intent-classification

intent-classification is a skill for Claude Code, Codex from xiaoranchenwai/12345-hotline-agent. It costs 29 tokens per session (248 once invoked), scanned A, original, no licence file.

A skill that sorts natural-language user messages into three categories: complaint, inquiry, or suggestion. The result can be used to decide where each message should go.

In plain words
What is it for?
Use it to classify incoming user text for routing decisions in a larger assistant or support workflow.
Why use it?
It removes the need to manually interpret every message before routing it to the right part of an application.

Skill for Claude CodeCodex

Which agent this was written for is unclear — body not stored (licence); the path alone says nothing.

Good fit Use it to classify incoming user text for routing decisions in a larger assistant or support workflow.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/xiaoranchenwai/12345-hotline-agent/intent-classification
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add xiaoranchenwai/12345-hotline-agent --skill intent-classification
Clone the repo
git clone --depth 1 https://github.com/xiaoranchenwai/12345-hotline-agent

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for intent-classification

README.md
[![agentmods](https://agentmods.dev/badge/skills/xiaoranchenwai/12345-hotline-agent/intent-classification/github.svg)](https://agentmods.dev/skills/xiaoranchenwai/12345-hotline-agent/intent-classification)
Your own site
<a href="https://agentmods.dev/skills/xiaoranchenwai/12345-hotline-agent/intent-classification"><img src="https://agentmods.dev/badge/skills/xiaoranchenwai/12345-hotline-agent/intent-classification/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.

agentmods 80×15 button for intent-classification

Your own site · 80×15
<a href="https://agentmods.dev/skills/xiaoranchenwai/12345-hotline-agent/intent-classification"><img src="https://agentmods.dev/badge/skills/xiaoranchenwai/12345-hotline-agent/intent-classification.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 29 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 248 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin unknown No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00029 $0.00248
Opus 5.5 $0.00012 $0.00099
Sonnet 5.5 $0.00006 $0.00050
Haiku 4.5 $0.00003 $0.00025

Measured 1mo ago against content hash 3a1ee0b1540a, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

intent-classification 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.

skills/intent-classification/SKILL.md · 27 lines

The source is not reproduced here

No licence file

A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.

Read it on GitHub

Changes

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.

  1. 1mo ago First seen · 27 lines · 29 tokens per session scan A 3a1ee0b1540a

Subscribe to this mod's changes

intent-classification is a skill published in the GitHub repository xiaoranchenwai/12345-hotline-agent (5 stars, last pushed 6mo ago), with no licence file. It adds 29 tokens to every session and 248 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

Related

Other skills, from other repositories

agent-builder

Build production-ready LLM agents with LangGraph, tool use, memory, streaming, and error handling. Use when designing or implementing an AI agent, multi-agent system, or agentic workflow.

chandrudp29/skillhub · 42 tokens

rag-retrieval

Retrieval-Augmented Generation patterns for grounded LLM responses. Use when building RAG pipelines, embedding documents, implementing hybrid search, contextual retrieval, HyDE, agentic RAG, multimodal RAG, query decomposition, reranking, or pgvector search.

yonatangross/orchestkit · 58 tokens

error-analysis

Evals-first error analysis for LLM apps: clusters real Langfuse or JSONL traces into a human-confirmed failure taxonomy with counts, then recommends binary pass/fail evals for recurring named modes. Use to learn what to measure before writing evals. Not for CI failures.

yonatangross/orchestkit · 61 tokens

llm-integration

LLM integration patterns for function calling, streaming responses, local inference with Ollama, and fine-tuning customization. Use when implementing tool use, SSE streaming, local model deployment, LoRA/QLoRA fine-tuning, or multi-provider LLM APIs.

yonatangross/orchestkit · 58 tokens

golden-dataset

Golden dataset lifecycle patterns for curation, versioning, quality validation, and CI integration. Use when building evaluation datasets, managing dataset versions, validating quality scores, or integrating golden tests into pipelines.

yonatangross/orchestkit · 44 tokens

multimodal-llm

Vision, audio, video generation, and multimodal LLM integration patterns. Use when processing images, transcribing audio, generating speech, generating AI video (Kling v3, Sora 2, Veo 3.1 std/lite/fast, Runway Gen-4.5 via gen4turbo), or building multimodal AI pipelines.

yonatangross/orchestkit · 82 tokens