Route tasks to the best AI model across paid subscriptions (Claude, ChatGPT, Codex, Gemini, Kimi) via OpenClaw gateway. Use when user mentions model routing, multi-model setup, "use Codex for this", "delegate to Gemini", "route to the best model", agent delegation, or has OpenClaw agents configured with multiple providers. Do NOT use for single-model conversations or general chat.
Standardmäßig ist der Prompt ausgewählt, der zuerst die Quelle prüft. Sie können zu einem direkten Befehl wechseln oder eine lokale Kopie herunterladen.
Quelldateien prüfen
Lesen Sie SKILL.md und alle von SkillsMP angezeigten Begleitdateien, bevor Sie sich für eine Installation entscheiden.
Mit Codex oder Claude installieren Kopieren Sie diesen Prompt, fügen Sie ihn in Codex, Claude oder einen anderen Assistant ein und lassen Sie die Skill-Seite prüfen und installieren.
Ein direkter Befehl überspringt den Prüf-Prompt. Prüfen Sie die Quelle, bevor Sie ihn ausführen.
Route tasks to the best AI model across paid subscriptions (Claude, ChatGPT, Codex, Gemini, Kimi) via OpenClaw gateway. Use when user mentions model routing, multi-model setup, "use Codex for this", "delegate to Gemini", "route to the best model", agent delegation, or has OpenClaw agents configured with multiple providers. Do NOT use for single-model conversations or general chat.
homepage
https://github.com/dorukardahan/ZeroAPI
user-invocable
true
compatibility
Requires OpenClaw 2026.2.6+ with at least one AI subscription. Bootstrap budget config requires 2026.2.14+.
Route incoming tasks to the optimal AI model across available providers. OpenClaw handles all API connections — this skill defines the classification and delegation logic. Classify each task by type and delegate to the appropriate agent/model.
First-Time Setup
When this skill is first loaded, determine the user's available providers:
Ask: "Which AI subscriptions do you have?" (Claude Max 5x/20x, ChatGPT Plus/Pro, Gemini Advanced, Kimi)
Map subscriptions to available tiers (see table below)
Disable tiers for missing providers — those decision steps get skipped
Confirm the active configuration with the user
If only Claude is available, all tasks stay on Opus. No routing needed — but conflict resolution and collaboration patterns still apply for judging task complexity.
To verify providers are actually working after setup, ask the user to run:
openclaw models status
Any model showing missing or auth_expired is not usable. Remove it from your active tiers until the user fixes it.
For full provider configuration details, consult references/provider-config.md (in the same directory as this SKILL.md).
GPQA (science): Gemini Pro 0.908, Opus 0.769, Codex 0.738*
Coding (SWE-bench): Codex 49.3*, Opus 43.3, Gemini Pro 35.1
Math (AIME '25): Codex 99.0*, Gemini Flash 97.0, Opus 54.0
IFBench (instruction following): Gemini Flash 0.780, Opus 0.639, Codex 0.590*
TAU-2 (agentic tool use): Kimi K2.5 0.959, Codex 0.811*, Opus 0.780
Scores marked with * are estimated from vendor reports, not independently verified. Source: Artificial Analysis API v4, February 2026. Structured data in benchmarks.json.
Decision Algorithm
Walk through these 9 steps IN ORDER for every incoming task. The FIRST match wins. If a required model is unavailable, skip that step and continue to the next.
Estimating token count for Step 1: Count characters in the input and divide by 4. 100k tokens ≈ 400,000 characters. If the user pastes a large file, codebase, or says "analyze this entire repo," assume it exceeds 100k.
Step
Signals
Route to
Fallbacks
1. Context >100k tokens
large file, long document, bulk, CSV, log dump, entire codebase, "analyze this PDF"
RESEARCH (Pro, 1M ctx)
Opus (200K)
2. Math / proof
calculate, solve, equation, proof, integral, probability, optimize, formula
Step 5 note: For sub-second TTFT needs (pings, health checks), use SIMPLE (Flash-Lite, 0.23s TTFT). For heartbeats and cron jobs, use FAST (Flash) — better instruction following (IFBench 0.780).
