costrict: strict AI coder for enterprises, quality first, including AI Agent, AI CodeReview, AI Completion

by zgsm-ai · Agent Tool · ★ 4.4k

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

Security audit verdict: SAFE · quality 55/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

agentagentic-aiaiaicodingclaude-codeclinecodingcostrictgemini-clijetbrains

Quick Facts

Stars4,430
Forks202
LanguageTypeScript
CategoryAgent Tool
LicenseApache-2.0
Quality Score55/100
Open Issues10
Last Updated2026-09-17
Created2025-04-10
Platformsclaude-code, cli, gemini, node
Est. Tokens~28547k

About costrict

CoStrict Strict AI Coder for Enterprises Free • Open Source • Private Deployment 简体中文 | English CoStrict is a free, open-source AI-powered coding assistant designed for enterprise-grade development. With support for private deployment, it's the optimal choice for organizations requiring secure, standardized AI development workflows. ✨ Core Capabilities Standardized AI c

Compatible Skills

These tools work well together with costrict for enhanced workflows:

  • context-engineering-kit — semantic(0.25)+complementary+rare_topics+same_lang+similar_pop+shared_platform (58%)
  • ccmanager — semantic(0.21)+complementary+rare_topics+same_lang+similar_pop+shared_platform (57%)
  • oh-my-claudecode — semantic(0.17)+complementary+same_lang+similar_pop+shared_platform (56%)

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. 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:

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