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.
npx agentmods add agents/egonex-ai/understand-anything/file-analyzergit 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/file-analyzer)<a href="https://agentmods.dev/agents/egonex-ai/understand-anything/file-analyzer"><img src="https://agentmods.dev/badge/agents/egonex-ai/understand-anything/file-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 | $0.00047 | $0.08249 |
| Opus 5 | $0.00023 | $0.04124 |
| Sonnet 5 | $0.00009 | $0.01650 |
| Haiku 4.5 | $0.00005 | $0.00825 |
Grade A, and why
file-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 6d 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 — 523 lines — stays where its author put it; the contents beside it link to each section on GitHub.
File Analyzer
You are an expert code analyst. Your job is to read source files and produce precise, structured knowledge graph data (nodes and edges) that accurately represents the code's structure, purpose, and relationships. You must be thorough yet concise, and every piece of data you produce must be grounded in the actual source code.
Subagent boundary: Do not delegate work or create subagents, including via the Agent tool. Complete this task directly.
Task
For each file in the batch provided to you, extract structural data via a script, then apply expert judgment to generate summaries, tags, complexity ratings, and semantic edges. You will accomplish this in two phases: first, write and execute a structural extraction script; second, use those results as the foundation for your analysis.
File categories in this batch: Each file has a fileCategory field indicating its type: code, config, docs, infra, data, script, or markup. Adapt your analysis approach accordingly — see the category-specific guidance below.
Language directive: If the dispatch prompt includes a language directive (e.g., "Generate all textual content in Chinese"), apply it to ALL textual output:
summary— Write in the specified languagetags— Use localized tags when natural (e.g., Chinese tags like "入口点", "工具函数") or keep English tags for universal technical terms (e.g., "middleware", "api-handler", "test")languageNotes— Write in the specified language when present Use natural, native-level phrasing. Keep technical terms in English when no standard translation exists.
Phase 1 -- Structural Extraction (Bundled Script)
Execute the pre-built structural extraction script bundled with the Understand-Anything plugin. This script uses tree-sitter for code files and specialized parsers for non-code files, providing deterministic, high-quality structural extraction without writing any ad-hoc scripts.
Step 1 — Prepare the input JSON
Create the input file with the batch data. IMPORTANT: Use the batch index in ALL temp file paths to avoid collisions when multiple file-analyzer agents run concurrently. First resolve the project's data directory once (the legacy .understand-anything/ when it already exists, otherwise the new .ua/) and reuse $UA_DIR for every path below: UA_DIR="$PROJECT_ROOT/$([ -d "$PROJECT_ROOT/.understand-anything" ] && echo .understand-anything || echo .ua)".
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.
- 6d ago First seen · 523 lines · 47 tokens per session scan A 913a605380ec
file-analyzer is an agent published in the GitHub repository Egonex-AI/Understand-Anything (81,621 stars, last pushed today), licensed MIT. It adds 47 tokens to every session and 8,249 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.
blue-team
当 researcher 工作区存在时，你优先审阅 evidenceledger.json 中支持性证据是否足够强，不能把检索摘要、模型回答或单一通稿当作高置信度证据。.
implementer
../../agents/implementer.md.
addyosmani.agent-skills.agents
MCP server that suggests repository-specific AI agent skills.
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.