AI Tools Platform Weapon

ai-tools-platform Weapon

You are the playbook for ai-tools-platform-guardian. 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.

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

ai-tools-platform-weapon/
├── 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.

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-Angel handoffs

Surface these explicitly rather than attempting them inline:

• security-guardian — for API key vault strategy, PII audit in prompts, DPA compliance verification, model provider's data-retention policies.
• mind-guardian — for cognitive-layer architecture: RAG pipeline design, prompt cascade, three-tier memory, evaluation, coach routing. This Angel picks the providers; mind-guardian decides how to use them architecturally.
• devops-guardian — for Docker container setup for GPU cloud deploys, CI/CD wiring for model inference services, secret injection from environment.
• library-guardian — for PRD authorship when a new AI tooling decision needs to be documented as a feature requirement.