index

A system that classifies user questions and sends them to specialized agents, which are helpers focused on particular kinds of work.

In plain words
What is it for?
Routing questions by intent, using custom agents with dedicated knowledge bases, and handling tasks such as entity extraction, geocoding, or personalization.
Why use it?
It removes the need to choose the right specialist manually for each question.

Agent

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.

agentmods
npx agentmods add agents/vericle/intellyweave/index
Clone the repo
git clone --depth 1 https://github.com/vericle/intellyweave
Per session 0 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 2,139 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original 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 $0.00000 $0.02139
Opus 5 $0.00000 $0.01069
Sonnet 5 $0.00000 $0.00428
Haiku 4.5 $0.00000 $0.00214

Measured 3d ago against content hash ddfd4db293a4, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

index 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 3d 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.

docs/guides/agents/index.md · 322 lines

How it starts

The opening of the file, as written. The whole thing — 322 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Agents and Domain Router

Specialized tools and intelligent query routing for domain-specific intelligence analysis.

What It Does

IntellyWeave's agent system provides two capabilities:

  1. Domain Router: Classifies user queries and routes them to appropriate specialized agents
  2. Specialized Agents: System and custom agents that handle domain-specific queries
User Query → Domain Router → Classification → Specialized Agent → Response

This system enables:

  • Automatic routing to the right agent based on query intent
  • Custom agents with user-defined knowledge bases
  • System agents for common tasks (entity extraction, geocoding, personalization)
  • Decision tree integration where agents act as Tools

Use When

  • You need specialized handling for specific query types
  • You want to create custom agents with dedicated knowledge bases
  • You need entity extraction, geocoding, or personalization
  • You want automatic routing without manual agent selection

Agent Categories

IntellyWeave has two categories of agents:

System Agents (Option A: Decision Tree Tools)

Always-available tools that the decision tree can invoke directly:

Agent Purpose Inputs
QueryExtractorTool Extracts and enriches entities from documents query, documents
GeospatialTransformationTool Geocodes locations and creates virtual documents entities, user_interest, operation_mode
PersonalizationTool Adjusts workflow based on user preferences query, user_id

Custom User Agents (Option B: Router-Accessible)

User-defined agents that the Domain Router can select:

Agent Purpose Selection Criteria
PersonalizationAgent Workflow customization via router Query involves preferences
Custom Agents User-uploaded knowledge bases Query matches agent domain

How the System Works

Query Flow

1. User submits query
2. Domain Router analyzes query intent
3. Router classifies into domain (agent name or "not-related")
4. Classification stored in hidden_environment
5. Matching agent's is_tool_available() returns True
6. Agent processes query and returns response

Read the full file on GitHub · 322 lines

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. 3d ago First seen · 322 lines · 0 tokens per session scan A ddfd4db293a4

Subscribe to this mod's changes

index is an agent published in the GitHub repository vericle/intellyweave (75 stars, last pushed 7mo ago), licensed BSD-3-Clause. It costs nothing until one of its globs matches a file; then it loads 2,139 tokens. 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 2026-08-30.