Deterministic Zero-LLM Web Compiler, Scraper, Crawler, and Site Mapper for AI Agents.

Enables AI agents to see compiled LaTeX output: it builds the document, returns rendered page images, maps any point on a page back to its exact source line, and diffs what the last edit changed. It also audits an entire book for regressions, letting an agent diagnose and verify layout fixes without reading source alone.

Audit a website with the squirrelscan CLI and fix the findings in code. Runs SEO, performance, security, technical, content, accessibility, and 16 other rule categories (300+ rules), returns an LLM-optimized report, then drives an iterative fix loop, mapping issues to source files, applying fixes, a

Free, open-source diagram canvas. Draw it, write it as code (Mermaid, D2, Structurizr in), ask any AI model or a local one, let Claude Code edit it live over MCP, or map a GitHub repo with every arrow cited to a file and line. MIT, local-first, no account.

Your thinking deserves a map: an infinite canvas where LLM conversations grow into an editable thought graph. Wires are the context.

A multi-agent application-security review harness for Claude Code (and, later, other AI agents). One router skill dispatches to a full offensive-security pipeline that maps a codebase, hunts vulnerabilities with a per-class knowledge base, chains findings into escalations, verifies real impact, and reports to README/JSON/SARIF/doc/PDF.

Encrypted, fully offline agentic memory. One click install, GUI w/ memory map, all OS and agents. Superior memory creation, storage and retrieval.

Daily radar for new AI terms: names of new models, products and repos, caught on X, Hugging Face, GitHub and sitemaps before keyword tools list them.

The ripgrep of AI context: a zero-dependency C++23 CLI + MCP server for coding agents. Find what you want without reading the repo, then check you built what you meant — blast radius, tests-to-run, quality deltas. Signatures at 74.7% fewer bytes than bodies; every guess labelled, every loss published. Paddle out with a map.

SAIL V2 (Secure AI Lifecycle) as an agent skill — the full 91-risk catalog for AI/agent gap assessments, security roadmaps, and compliance checklists. Installs on Claude Code, Codex, ChatGPT, Antigravity, and any SKILL.md-compatible agent.

27 scope-enforced AI agents that run the full pentest kill-chain (recon → exploit → post-ex → DFIR → report) as a one-command Claude Code plugin. Backed by 754 MITRE-mapped skills.

This MCP server lets an agent run local binary inspection on ELF, Mach-O, PE, APK, IPA, ASAR, WebAssembly, and firmware targets — pulling symbols, sections, classified strings, disassembly, and mitigation posture, and flagging dangerous imports and CWE patterns. It exposes eight stdio JSON-RPC tools (with no external API keys) that feed a multi-agent swarm for attack-surface mapping and exploit-chain synthesis.

Enables agents to crawl, search, map, and extract structured evidence from websites, SPAs, PDFs, Office files, YouTube videos, GitHub repositories, ArXiv papers, shopping and travel listings, and CVE advisories via local or cloud-backed tools.

Enables agents to search the web with ranked content-bearing results, extract clean text from pages, crawl sites under configured limits, and map site structure via the Tavily API.

Enables agents to list indices, read mappings, run query DSL and ES|QL searches, and inspect shards on an Elasticsearch cluster through its HTTP API.

Multi-language repository map for agents and humans: MCP tools, dependency graphs, symbol indexes, impact review, and bounded source evidence.

Meet Mitsu, your chibi red-team operator. Connect your AI provider, set the scope, and she can take an authorized pentest from recon and attack-surface mapping through vulnerability discovery, > exploitation, and post-exploitation, armored with 165+ MCP-connected tools and live session intel. Guide every step or let her pwn autonomously. ( ◠‿◠)

Open-source alternative to Claude Cowork — a local-first AI agent desktop app · multi-model · self-evolving skills · privacy-first · multi-Harness roadmap · DeepSeek Harness integration in progress

Agent Dashboard: Visualization and analytics for Sessions and Quota Usage. Track, analyze, and optimize token usage across providers with heatmaps, cost tracking, token counting and quota resets..

Enables travelers and agents to route destination crowds in real time by dispersing visitors and reducing queue waits with queuing-theory and dynamic heatmap guidance.

Enables agents to optimize real-time attraction visitor dispersion and queueing wait times using queuing-theory models and dynamic heatmap routing.

Enables AI agents to drive a real Chromium browser by receiving compact, token-efficient page maps and only the changes after each action. It guards against hidden prompt injections, blocks payments, sends and deletions until the user approves, and lets agents learn site-specific notes over time.

Gives browser agents a reviewed map of 79 real sites — entry URLs, access verdicts, and role/accessible-name button maps for 104 screens — so an agent can find where things are and know whether it may act at all. Safety rules in code halt irreversible actions such as pay, order, submit and delete pending explicit human approval, while per-site memory recalls which routes worked last time.

Enables AI agents to connect to Maptek Vulcan 12 Desktop through MCP, allowing natural-language inspection of block models and triangulations, drillhole and CAD database operations, legend updates, 3D screenshots, and PyVulcan script execution.