Skill: dagangtj/smart-spawn-api (version 1.0.0)

--- name: smart-spawn-api description: "Pick the best AI model for any task using the Smart Spawn API. No plugin needed — just HTTP requests to ss.deeflect.com/api." --- # Smart Spawn API Pick the best AI model for any task. Call the API, get a model recommendation, spawn with it. **No plugin required.** Works with any OpenClaw instance or any HTTP client. ## Quick Start ``` 1. GET ss.deeflect.com/api/pick?task=<description>&budget=<tier> 2. Use the returned model ID in sessions_spawn ``` ## Pick Best Model ```bash GET https://ss.deeflect.com/api/pick?task=build+a+react+dashboard&budget=medium ``` Response: ```json { "data": { "id": "anthropic/claude-opus-4.6", "name": "Claude Opus 4.6", "score": 86, "pricing": { "prompt": 5, "completion": 25 }, "reason": "Best general model at medium budget ($0-5/M) — score: 86" } } ``` Then spawn: ``` sessions_spawn(task="Build a React dashboard with auth", model="anthropic/claude-opus-4.6") ``` ## Parameters | Param | Required | Description | |-------|----------|-------------| | `task` | Yes | Task description or category: `coding`, `reasoning`, `creative`, `vision`, `research`, `fast-cheap`, `general` | | `budget` | No | `low` ($0-1/M), `medium` ($0-5/M, default), `high` ($2-20/M), `any` | | `exclude` | No | Comma-separated model IDs to skip | | `context` | No | Tags like `vision,long-context` for routing boost | ## Get Multiple Recommendations ```bash GET https://ss.deeflect.com/api/recommend?task=coding&budget=low&count=3 ``` Returns diverse models from different providers. Use for collective/parallel spawning. ## Compare Models ```bash GET https://ss.deeflect.com/api/compare?models=anthropic/claude-opus-4.6,openai/gpt-5.2 ``` Side-by-side scores, pricing, capabilities. ## Browse All Models ```bash GET https://ss.deeflect.com/api/models?category=coding&sort=score&limit=10 ``` Sort by `score`, `cost`, `efficiency`, or any category name. ## Decompose Complex Tasks ```bash POST https://ss.deeflect.com/api/decompose {"task": "Build and deploy a SaaS app", "budget": "medium"} ``` Returns sequential steps with optimal model per step. ## Swarm (Parallel DAG) ```bash POST https://ss.deeflect.com/api/swarm {"task": "Research competitors and build pitch deck", "budget": "low"} ``` Returns a dependency graph of parallel tasks with models assigned. ## Usage Pattern For any task that needs a sub-agent: 1. **Decide budget** — `low` for cheap/fast, `medium` for quality, `high` for best available 2. **Call /api/pick** with the task description 3. **Use the returned `id`** as the `model` parameter in `sessions_spawn` 4. **If task is complex** — use `/api/decompose` or `/api/swarm` to break it into subtasks, spawn each with its recommended model ## Error Handling - API down → skip model selection, use `sessions_spawn` without a model (falls back to default) - No model found (404) → widen budget to `any` and retry - Rate limited (429) → wait and retry, or fall back to default ## API Status ```bash GET https://ss.deeflect.com/api/status ``` Shows model count, data freshness, source health. Data refreshes every 6 hours from 5 benchmark sources.

path: _meta.json
content: ownerId: kn72k5yywr6jmsfr2x7h68z03980dvzx
slug: smart-spawn
version: 1.0.0
publishedAt: 1771366993458

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