Disambiguation Examples
When a task matches multiple steps:
"Analyze this 200-page PDF and write a Python parser for it" -- Step 1 wins (context size), route to RESEARCH. Then delegate code writing to CODE as a follow-up.
"Quickly solve this integral" -- Step 2 wins over Step 5 (math trumps speed).
"Generate a JSON schema for this API" -- Step 8 wins (structured output, not code writing).
"Review this code and refactor the authentication module" -- Step 4 wins for review, then Step 3 for the refactor (delegate to CODE).
When NOT to Route
Do NOT route away from the current model when:
User explicitly requests a model. "Use Opus for this" or "don't delegate this" — always respect direct instructions.
Security-sensitive tasks. If the task involves credentials, private keys, secrets, or personally identifiable data, keep it on the main agent. Do not send sensitive content to sub-agents.
Debugging a specific model. If the user is testing or comparing model behavior, route to the model they specify.
Mid-conversation continuity. In a multi-turn conversation where the user asks a quick follow-up, do not switch models just because the follow-up is "simple." Stay on the current model for context continuity unless the user explicitly asks to delegate.
Conflict Resolution
When multiple steps seem to match, resolve with these priority rules:
Judgment trumps speed. If the task has ambiguity, nuance, or risk — stay on Opus.
Specialist trumps generalist. If a model has a standout benchmark for the exact task type, prefer it.
Code writing -- Codex. Code review -- Opus. Different models for writing vs judging.
Context overflow -- Gemini. Only Gemini models handle 1M context.
TTFT matters for interactive tasks. Flash-Lite (0.23s), Kimi (1.65s), and Opus (1.76s) respond fast. Codex (20s) and Pro (29.59s) are slow to start — don't use them for quick back-and-forth.
When truly tied -- Opus. Highest general intelligence, lowest risk of subtle errors.
Sub-Agent Delegation
Use OpenClaw's agent system to delegate:
/agent <agent-id> <instruction>
You send /agent codex <instruction> — OpenClaw spawns the sub-agent with that instruction.
The sub-agent runs in its own workspace and returns a text response.
Sub-agents do NOT share your conversation context or workspace files. Pass ALL necessary context in the instruction.
What to pass: The specific task, relevant code snippets, output format expectations, and constraints.
Examples
/agent codex Write a Python function that parses RFC 3339 timestamps with timezone support. Return only the code.
/agent gemini-researcher Analyze the differences between SQLite WAL mode and journal mode. Include benchmarks and a recommendation.
/agent gemini-fast Convert the following list into a markdown table with columns: Name, Role, Status.
/agent kimi-orchestrator Coordinate: (1) gemini-researcher gathers data on X, (2) codex writes a parser, (3) report results.
Error Handling and Retries
Timeout (no response within 60s): Retry once on same model. If it fails again, fall to next fallback.
Auth error (401/403): Do NOT retry — fall to next fallback immediately and tell user to re-authenticate. See references/oauth-setup.md.
Rate limit (429): Wait 30 seconds, retry once. If still limited, fall to next fallback.
Partial/garbage response: Retry once. If still broken, fall to next fallback.
Model unavailable: Skip that tier entirely and continue.
Maximum retries: 1 retry on same model, then next fallback. If ALL fallbacks fail, stay on Opus. Never retry more than 3 times total across all fallbacks.
When a fallback is triggered, briefly inform the user:
"Codex is unavailable, routing to Opus instead."
Multi-Turn Conversation Routing
Stay on the same model for follow-up messages in the same topic. Context continuity matters more than optimal model selection.
Re-route only when the task type clearly changes. Example: user discusses architecture (Opus) -- then says "now write the implementation" -- delegate code writing to Codex.
When switching models mid-conversation:
Summarize the relevant context from the current conversation.
Pass that summary as part of the delegation instruction.
Continue on the original model (Opus) with awareness of what the sub-agent produced.
