AIPOCH - Medical Research Agent Skills for OpenClaw
AIPOCH is building a new generation of AI Agent Skills designed to help researchers uncover the signals that move medicine forward. It integrates with AI agents like OpenClaw to provide structured medical research agent skills across medical research workflows.
200+ specialized medical research skills (growing), Structured around the four pillars of medical research:
- Evidence Insights
- Protocol Design
- Data Analysis
- Academic Writing
Each skill represents a well-defined medical research capability designed to be both human-understandable and AI-compatible.
Fully integrated with OpenClaw.
AIPOCH provides an open-source, local-first AI workbench with 550+ medical research agent skills for structured scientific workflows.
- for
- Medical researchers and data scientists building AI-assisted research pipelines.
- pricing
- open source
- license
- MIT
- latestSkill Packages (auto-updated)7 months ago
- Release-2026-3-200.1.37 months ago
- Rlease-2026-3-130.1.27 months ago
works withPythonROpenClawClaudeCodexOpen CodeHermes Agent
Key features
6 features of AIPOCH
- Execution, not suggestions — Runs Python, R, and other commands only after user approval.
- Traceable artifacts — Creates immutable versions of data and analysis with full provenance.
- Model-agnostic — Works with any LLM provider or custom gateway without lock-in.
- Medical research skill library — Offers 550+ reusable, audited skills across evidence, protocol, analysis, and writing.
- Local-first workspace — All project state and artifacts stay on the user's computer.
- Scientific data connectors — Built-in connectors for genomics, pharmacogenomics, and other research data sources.
Use cases
- Run a reproducible statistical analysis pipeline on clinical trial data.
- Automate literature synthesis and meta-analysis for a systematic review.
- Generate manuscript sections, figures, and citations from raw research outputs.
- Query pharmacogenomic annotations and dosing guidelines for drug-gene interactions.
AIPOCH vs alternatives
 AIPOCH |  Fine |  Pocketenv |  SuperAGI Cloud | .jpg1690446595296.png?width=64) Dify.AI | |
|---|---|---|---|---|---|
| Best for | Open-source AI workbench for medical research | General AI-agent software development | Universal agent sandbox | Managed autonomous AI agents | LLMOps and AI app development |
| Pricing | Open source | Free | Free | Free | Free |
| DevHunt upvotes | 1 | 75 | 73 | 43 | 30 |
| Launched | Oct 2027 | Jan 2023 | Apr 2026 | Jan 2023 | Jan 2023 |
- AIPOCH vs Fine: Focuses on building software with AI agents, not specialized medical research skills.
- AIPOCH vs Pocketenv: Provides a sandbox runtime for agents, but lacks the curated medical research skill library.
- AIPOCH vs SuperAGI Cloud: Cloud platform for autonomous agents; AIPOCH runs locally and is open-source.
- AIPOCH vs Dify.AI: Open-source LLMOps platform for building AI-native apps, not a scientific research workbench.
AIPOCH FAQ
Is AIPOCH a hosted service or can I run it locally?+
AIPOCH is a local-first, open-source workbench that runs on Windows, macOS, and Linux.
Do I need a specific LLM to use the workbench?+
No, it is model-agnostic and supports any provider, including Claude, Codex, and custom gateways.
How are the medical research skills validated?+
Each skill passes through MedSkillAudit, a domain-specific audit that checks scientific integrity and safety before release.
Can I add my own custom skills?+
Yes, you can add custom SKILL.md-compatible agents and extend the library with your own logic.
Is the output of analyses reproducible?+
All actions are traceable and produce immutable artifacts, ensuring full reproducibility of results.
Summarized by DevHunt from aipoch.com · Sep 28, 2026. Details may change; check the official site.
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