by aibuildai-inc · Agent Tool · ★ 71
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
git clone https://github.com/aibuildai-inc/aibuildai-llm-posttrain-agent.git && cd aibuildai-llm-posttrain-agentTaken from the project's README on GitHub. Check the security audit before running it.
Flagged: sudo usage. 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.
| Stars | 71 |
| Forks | 11 |
| Language | Python |
| Category | Agent Tool |
| License | Apache-2.0 |
| Quality Score | 74/100 |
| Last Updated | 2026-09-23 |
| Created | 2026-09-16 |
| Platforms | python |
| Est. Tokens | ~18k |
AIBuildAI LLM-Post-Train Agent 🏆 #1 on PostTrainBench The AIBuildAI LLM-Post-Train Agent is a recursive self-improving (RSI) agent for automated, customized post-training of LLMs. It autonomously designs post-training algorithms tailored to each enterprise's proprietary data and use cases, collects and curates its own training data, writes code, runs experiments, and iteratively improves model...
These tools work well together with aibuildai-llm-posttrain-agent for enhanced workflows:
Looking for a aibuildai-llm-posttrain-agent alternative? If you're comparing aibuildai-llm-posttrain-agent 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.
TradingAgents-MCPmode 是一个创新的多智能体交易分析系统，集成了 Model Context Protocol (MCP) 工具，实现了智能化的股票分析和交易决策流程。系统通过多个专业化智能体的协作，
An introduction to the world of AI Agents
Code search MCP for Claude Code. Make entire codebase the context for any coding agent. Embeddings are created
Your own Claude Code UI, sandbox, in-browser VS Code, terminal, multi-provider support (Anthropic, OpenAI, Git
Typed-decision models (noul / choice / score) trained by a self-improving loop of AI agents — checkpoints, the
GBase — Recursive Self-Improvement Agent Framework. Memory, evolution, quality gates, identity system, and 40+
Explore other popular agent tool tools:
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: