by chr15m · Agent Tool · ★ 445
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
uvx --from git+https://github.com/chr15m/runprompt runprompt hello.prompt
uv pip install git+https://github.com/chr15m/runpromptTaken from the project's README on GitHub.
Flagged: dynamic eval(). 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.
| Stars | 445 |
| Forks | 19 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 68/100 |
| Open Issues | 7 |
| Last Updated | 2026-09-07 |
| Created | 2025-11-27 |
| Platforms | cli, gemini, python |
| Est. Tokens | ~19k |
runprompt Run LLM .prompt files from your shell with a single-file Python script. A .prompt file contains the prompt and metadata (model, schema, config) in a single file. You can use it to run LLM prompts and get structured responses right in your shell. You can use it to automate AI workflows or build your own harness. Make prompts first-class artifacts.
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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: