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 samuelfaj · Codex Skill · ★ 662
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is distill safe to install? View the security audit →
distill Agent command outputs are one of the biggest sources of token waste. Logs, test results, stack traces… thousands of tokens sent to an LLM just to answer a simple question. 🔥 compresses command outputs into only what the LLM actually needs. Save up to 99% of tokens without losing the signal. How to use You can also point at OpenAI-compatible providers such as LM Studio, Jan, LocalAI, vLLM, SGLang, llama.cpp-compatible servers, MLX-based servers, and Docker Model Runner. Add in your global agent instructions file: md CRITICAL: Pipe every non-interactive shell command through unless raw output is explicitly required. CRITICAL: Your prompt to must be fully explicit. State exactly what you want to know and exactly what the output must contain. If you want only filenames, say If you want JSON, say Do not ask vague questions. Bad: Good: Examples: npm audit 2&1 | d
| Stars | 662 |
| Forks | 42 |
| Language | TypeScript |
| Category | Codex Skill |
| Quality Score | 68.9432159038816/100 |
| Open Issues | 7 |
| Last Updated | 2026-06-18 |
| Created | 2026-03-06 |
| Platforms | claude-code, cli, codex, node |
| Est. Tokens | ~15k |
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distill is Distill large CLI outputs into small answers for LLMs and save tokens!. It is categorized as a Codex Skill with 662 GitHub stars.
distill is primarily written in TypeScript. It covers topics such as claude-code, codex, llm.
You can find installation instructions and usage details in the distill GitHub repository at github.com/samuelfaj/distill. The project has 662 stars and 42 forks, indicating an active community.
The top alternatives to distill on Agent Skills Hub include Overture, google_workspace_mcp, cocoindex-code. 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: