iFixAi

AI Manager is a local-first desktop application for installing, updating, configuring, and repairing AI coding command-line tools on a user's computer. It provides a single interface for checking installed tools, managing versions, connecting tools to reviewed or custom HTTPS AI-service endpoints, and viewing the effective endpoint and environment-variable overrides without editing shell profiles or PATH files. Its sidebar includes software management, API endpoints, skills, MCP connections, global prompts, sessions, and settings. It installs skills from trusted GitHub catalogues or local ZIP files, supports stdio, HTTP, and SSE MCP servers, and searches local coding-tool sessions that can be resumed in a terminal. Tool behavior is controlled by a capability registry, and the application uses local signals for checks rather than claiming a process is healthy without evidence. The application is built with Tauri 2 and React 18 and is released under the GNU AGPL-3.0-or-later. It has no analytics or telemetry SDK, stores product data locally, and offers signed update verification and platform-specific installers. Provider credentials are currently stored in plaintext in the local database, backups, and SQL configuration exports. AI Manager began as a fork of CC Switch, with inherited portions retaining their original MIT licensing.

AgentGuard is a web-based scanner for AI-agent Skills, including SKILL.md instructions. It combines deterministic pattern checks with AI-assisted analysis to identify capabilities and risks such as shell-command execution, file-system access, environment-variable and credential access, network requests, external downloads, suspicious URLs, obfuscated instructions, prompt-injection patterns, and destructive operations. It produces a trust report with security and quality scores, a risk rating, detected capabilities, findings, explanations, and recommendations. The scanner is free to use, requires no account, accepts up to 80,000 characters of Skill text, and is designed for analysis before installation.

Evo by Snyk is a security and governance platform for AI-native applications, agentic development, and AI systems. It provides continuous visibility, governance, testing, and real-time control across the AI software lifecycle. Its Agentic Development Security capability discovers models, agents, MCP servers, datasets, plugins, tools, and services used by agents across repositories; generates an AI bill of materials; applies policy and guardrails to agent actions; and validates AI-generated code before it reaches repositories, pipelines, or production. Its Continuous Offensive Security capability autonomously stress-tests applications and AI systems like an adversary, with agents coordinating reconnaissance, vulnerability hunting, exploit validation, remediation, and reporting, and an independent validation model intended to confirm exploitability before reporting findings. Testing is intended to run on code changes and pull requests.

Inspect is an AI evaluation framework associated with the UK AI Security Institute. It provides an alternative framework for evaluating AI systems.

ai-evaluation-framework is an open-source framework by Dreamers Inc. for benchmarking model-based solutions by accuracy, p95 latency, and estimated cost against user-defined ground-truth cases. Each task specifies fields and a scoring rule per field, including exact identifiers, normalized dates, numeric tolerances, containment, token overlap, and expected-missing values; cases can also provide alternate accepted renderings. It runs models from OpenAI, Anthropic, or an OpenAI-compatible gateway, compares results in a table, emits detailed JSON reports, and caches responses by model, task, and case so scoring rules can be changed without another model call. The repository uses document-field extraction as its worked example but accepts arbitrary text inputs such as OCR output, transcripts, and PDF text. A separate feedback server collects thumbs-up or thumbs-down responses with required comments, stores append-only JSONL data, and exposes statistics, exports, and candidate cases for human review; it does not train models. The repository includes tests using scripted mock models and is licensed under Apache-2.0.