Skill: ats3v/deep-infra (version 1.0.0)

--- name: Deep Infra slug: deep-infra version: 1.0.0 homepage: https://clawhub.ai/ats3v/deep-infra description: Configure DeepInfra model routing with provider auth, model selection, fallback chains, and cost-aware defaults for stable open-source and frontier model workflows. changelog: Initial release with practical DeepInfra setup, routing rules, fallback reliability, and budget-safe operating guidance. metadata: {"clawdbot":{"emoji":"🧠","requires":{"bins":["curl","jq"],"env":["DEEPINFRA_API_KEY"]},"os":["linux","darwin","win32"]}} --- ## 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`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)