by habit-hooks · Agent Tool · ★ 160
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
Automated quality checks that nudge AI coding agents toward better habits
| Stars | 160 |
| Forks | 17 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 58.9063918521144/100 |
| Open Issues | 45 |
| Last Updated | 2026-09-06 |
| Created | 2026-05-26 |
| Platforms | claude-code, python |
| Est. Tokens | ~13k |
These tools work well together with habit-hooks for enhanced workflows:
Looking for a habit-hooks alternative? If you're comparing habit-hooks with other agent tool tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.
Local codebase intelligence CLI + MCP server for AI coding agents: SQLite code graph, 28 languages, 287 comman
Agent Skill for code and test smell audits. Evidence-ranked findings from Refactoring, Clean Code, and the tes
Development workflows for Claude Code that keep broad exploration focused on the outcome you approved.
An AI-powered GitHub code review tool that uses LLMs to detect high-confidence, high-impact issues—such as sec
An LLM council that reviews your coding agent's every move
A Context Compiler for TypeScript. Deterministic, diffable architectural contracts and dependency graphs for A
Explore other popular agent tool tools:
habit-hooks is Automated quality checks that nudge AI coding agents toward better habits. It is categorized as a Agent Tool with 160 GitHub stars.
habit-hooks is primarily written in Python. It covers topics such as ai-agents, ai-coding-agents, ci.
You can find installation instructions and usage details in the habit-hooks GitHub repository at github.com/habit-hooks/habit-hooks. The project has 160 stars and 17 forks, indicating an active community.
habit-hooks is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to habit-hooks on Agent Skills Hub include roam-code, smell-check, claude-code-workflows. 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: