codegrab: Interactive CLI tool for selecting and bundling code into a single, LLM-ready output file

by epilande · Agent Tool · ★ 96

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

How to install codegrab

brew install codegrab
go install github.com/epilande/codegrab/cmd/grab@latest

Taken from the project's README on GitHub.

Security audit verdict: SAFE · quality 66/100

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.

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.

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Quick Facts

Stars96
Forks10
LanguageGo
CategoryAgent Tool
LicenseMIT
Quality Score66/100
Open Issues4
Last Updated2026-01-02
Created2025-03-31
Platformscli, go
Est. Tokens~15k

About codegrab

CodeGrab ✋ An interactive CLI tool for selecting and bundling code into a single, LLM-ready output file. ❓ Why? When working with LLMs, sharing code context is essential for getting accurate responses. However, manually copying files or creating code snippets is tedious. CodeGrab streamlines this process by providing a clean TUI (Terminal UI), alongside a versatile CLI (Command-Line Interface).

codegrab alternative? Top 6 similar tools

Looking for a codegrab alternative? If you're comparing codegrab 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.

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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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