IntentRouter

Your AI's Smart Traffic Director: Precisely Matching Your OpenClaw Tasks to the Perfect LLM.

v1.7.0 — Security-focused release. COMPLEX tier, absolute paths. Tested and working with OpenClaw TUI delegation. Removed gateway auth secret exposure and gateway management for improved security.

IntentRouter analyzes your tasks and directs them to the best LLM—MiniMax 2.5 for code, Kimi k2.5 for creative, Grok Fast for research. Eliminate guesswork; route with purpose.

Security improvements in v1.7.0: Removed gateway auth token/password from router output. Gateway management removed - use gateway-guard skill separately. FACEPALM integration removed - use FACEPALM skill separately.

Requirements: OpenRouter — All model IDs use the

openrouter/...

prefix. Configure OpenClaw with an OpenRouter API key so one auth profile covers every tier.

Config access: This skill reads ONLY its own

config.json

file (located in the skill directory) for model configuration and routing rules. It does NOT read

openclaw.json

, does NOT access gateway tokens/passwords, and does NOT perform any gateway management operations. Router output contains only:

task

,

model

,

sessionTarget

, and optional

label

— no secrets or credentials.

Default / orchestrator model: Gemini 2.5 Flash (

openrouter/google/gemini-2.5-flash

) — fast, cheap, reliable at tool-calling. The router delegates tasks to tier-specific sub-agents (Kimi for creative, MiniMax 2.5 for code, etc.).

Orchestrator flow (mandatory for task requests)

When you are the main agent (Gemini 2.5 Flash) and the user gives you a task (code, research, write, create, design, poem, story, fix, build, etc.), you must delegate it. You do not perform the task yourself; a sub-agent does.

Steps (all three required):

1. Run IntentRouter to get spawn params:

   python3 workspace/skills/friday-router/scripts/router.py spawn --json "<user message>"
   

   Example output:

   {"task":"write a poem about Mai","model":"openrouter/moonshotai/kimi-k2.5","sessionTarget":"isolated"}

2. Immediately call

   sessions_spawn

   with the router's output. Use the exact

   model

   from the JSON. Example:

   sessions_spawn(task="write a poem about Mai", model="openrouter/moonshotai/kimi-k2.5", sessionTarget="isolated")
   

   Do not change the

   model

   value. If the router said

   openrouter/moonshotai/kimi-k2.5

   , pass exactly that.

3. Forward the sub-agent's reply to the user. That reply IS the task output. Say "Using: Kimi k2.5" (the model that actually ran). Never say "Using: Claude Sonnet 4" or any model that didn't run. Output hygiene: If the sub-agent result contains internal text ("A subagent task … completed", "Findings:", "Stats:", "sessionKey", "Summarize this naturally"), strip that block and show only the final user-facing content to the user.

If

sessions_spawn

returns an error (e.g.

device_token_mismatch

): tell the user delegation failed and suggest checking gateway status or using the

gateway-guard

skill. Do not do the task yourself.

Hard-stop rule: If

sessions_spawn

fails or is skipped, return only the delegation error and next-step fix. Do not write the requested output directly.

No-classify execution rule: For real user tasks, do not execute via

classify

.

classify

is diagnostics only. Execution must use

spawn --json

->

sessions_spawn

.

Label gate: Only print

Using: <model>

after successful spawn. If no successful spawn, do not print a

Using:

label.

Output hygiene: Never return internal orchestration metadata to the user (no session keys/IDs, transcript paths, runtime/token stats, or internal "summarize this" instructions). Forward only clean user-facing content.

Exception: Meta-questions ("what model are you?", "how does routing work?") you answer yourself.

Security note: This skill does NOT expose gateway auth secrets (tokens/passwords) in its output. Gateway management functionality has been removed. Use the separate

gateway-guard

skill if gateway auth management is needed.

Model Selection (Austin's Prefs)

Use Case	Primary (OpenRouter)	Fallback
Default / orchestrator	Gemini 2.5 Flash	—
Fast/cheap	Gemini 2.5 Flash	Gemini 1.5 Flash, Haiku
Reasoning	GLM-5	Minimax 2.5
Creative/Frontend	Kimi k2.5	—
Research	Grok Fast	—
Code/Engineering	MiniMax 2.5	Qwen2.5-Coder
Quality/Complex	GLM 4.7 Flash	GLM 4.7, Sonnet 4, GPT-4o
Vision/Images	GPT-4o	—

All model IDs use

openrouter/

prefix (e.g.

openrouter/moonshotai/kimi-k2.5

).

Usage

CLI

python scripts/router.py default                          # Show default model
python scripts/router.py classify "fix lint errors"        # Classify → tier + model
python scripts/router.py spawn --json "write a poem"       # JSON for sessions_spawn (no gateway secrets)
python scripts/router.py models                            # List all models

Note: Gateway auth management is not included. Use

gateway-guard

skill separately if needed.

sessions_spawn examples

Creative task (poem):

router output: {"task":"write a poem","model":"openrouter/moonshotai/kimi-k2.5","sessionTarget":"isolated"}
→ sessions_spawn(task="write a poem", model="openrouter/moonshotai/kimi-k2.5", sessionTarget="isolated")

Code task (bug fix):

router output: {"task":"fix the login bug","model":"openrouter/minimax/minimax-m2.5","sessionTarget":"isolated"}
→ sessions_spawn(task="fix the login bug", model="openrouter/minimax/minimax-m2.5", sessionTarget="isolated")

Research task:

router output: {"task":"research best LLMs","model":"openrouter/x-ai/grok-4.1-fast","sessionTarget":"isolated"}
→ sessions_spawn(task="research best LLMs", model="openrouter/x-ai/grok-4.1-fast", sessionTarget="isolated")

Tier Detection

• FAST: check, get, list, show, status, monitor, fetch, simple
• REASONING: prove, logic, analyze, derive, math, step by step
• CREATIVE: creative, write, story, design, UI, UX, frontend, website (website/frontend/landing projects → Kimi k2.5 only; do not use CODE tier)
• RESEARCH: research, find, search, lookup, web, information
• CODE: code, function, debug, fix, implement, refactor, test, React, JWT (code/API only; not website builds)
• QUALITY: complex, architecture, design, system, comprehensive
• VISION: image, picture, photo, screenshot, visual

What Changed from Original

Bug	Fix
Simple indicators inverted (high match = complex)	Now correctly: high simple keyword match = FAST tier
Agentic tasks not bumping tier	Multi-step tasks now properly bump to CODE tier
Vision tasks misclassified	Vision keywords now take priority over other classifications
Code keywords not detected	Added React, JWT, API, and other common code terms
Confidence always low	Now varies appropriately based on keyword match strength