OpenSSF Scorecard

SkillSpector is an open-source security scanner for AI agent skills, developed by NVIDIA. It scans Git repositories, URLs, ZIP archives, directories, and individual skill files before installation, looking for prompt injection, data exfiltration, privilege escalation, supply-chain issues, dangerous code, MCP permission problems, tool poisoning, and other malicious patterns. Its two-stage pipeline combines static analysis—including regex, Python AST, taint tracking, YARA signatures, and OSV.dev dependency lookups—with optional LLM-based semantic evaluation. It produces terminal, JSON, Markdown, or SARIF reports containing findings, severity labels, a 0–100 risk score, and installation recommendations. Baselines can suppress accepted findings, and the CLI can be used in CI or install gates through exit codes and machine-readable output. SkillSpector can also run as an MCP server callable by compatible agents. It does not execute scanned skills or sandbox them; optional LLM analysis sends eligible file contents to the configured provider, while dependency checks query OSV.dev. The project is distributed under the Apache License 2.0 and is part of NVIDIA's Verified Skills pipeline.

Codex Security is an OpenAI CLI and TypeScript SDK for finding, validating, and fixing security vulnerabilities in authorized code repositories. It scans directories and codebases, including repository changes, uses AI-assisted analysis to trace attack paths and apply custom threat models, validates findings, and can generate reviewable patches and exports in SARIF, JSON, and CSV formats. The CLI supports CI use with an API key and can stop builds at a selected severity threshold. The package also supports configurable deep scans, worker and subagent counts, time limits, containerized bulk scans, and a preview findings service. The service stores findings and embeddings in SQLite, provides paginated listings and a read-only dashboard, identifies potential duplicates through embedding similarity, and allows completed findings to be published and deduplicated through the CLI or SDK. It requires Node.js 22.13.0 or later and Python 3.10 or later, and can use OpenAI or other documented inference providers.

security-audit is a Cloudflare coding-agent skill that orchestrates multi-phase security audits of codebases. It uses reconnaissance to map architecture, trust boundaries, input surfaces, prior evidence, and deterministic coverage; assigns isolated agents to coverage-led hunting; sends each unique candidate to a fresh verifier; and records confirmed, needs_validation, and rejected results in machine-readable findings.json validated against a report schema. Independent agents verify final source claims, after which the skill derives target-neutral reports and coverage records. It can be installed with the Skills CLI and requires a tool-using coding agent with parallel sub-agent support, Node.js for its validators, and an OS-enforced sandbox for target-controlled execution. The repository is licensed under MIT.

SkillRadar is open-source discovery, security, ranking, and routing infrastructure for Agent Skills and Codex. It discovers public SKILL.md files, parses their contents, performs conservative static checks for capabilities such as shell commands, dynamic execution, secret access, networking, package installation, filesystem writes, and deployment tooling, then classifies and ranks candidates in a safety-gated registry. D and Blocked candidates are kept audit-only and excluded from automatic routing. Its Codex plugin provides task-to-Top-3 routing, skill search, provenance and safety inspection, and a read-only Skill Budget Doctor. Routing can use a bundled offline registry and returns relevance, SkillRadar score, security grade, provenance, reasons, and match details without executing candidate repositories or depending on live GitHub discovery. The repository includes radar data, a matching system, a router-quality benchmark, a local registry UI, and daily bot-refreshed generated data; it is released under the MIT license.

Open Code Review is an open-source, AI-powered command-line code-review tool developed from Alibaba Group’s internal review assistant. It reads Git diffs and sends changed files to a configurable LLM through an agent with tool-use capabilities; the agent can read complete files, search the repository, and inspect other changed files for context before producing structured comments tied to source lines. Its hybrid architecture combines deterministic engineering with dynamic agent decisions: deterministic stages select and bundle files, match rules to file characteristics, and apply independent comment-positioning and reflection modules, while scenario-specific prompts and tools guide context retrieval and review. The `ocr scan` command audits complete files or directories without requiring a meaningful diff, and the project provides multilingual rules for issues including null-pointer exceptions, thread safety, cross-site scripting, and SQL injection. It supports configurable model endpoints, including OpenAI- and Anthropic-compatible endpoints, and requires Git 2.41 or newer.