Deep Infra

Configure DeepInfra model routing with provider auth, model selection, fallback chains, and cost-aware defaults for stable open-source and frontier model wor...

Security Scan

The skill's files, runtime instructions, and required environment variables are consistent with its stated purpose (DeepInfra routing and model selection); the main user risk is trusting the remote DeepInfra service and protecting the DEEPINFRA_API_KEY.

Purpose & Capability

Name/description match the requested resources: curl/jq are used in examples, and DEEPINFRA_API_KEY is the expected credential for calling api.deepinfra.com. The documented files (routing playbooks, auth, setup, cost guardrails) align with a routing/configuration skill.

Instruction Scope

Runtime instructions operate on ~/deep-infra/ and call DeepInfra endpoints via curl/jq; they do not request unrelated system credentials or cross-check unrelated files. Note: the README shows an example CLI invocation (openclaw onboard --deepinfra-api-key <key>) that would place the key on the command line (process args) and could expose it to local process listings — prefer environment variables or secure input. Also verify created memory files do not accidentally include secrets.

Install Mechanism

Instruction-only skill with no install spec and no remote downloads; lowest install risk. Required binaries are minimal and expected for the provided curl/jq examples.

Credentials

Only a single environment variable (DEEPINFRA_API_KEY) is required and is justified by the skill's purpose. No unrelated secrets, config paths, or additional credentials are requested.

Persistence & Privilege

The skill stores state under ~/deep-infra/ (documented) and does not request always:true or other elevated persistent privileges. It does not claim to modify other skills or system-wide settings.

Assessment

This skill appears coherent for configuring DeepInfra routing. Before installing: 1) Confirm you trust api.deepinfra.com (data sent there includes user prompts). 2) Protect your DEEPINFRA_API_KEY — set it as an environment variable rather than passing it on a command line to avoid process-list exposure. 3) Inspect ~/deep-infra/ after first run to ensure no secrets are stored in plaintext. 4) If you need tighter controls, use a least-privileged/rotation-capable key and review DeepInfra's data retention and privacy terms. If any of these checks fail or DeepInfra is untrusted, do not install.

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.

~/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.

Topic	File
Setup and activation preferences	setup.md
Memory template	memory-template.md
Authentication and provider wiring	auth-and-provider.md
Routing patterns by workload	routing-playbooks.md
Reliability and fallback handling	fallback-reliability.md
Cost controls and spend reviews	cost-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.

Endpoint	Data Sent	Purpose
https://api.deepinfra.com/v1/openai/models	none or auth header	Discover current model catalog and metadata
https://api.deepinfra.com/v1/openai/chat/completions	user prompt content and selected model id	Execute 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