Borrowing it
Nothing to install: this file belongs to diillson/chatcli. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/diillson/chatcli/main/.agent/skills/intelligent-routing/SKILL.mdgit clone --depth 1 https://github.com/diillson/chatcliWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/diillson/chatcli/intelligent-routing)<a href="https://agentmods.dev/skills/diillson/chatcli/intelligent-routing"><img src="https://agentmods.dev/badge/skills/diillson/chatcli/intelligent-routing/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/diillson/chatcli/intelligent-routing"><img src="https://agentmods.dev/badge/skills/diillson/chatcli/intelligent-routing.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00032 | $0.02475 |
| Opus 5.5 | $0.00013 | $0.00990 |
| Sonnet 5.5 | $0.00006 | $0.00495 |
| Haiku 4.5 | $0.00003 | $0.00248 |
Grade A, and why
intelligent-routing scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
88% identical to intelligent-routing — 14 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 336 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Intelligent Agent Routing
Purpose: Automatically analyze user requests and route them to the most appropriate specialist agent(s) without requiring explicit user mentions.
Core Principle
The AI should act as an intelligent Project Manager, analyzing each request and automatically selecting the best specialist(s) for the job.
How It Works
1. Request Analysis
Before responding to ANY user request, perform automatic analysis:
graph TD
A[User Request: Add login] --> B[ANALYZE]
B --> C[Keywords]
B --> D[Domains]
B --> E[Complexity]
C --> F[SELECT AGENT]
D --> F
E --> F
F --> G[security-auditor + backend-specialist]
G --> H[AUTO-INVOKE with context]
2. Agent Selection Matrix
Use this matrix to automatically select agents:
| User Intent | Keywords | Selected Agent(s) | Auto-invoke? |
|---|---|---|---|
| Authentication | "login", "auth", "signup", "password" | security-auditor + backend-specialist |
✅ YES |
| UI Component | "button", "card", "layout", "style" | frontend-specialist |
✅ YES |
| Mobile UI | "screen", "navigation", "touch", "gesture" | mobile-developer |
✅ YES |
| API Endpoint | "endpoint", "route", "API", "POST", "GET" | backend-specialist |
✅ YES |
| Database | "schema", "migration", "query", "table" | database-architect + backend-specialist |
✅ YES |
| Bug Fix | "error", "bug", "not working", "broken" | debugger |
✅ YES |
| Test | "test", "coverage", "unit", "e2e" | test-engineer |
✅ YES |
| Deployment | "deploy", "production", "CI/CD", "docker" | devops-engineer |
✅ YES |
| Security Review | "security", "vulnerability", "exploit" | security-auditor + penetration-tester |
✅ YES |
| Performance | "slow", "optimize", "performance", "speed" | performance-optimizer |
✅ YES |
| Product Def | "requirements", "user story", "backlog", "MVP" | product-owner |
✅ YES |
| New Feature | "build", "create", "implement", "new app" | orchestrator → multi-agent |
⚠️ ASK FIRST |
| Complex Task | Multiple domains detected | orchestrator → multi-agent |
⚠️ ASK FIRST |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 336 lines · 32 tokens per session scan A b0da558d5cbd
intelligent-routing is a skill published in the GitHub repository diillson/chatcli (93 stars, last pushed yesterday), licensed Apache-2.0. It adds 32 tokens to every session and 2,475 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 88% identical to intelligent-routing, differing in 14 lines, and is treated as a copy.
Other skills, from other repositories
agent-state-bar
A design guide for an agent status bar: a short, automatically updated summary of progress, remaining tasks, tool counts, errors, and system state placed near the latest model input.
context-engineering
A guide to organizing the instructions and information an AI agent receives at each step. It covers system prompts, tool descriptions, conversation history, on-demand skills, and protections against unsafe outside content.
kv-cache-design
A guide to arranging an AI agent's conversation so previously processed text can be reused. A KV cache stores intermediate results for earlier tokens, while a prompt cache reuses a stable beginning across requests.
loop-engineering
A guide to building reliable loops for AI agents that plan, act, check their work, and continue until a task is truly complete. It includes independent verification and a proposer-reviewer pattern in which one agent's work is checked using outside evidence.
reward-design
A guide to defining the scores that an AI agent receives during trial-and-error training. It covers checks based on rules, human preferences, or another model, and distinguishes rewarding the final result from rewarding the steps taken.
pptx
A guide for turning a paper, outline, or structured text into a PowerPoint presentation. It covers an 8–12-slide structure, concise slide points, and generation of a .pptx file.