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 WILLOSCAR · Codex Skill · ★ 509
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is research-units-pipeline-skills safe to install? View the security audit →
research-units-pipeline-skills Languages: English | 简体中文 This project uses semantic skills to turn research workflows into reusable pipelines. It is designed for the space between fragile prompting and overly rigid scripting. By organizing research tasks into staged pipelines with explicit artifacts, checkpoints, and guardrails, it makes complex work more reusable, inspectable, and iterative. The result is a workflow that can be resumed, audited, and continuously improved instead of being rebuilt from scratch each time. What This Repo Covers The codebase currently centers on seven workflows: [说明](readme/ev
| Stars | 509 |
| Forks | 39 |
| Language | Python |
| Category | Codex Skill |
| Quality Score | 65.2787369617853/100 |
| Open Issues | 1 |
| Last Updated | 2026-09-15 |
| Created | 2026-01-07 |
| Platforms | claude-code, codex, python |
| Est. Tokens | ~15k |
Looking for a research-units-pipeline-skills alternative? If you're comparing research-units-pipeline-skills with other codex skill tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.
Manage multiple Claude Code, OpenCode agents from either TUI or Web for easy access on mobile. Also supports M
Web, Desktop & Mobile client for Codex, Claude Code, OpenCode, Kimi, Augment Code, Qwen, fully end-to-end encr
AI writes code. This automates everything else · 24 plugins · 49 agents · 44 skills · for Claude Code, OpenCod
Safely run OpenCode, Codex, Claude Code with full permissions.
Ship 10x faster by running multiple Claude Code sub agents in parallel. GitHub-native orchestration for AI cod
A Dynamic Island-style command center for managing all your AI coding agents on macOS.
Explore other popular codex skill tools:
research-units-pipeline-skills is Research pipelines as semantic execution units: each skill declares inputs/outputs, acceptance criteria, and guardrails. Evidence-first methodology prevents hollow writing through structured intermedi. It is categorized as a Codex Skill with 509 GitHub stars.
research-units-pipeline-skills is primarily written in Python. It covers topics such as claude, claude-code, codex.
You can find installation instructions and usage details in the research-units-pipeline-skills GitHub repository at github.com/WILLOSCAR/research-units-pipeline-skills. The project has 509 stars and 39 forks, indicating an active community.
The top alternatives to research-units-pipeline-skills on Agent Skills Hub include agent-of-empires, happier, agentsys. 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: