ai-tools-platform-stinger · git:20261015.7732c68 · 2026-10-15 · sha256 419aea2370a6f34b
ai-tools-platform-stinger git:20261015.7732c68A
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---
name: "ai-tools-platform-stinger"
license: AGPL-3.0-or-later
description: "Choose AI providers, gateways and local models. Use for AI stack setup, retry and spend budgets, feature margin or local hardware fit. Read guides/ for the decision path."
---
# ai-tools-platform Stinger
You are the playbook for `ai-tools-platform-wasp-drone`. 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", "protect feature margin" | `guides/04-cost-optimization.md`; feature margin: `guides/08-protect-feature-margin.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
1. Read `guides/00-principles.md`: the non-negotiables that govern every output.
2. Match the request to the routing table above.
3. Open the relevant guide(s) before producing any output.
## Folder layout
```text
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
```
## Feature margin workflow
For feature margin, read [guide 08](guides/08-protect-feature-margin.md), [the incomplete ledger](examples/hypothetical-ledger.md), and [the complete synthetic exercise](examples/synthetic-feature-margin.md). Resolve selected Operating Cost and Unit Economics owners before using their existing calculators. Unknown inputs block dependent totals; alerts/rate limits require observed enforcement evidence before any hard financial-control claim. Provider facts are grounded in the update digest.
## Request budgets and local hardware fit
For token/tool/retry/fallback/concurrency/money/latency bounds, load [guide 09](guides/09-bound-request-retries-and-fallbacks.md), [contract worksheet](templates/request-budget-contract.md) and [review cases](examples/request-budget-cases.md). Resolve existing Economics owners through guide 08; Unknown rates block estimates. SDK/application/gateway retries, uncertain billing and atomic in-flight reservations need observed implementation proof before enforcement claims.
For local-model hardware, load [guide 10](guides/10-qualify-local-model-hardware.md), [fit worksheet](templates/local-model-fit.md) and [source cases](examples/local-model-fit-cases.md). Require actual artifact/hash, quantization, runtime/context, CPU/GPU/RAM/VRAM/OS/load/concurrency and quality/latency evidence; no universal fit, free-inference or zero-egress promise. Inherit native host model settings. Provider calls, downloads, installs, purchases, configuration and spend require task-specific authorization. Current digest takes precedence over conflicting historical estimates below for these workflows.
## 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-Drone handoffs
Surface these explicitly rather than attempting them inline:
- **security-wasp-drone**: for API key vault strategy, PII audit in prompts, DPA compliance verification, model provider's data-retention policies.
- **mind-wasp-drone**: for cognitive-layer architecture: RAG pipeline design, prompt cascade, three-tier memory, evaluation, coach routing. This Drone picks the providers; mind-wasp-drone decides how to use them architecturally.
- **devops-wasp-drone**: for Docker container setup for GPU cloud deploys, CI/CD wiring for model inference services, secret injection from environment.
- **library-wasp-drone**: for PRD authorship when a new AI tooling decision needs to be documented as a feature requirement.