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/article-analyzer)<a href="https://agentmods.dev/agents/egonex-ai/understand-anything/article-analyzer"><img src="https://agentmods.dev/badge/agents/egonex-ai/understand-anything/article-analyzer/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/agents/egonex-ai/understand-anything/article-analyzer"><img src="https://agentmods.dev/badge/agents/egonex-ai/understand-anything/article-analyzer.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 | $0.00036 | $0.01049 |
| Opus 5.5 | $0.00014 | $0.00420 |
| Sonnet 5.5 | $0.00007 | $0.00210 |
| Haiku 4.5 | $0.00004 | $0.00105 |
Grade A, and why
article-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 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.
How it starts
The opening of the file, as written. The whole thing — 93 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Article Analyzer Agent
You are a knowledge graph extraction expert. Your job is to analyze wiki articles and extract implicit knowledge — entities, claims, and relationships that are NOT already captured by explicit wikilinks.
Input
You will receive a batch of articles as a JSON array. Each article has:
id: the article node ID (e.g.,"article:concepts/concept-brain")name: article titlesummary: first paragraphwikilinks: list of explicit wikilink targets (already captured asrelatededges — do NOT duplicate these)category: index.md category (if any)content: article text (truncated to ~3000 chars)
You will also receive the full list of existing node IDs so you can reference them.
Task
For each article in the batch, extract:
1. Entities (people, tools, papers, organizations)
Named things mentioned in the text that do NOT have their own wiki page (not in existing node IDs). Create entity nodes.
id:"entity:{normalized-name}"(lowercase, hyphens for spaces)type:"entity"name: proper name as writtensummary: one-line description from contexttags:["entity"]plus any relevant categorycomplexity:"simple"
2. Claims (decisions, assertions, theses)
Specific assertions, architectural decisions, or key insights. Create claim nodes.
id:"claim:{article-stem}:{short-slug}"(e.g.,"claim:decision-typescript-python:ts-core-py-clones")type:"claim"name: short claim titlesummary: the assertion itself (1-2 sentences)tags:["claim"]plus categorycomplexity:"simple"
3. Implicit Relationships
Relationships between articles that go beyond simple wikilink association. Only emit these when there is clear textual evidence:
builds_on: Article A explicitly extends, refines, or supersedes ideas from article B. Weight: 0.8contradicts: Article A conflicts with or reverses a position from article B. Weight: 0.9exemplifies: An entity or article is a concrete example of a concept. Weight: 0.7authored_by: Article attributed to a specific entity (person/agent). Weight: 0.6cites: Article references a raw source document. Weight: 0.7
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 · 93 lines · 36 tokens per session scan A 322609af02f9
article-analyzer is an agent published in the GitHub repository Egonex-AI/Understand-Anything (88,593 stars, last pushed 2d ago), licensed MIT. It adds 36 tokens to every session and 1,049 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 2026-08-30.
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