No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by ariel-frischer · Codex Skill · ★ 144
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is autospec safe to install? View the security audit →
▄▀█ █ █ ▀█▀ █▀█ █▀ █▀█ █▀▀ █▀▀ █▀█ █▄█ █ █▄█ ▄█ █▀▀ ██▄ █▄▄ Spec-Driven Development Automation Build features systematically with AI-powered specification workflows. Stop AI slop. Autospec brings structure to AI coding: spec → plan → tasks → implement - all in one command. Built with a multi-agent architecture and inspired by GitHub SpecKit, Autospec reimagines the specification workflow with YAML-first artifacts for programmatic access and validation. These principles ensure reliable, performant, and maintainable software that developers can trust for their critical development workflows. Supported agents: Claude Code, Codex CLI, and OpenCode. 📦 Installation bash curl -fsSL https://raw.githubuserconten
| Stars | 144 |
| Forks | 8 |
| Language | Go |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 64.9003182755789/100 |
| Last Updated | 2026-09-28 |
| Created | 2025-12-15 |
| Platforms | claude-code, cli, codex, go |
| Est. Tokens | ~19k |
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autospec is CLI for streamlined spec-driven development. It is categorized as a Codex Skill with 144 GitHub stars.
autospec is primarily written in Go. It covers topics such as agentic-workflow, ai, claude-code.
You can find installation instructions and usage details in the autospec GitHub repository at github.com/ariel-frischer/autospec. The project has 144 stars and 8 forks, indicating an active community.
autospec is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to autospec on Agent Skills Hub include hcom, Dossier, gob. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: