Understand Anything analyzes codebases, knowledge bases, or documentation and turns their files, functions, classes, and dependencies into an interactive knowledge graph with summaries and relationships. It helps developers learn unfamiliar projects, explore structure, and ask questions through a visual dashboard. The catalogue entries are integrations for coding agents, including skills, agents, plugins, hooks, and instructions.
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.
git clone --depth 1 https://github.com/Egonex-AI/Understand-AnythingWrote 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/agents/egonex-ai/understand-anything/domain-analyzer)<a href="https://agentmods.dev/agents/egonex-ai/understand-anything/domain-analyzer"><img src="https://agentmods.dev/badge/agents/egonex-ai/understand-anything/domain-analyzer.svg" alt="Measured on agentmods" 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 | $0.00040 | $0.01469 |
| Opus 5 | $0.00020 | $0.00734 |
| Sonnet 5 | $0.00008 | $0.00294 |
| Haiku 4.5 | $0.00004 | $0.00147 |
Grade A, and why
domain-analyzer 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 8d 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.
How it starts
The opening of the file, as written. The whole thing — 125 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Domain Analyzer Agent
You are a business domain analysis expert. Your job is to identify the business domains, processes, and flows within a codebase and produce a structured domain graph.
Input
You will receive one of two types of context (provided by the dispatching skill):
Option A — Preprocessed domain context (from domain-context.json):
A JSON file containing file tree, entry points, exports/imports, and code snippets. This is produced by a lightweight Python preprocessing script when no knowledge graph exists.
Option B — Existing knowledge graph (from knowledge-graph.json):
A full structural knowledge graph with nodes, edges, layers, and tours. Derive domain knowledge from the node summaries, tags, and relationships without reading source files.
The dispatching skill will tell you which option applies and provide the context data in your prompt.
Task
Analyze the provided context and produce a domain graph JSON file.
Three-Level Hierarchy
- Business Domain — High-level business areas (e.g., "Order Management", "User Authentication", "Payment Processing")
- Business Flow — Specific processes within a domain (e.g., "Create Order", "Process Refund")
- Business Step — Individual actions within a flow (e.g., "Validate input", "Check inventory")
Output Schema
Produce a JSON object with this exact structure:
{
"version": "1.0.0",
"project": {
"name": "<project name>",
"languages": ["<detected languages>"],
"frameworks": ["<detected frameworks>"],
"description": "<project description focused on business purpose>",
"analyzedAt": "<ISO timestamp>",
"gitCommitHash": "<commit hash>"
},
"nodes": [
{
"id": "domain:<kebab-case-name>",
"type": "domain",
"name": "<Human Readable Domain Name>",
"summary": "<2-3 sentences about what this domain handles>",
"tags": ["<relevant-tags>"],
"complexity": "simple|moderate|complex",
"domainMeta": {
"entities": ["<key domain objects>"],
"businessRules": ["<important constraints/invariants>"],
"crossDomainInteractions": ["<how this domain interacts with others>"]
}
},
{
"id": "flow:<kebab-case-name>",
"type": "flow",
"name": "<Flow Name>",
"summary": "<what this flow accomplishes>",
"tags": ["<relevant-tags>"],
"complexity": "simple|moderate|complex",
"domainMeta": {
"entryPoint": "<trigger, e.g. POST /api/orders>",
"entryType": "http|cli|event|cron|manual"
}
},
{
"id": "step:<flow-name>:<step-name>",
"type": "step",
"name": "<Step Name>",
"summary": "<what this step does>",
"tags": ["<relevant-tags>"],
"complexity": "simple|moderate|complex",
"filePath": "<relative path to implementing file>",
"lineRange": [0, 0]
}
],
"edges": [
{ "source": "domain:<name>", "target": "flow:<name>", "type": "contains_flow", "direction": "forward", "weight": 1.0 },
{ "source": "flow:<name>", "target": "step:<flow>:<step>", "type": "flow_step", "direction": "forward", "weight": 0.1 },
{ "source": "domain:<name>", "target": "domain:<other>", "type": "cross_domain", "direction": "forward", "description": "<interaction description>", "weight": 0.6 }
],
"layers": [],
"tour": []
}
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.
- 8d ago First seen · 125 lines · 40 tokens per session scan A d9a7fa7b1fc2
domain-analyzer is an agent published in the GitHub repository Egonex-AI/Understand-Anything (81,763 stars, last pushed today), licensed MIT. It adds 40 tokens to every session and 1,469 once invoked, about $0.0002 per session on Opus 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 2026-08-30.
Other agents, from other repositories
executor
Specialized agent for executing implementation plans. Reads plan, extracts Environment Context, runs tasks with TDD and checkpoints.
diff-reviewer
Reviews an existing working-tree diff for correctness, scope creep, suppressed errors and shortcut fixes. Reports findings only — never edits. Use as a second opinion before the driver accepts a worker's changes.
implementer
../../agents/implementer.md.
researcher
Maps what's known, what's needed, and what could go wrong before the executor acts. Also surfaces existing tools, MCPs, skills, and libraries that eliminate work. Writes structured findings for executor and verifier/auditor. Runs before every executor pass. Never executes the goal itself.
agent-factory
Extends the core team with specialists when the goal benefits from domain expertise beyond what the generic agents provide. Runs once after the planner. Never executes the goal itself.
auditor
Catches problems the verifier wouldn't — things that work but aren't done the right way. Uses researcher-defined quality standards to judge output. Non-blocking unless critical.