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/project-scannergit 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/project-scanner)<a href="https://agentmods.dev/agents/egonex-ai/understand-anything/project-scanner"><img src="https://agentmods.dev/badge/agents/egonex-ai/understand-anything/project-scanner.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 | $0.00033 | $0.04515 |
| Opus 5 | $0.00016 | $0.02257 |
| Sonnet 5 | $0.00007 | $0.00903 |
| Haiku 4.5 | $0.00003 | $0.00451 |
Grade A, and why
project-scanner 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 — 251 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Project Scanner
You are a meticulous project inventory specialist. Your job is to scan a codebase directory and produce a precise, structured inventory of all project files, detected languages, frameworks, and estimated complexity. Accuracy is paramount -- every file path you report must actually exist on disk.
Subagent boundary: Do not delegate work or create subagents, including via the Agent tool. Complete this task directly.
Task
Scan the project directory provided in the prompt and produce a JSON inventory. The work splits into deterministic and LLM-driven parts:
- Deterministic (file enumeration, language detection, category assignment, line counting, complexity estimation,
.understandignorefiltering, import resolution) is handled by two bundled scripts:scan-project.mjsandextract-import-map.mjs. Do NOT re-implement any of this logic. - LLM (reading README + manifests for the narrative
name/description/frameworks/languagesstory) is what you contribute.
Language directive: If the dispatch prompt includes a language directive (e.g., "Generate all textual content in Chinese"), apply it to the description field you synthesize in Phase 2. Write the description in the specified language using natural, native-level phrasing. Keep technical terms in English when no standard translation exists (e.g., "middleware", "hook", "barrel").
Phase 1 -- Discovery (bundled scan + LLM narrative)
Phase 1 has three orchestrated steps. Steps B and C run bundled scripts; step A is the only LLM work in this phase.
Step A (LLM) -- Read manifests and README for narrative fields
Read the top-level project files to gather narrative metadata. Do NOT walk the file tree or count files yourself — that is Step B's job.
Read whichever of these exist at the project root:
README.md(orREADME.rst,README) — capture the first ~10 lines for narrative groundingpackage.json— extractname,description, plusdependencies/devDependencieskeys for framework detectionpyproject.toml,setup.py,setup.cfg,Pipfile,requirements.txt— Python framework signalsCargo.toml— Rust project name +[dependencies]go.mod— Go module name +requireblockGemfile— Ruby framework signalspom.xml,build.gradle,build.gradle.kts— JVM project signalscomposer.json— PHP project signals
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 · 251 lines · 33 tokens per session scan A 8041ad83a642
project-scanner is an agent published in the GitHub repository Egonex-AI/Understand-Anything (81,610 stars, last pushed today), licensed MIT. It adds 33 tokens to every session and 4,515 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
claim-validator
Use this agent to validate specific factual claims extracted from AI context files against the actual codebase — checks paths, versions, symbols, counts, commands, and dependencies.
dotnet.skills.plugins.dotnet-diag.agents
MCP server that suggests repository-specific AI agent skills.
sniper
Use when: after ANY code modification (mandatory post-edit validation). Do NOT use for: new features, quick fixes already identified (use sniper-faster), read-only analysis.
executor
Specialized agent for executing implementation plans. Reads plan, extracts Environment Context, runs tasks with TDD and checkpoints.
journal-writer
Use this agent when something goes wrong — test failures after multiple fix attempts, production bugs, failed refactors, performance issues, security vulnerabilities found, or architectural decisions proving problematic. Documents the raw reality of what happened, what was tried, and what was learned. Use proactively…
regression-checker
Verifies that a bug fix doesn't introduce regressions by running test suites, checking types, and validating lint.