ └── cost-estimate.md (monthly cost estimate sheet)
├── reports/
│ └── README.md (describes how past recommendation reports accumulate)
└── research/
├── research-plan.md
├── research-summary.md
├── index.md
├── internal/
│ └── command-brief-notes.md
└── external/
├── portkey-openrouter-gateways.md
├── aws-bedrock-vertex-azure-comparison.md
├── frontier-model-landscape-2026.md
├── gpu-cloud-inference-vendors.md
├── ollama-local-llm-workflows.md
└── mcp-servers-ide-plugins-2026.md
Canonical stack defaults
These are the recommended defaults. Deviating requires explicit rationale.
| Decision | Recommended default | Rationale |
|---|
| AI gateway | Portkey | Unified virtual keys, budget caps, fallback routing, observability; OpenRouter preferred when pure model routing with no ops overhead needed |
| Primary frontier model (capability) | Claude 3.7 Sonnet / Opus or GPT-4.1 | Top-tier reasoning, long context; choose by use case (see guides/03-model-selection.md) |
| Cheap fallback (cloud) | Claude Haiku 3.5 or Gemini 2.0 Flash | Sub-cent per 1K tokens; fast; adequate for classification, summarization, simple generation |
| Local LLM runtime | Ollama | Easiest setup; OpenAI-compatible REST; cross-platform; large model library |
| Local model (8B class) | Llama 3.1 8B / 3.2 3B or Gemma 3 9B | Best quality-per-GB in the 4-bit quantized range |
| GPU cloud (serverless) | Modal | Best developer experience; container caching; Python-native; pay-per-second |
| GPU cloud (persistent) | Runpod | Lowest price-per-GPU-hour; good for always-on inference |
| Fast inference (Llama) | Groq | Sub-100ms latency for Llama 3.1 70B; free tier available |
| MCP toolbox | See guides/07-mcp-and-ide-plugins.md | Context-dependent; filesystem + Supabase + GitHub are near-universal |
Severity rubric
Used to classify findings when auditing an existing AI tooling stack.
- Must-fix: No fallback model configured (single point of failure); API keys committed to code; no spend cap on gateway; PII sent to a provider without a DPA.
- Should-refactor: Using a frontier model for tasks a cheap model handles adequately; no prompt caching on repeated system prompts; local-capable workloads running on expensive cloud inference.
- Style / nice-to-have: Observability dashboard not configured; no cost attribution per feature; MCP server count excessive for the project size.
Cross-Bee handoffs
Surface these explicitly rather than attempting them inline:
- security-worker-bee: for API key vault strategy, PII audit in prompts, DPA compliance verification, model provider's data-retention policies.
- mind-worker-bee: for cognitive-layer architecture: RAG pipeline design, prompt cascade, three-tier memory, evaluation, coach routing. This Bee picks the providers; mind-worker-bee decides how to use them architecturally.
- devops-worker-bee: for Docker container setup for GPU cloud deploys, CI/CD wiring for model inference services, secret injection from environment.
- library-worker-bee: for PRD authorship when a new AI tooling decision needs to be documented as a feature requirement.