Workspace Isolation
Sub-agents cannot read your files — paste content into the instruction.
Sub-agents cannot write to your workspace — output comes back as text.
Sub-agents share nothing with each other — complete isolation by design.
Collaboration Patterns
Pattern
Flow
Use when
Pipeline
Research Agent -- Main Agent -- Code Agent
Task requires gathering facts before implementing
Parallel + Merge
Main spawns Code (approach A) + Research (approach B), then merges
Exploring multiple solutions or under time pressure
Adversarial Review
Code Agent writes -- Main critiques -- Code revises
Security-sensitive or production-critical code
Orchestrated (Kimi)
/agent kimi-orchestrator Plan and execute: <task>
3+ agents in complex dependency graphs (Kimi: slowest at 39 tok/s, best at TAU-2 0.959)
Choose this for tasks requiring 3+ agents in complex dependency graphs. Caution: Kimi is slowest (39 tok/s) but best at tool orchestration (TAU-2: 0.959).
Fallback Chains
When a model is unavailable or rate-limited, fall through in reliability order.
Full Stack (4 providers)
Task Type
Primary
Fallback 1
Fallback 2
Fallback 3
Reasoning
Opus
Gemini Pro
Codex
Kimi K2.5
Code
Codex
Opus
Gemini Pro
Kimi K2.5
Research
Gemini Pro
Opus
Codex
Kimi K2.5
Fast tasks
Flash-Lite
Flash
Opus
Codex
Agentic
Kimi K2.5
Codex
Gemini Pro
Opus
Important: Always use cross-provider fallbacks. Same-provider fallbacks (e.g., Gemini Pro -- Flash) help with model-specific issues but not provider outages. Every fallback chain should span at least 2 different providers.
Claude + Gemini (2 providers)
Task Type
Primary
Fallback 1
Fallback 2
Reasoning
Opus
Gemini Pro
—
Code
Opus
Gemini Pro
—
Research
Gemini Pro
Opus
—
Fast tasks
Flash-Lite
Flash
Opus
Claude + Codex (2 providers)
Task Type
Primary
Fallback 1
Reasoning
Opus
Codex
Code
Codex
Opus
Everything else
Opus
Codex
Claude Only (1 provider)
All tasks route to Opus. No fallback needed.
Provider Setup
For auth setup, OAuth flows (including headless VPS), and multi-device safety details, consult references/oauth-setup.md (in the same directory as this SKILL.md).
For provider configuration (openclaw.json, per-agent models.json, Google Gemini workarounds), consult references/provider-config.md.
Quick reference:
Provider
Auth Method
Maintenance
Anthropic
Setup-token (OAuth)
Low — auto-refresh
Google Gemini
OAuth (CLI plugin)
Very low — long-lived tokens
OpenAI Codex
OAuth (ChatGPT PKCE)
Low — auto-refresh
Kimi
Static API key
None — never expires
Troubleshooting
For detailed troubleshooting, consult references/troubleshooting.md (in the same directory as this SKILL.md). Common issues:
"No API provider registered for api: undefined" -- Missing api field in provider config
"API key not valid" with Gemini subscription -- Wrong API type; use google-gemini-cli not google-generative-ai
Model shows missing -- Model ID mismatch; gemini-2.5-flash-lite (no -preview suffix)
Sub-agent "Unknown model" -- Provider missing from sub-agent's auth-profile
Cost Summary
Setup
Monthly
Notes
Claude only (Max 5x)
$100
No routing, Opus handles everything
Claude only (Max 20x)
$200
No routing, 20x rate limits
Balanced (Max 20x + Gemini)
$220
Adds Flash speed + Pro research
Code-focused (+ ChatGPT Plus)
$240
Adds Codex for code + math
Full stack (all 4, ChatGPT Plus)
$250
Full specialization
Full stack Pro (all 4, ChatGPT Pro)
$430
Maximum rate limits
Source: Artificial Analysis API v4, February 2026. Codex scores estimated (*) from OpenAI blog data. Structured benchmark data available in references/benchmarks.json.
