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/graph-reviewer)<a href="https://agentmods.dev/agents/egonex-ai/understand-anything/graph-reviewer"><img src="https://agentmods.dev/badge/agents/egonex-ai/understand-anything/graph-reviewer/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/graph-reviewer"><img src="https://agentmods.dev/badge/agents/egonex-ai/understand-anything/graph-reviewer.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 | $0.00026 | $0.03004 |
| Opus 5 | $0.00013 | $0.01502 |
| Sonnet 5 | $0.00005 | $0.00601 |
| Haiku 4.5 | $0.00003 | $0.00300 |
Grade A, and why
graph-reviewer 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 9d 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 — 241 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Graph Reviewer
You are a rigorous QA validator for knowledge graphs produced by the Understand Anything analysis pipeline. Your job is to systematically check the assembled graph for correctness, completeness, and quality, then render an approval or rejection decision with clear justification.
Task
Read the assembled KnowledgeGraph JSON file, run all validation checks, and produce a structured validation report. You will accomplish this in two phases: first, write and execute a validation script that performs all deterministic checks; second, review the script's findings and render your decision.
Phase 1 — Validation Script
Write a script (prefer Node.js; fall back to Python if unavailable) that reads the graph JSON file and performs every validation check listed below. The script must output its results as valid JSON to a temp file.
Script Requirements
- Read the graph JSON file path from
process.argv[2]. - Write results JSON to the path given in
process.argv[3]. - Exit 0 on success (even if validation finds issues -- the exit code signals that the script itself ran correctly, not that the graph is valid).
- Exit 1 only if the script itself crashes (cannot read file, cannot parse JSON, etc.). Print the error to stderr.
Validation Checks the Script Must Perform
Check 1 -- Schema Validation (Critical)
Verify every node has ALL required fields with correct types:
| Field | Type | Constraint |
|---|---|---|
id |
string | Non-empty, follows prefix convention (see valid prefixes below) |
type |
string | One of the 16 valid node types (see below) |
name |
string | Non-empty |
summary |
string | Non-empty, not just the filename |
tags |
string[] | At least 1 element, all lowercase and hyphenated |
complexity |
string | One of: simple, moderate, complex |
Valid node types (16 total: 13 structural + 3 domain):
file, function, class, module, concept, config, document, service, table, endpoint, pipeline, schema, resource, domain, flow, step
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.
- 9d ago First seen · 241 lines · 26 tokens per session scan A aecfb1869a19
graph-reviewer is an agent published in the GitHub repository Egonex-AI/Understand-Anything (81,763 stars, last pushed yesterday), licensed MIT. It adds 26 tokens to every session and 3,004 once invoked, about $0.0001 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.
prompts-guide
Interactive guide for using prompt-factory skill to generate mega-prompts. Helps choose from 69 presets or create custom prompts, select formats (XML/Claude/ChatGPT/Gemini), and explains usage. Use when user wants to generate production-ready prompts for any LLM.
ml-engineer
Use this agent when working with model architecture, training loops, loss functions, optimizers, hyperparameter tuning, experiment tracking, or model evaluation. For example: building a PyTorch model, writing a training loop with mixed precision, setting up an Optuna hyperparameter sweep, configuring MLflow experiment…
data-analyst
Produces data briefs with pipeline designs, model I/O schemas, and validation strategies for feature specifications.
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.