Deep Infra

Setup#

On first use, read setup.md to align activation boundaries, reliability goals, and routing preferences before making configuration changes.

When to Use#

Use this skill when the user wants to connect an OpenAI-compatible workflow to DeepInfra, choose open-source and frontier models by task type, set safe fallbacks, and control cost drift over time.

Architecture#

Memory lives in ~/deep-infra/. See memory-template.md for structure.

text

~/deep-infra/
├── memory.md            # Active routing profile and constraints
├── providers.md         # Confirmed provider and auth choices
├── routing-rules.md     # Task -> model and fallback policy
├── incidents.md         # Outages, rate limits, and recovery notes
└── budgets.md           # Spend guardrails and optimization actions

Quick Reference#

Use the smallest relevant file for the current task.

TopicFileSetup and activation preferencessetup.mdMemory templatememory-template.mdAuthentication and provider wiringauth-and-provider.mdRouting patterns by workloadrouting-playbooks.mdReliability and fallback handlingfallback-reliability.mdCost controls and spend reviewscost-guardrails.md

Core Rules#

1. Start from Workload Classes, Not Model Hype#

• Classify requests first: coding, analysis, extraction, summarization, or long-context synthesis.
• Map each class to a primary model and a fallback before changing any defaults.

2. Keep Authentication Explicit and Verifiable#

• Use DEEPINFRA_API_KEY from the local environment, never pasted into logs or chat memory.
• Validate auth with a minimal request before applying routing changes.

3. Design Fallbacks for Failure Modes, Not Convenience#

• Separate fallback reasons: rate limit, provider outage, latency spike, or output quality failure.
• Keep at least one fallback from a different model family for resilience.

4. Leverage Open-Source Model Diversity#

• DeepInfra hosts models from many providers (DeepSeek, Moonshot, MiniMax, StepFun, NVIDIA, and more).
• Use model diversity to build resilient fallback chains across independent model families.

5. Enforce Cost Boundaries Before Throughput Tuning#

• Set cost ceilings by task class and check expected token burn before broad rollout.
• Route low-stakes tasks to cheaper models and reserve premium models for high-impact tasks.

6. Change One Layer at a Time#

• Modify either model selection, fallback policy, or budget limits in a single iteration.
• After each change, run a quick verification prompt set and record outcome.

7. Record Decisions for Repeatability#

• Save the final routing policy, rationale, and known tradeoffs in memory.
• Reuse proven policies instead of repeatedly rebuilding from scratch.

Common Traps#

• Choosing one model for every task -> higher cost and unstable quality under varied workloads.
• Using same-family fallback chain only -> cascading failures during model-specific incidents.
• Ignoring token limits for long inputs -> truncated responses and hidden quality loss.
• Changing routing and budgets simultaneously -> unclear root cause when quality drops.
• Running without verification prompts -> broken routing detected only after user-facing failures.

External Endpoints#

These endpoints are used only to discover model metadata and execute routed inference requests under explicit user task intent.

EndpointData SentPurposehttps://api.deepinfra.com/v1/openai/modelsnone or auth headerDiscover current model catalog and metadatahttps://api.deepinfra.com/v1/openai/chat/completionsuser prompt content and selected model idExecute routed inference requests

No other data is sent externally.

Security & Privacy#

Data that leaves your machine:

• Prompt text and selected model metadata sent to DeepInfra when inference is requested.

Data that stays local:

• Routing notes and preferences under ~/deep-infra/.
• Local environment variable references and verification logs.

This skill does NOT:

• Request raw API keys in chat.
• Store plaintext secrets in skill memory files.
• Modify files outside ~/deep-infra/ for its own state.

Trust#

By using this skill, prompt content is sent to DeepInfra for model execution. Only install if you trust this service with your data.

Related Skills#

Install with clawhub install <slug> if user confirms:

• api — API request design, payload shaping, and response validation patterns
• auth — credential handling and auth troubleshooting workflows
• models — model comparison and selection guidance
• monitoring — runtime health checks and incident tracking practices

Feedback#

• If useful: clawhub star deep-infra
• Stay updated: clawhub sync