by EvoScientist
🧬 Extend EvoScientist with Installable Skill & Knowledge Packs
# Add to your Claude Code skills
git clone https://github.com/EvoScientist/EvoSkillsLast scanned: 5/25/2026
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}EvoSkills is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by EvoScientist. 🧬 Extend EvoScientist with Installable Skill & Knowledge Packs. It has 397 GitHub stars.
Yes. EvoSkills passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.
Clone the repository with "git clone https://github.com/EvoScientist/EvoSkills" and add it to your Claude Code skills directory (see the Installation section above).
EvoSkills is primarily written in Python. It is open-source under EvoScientist on GitHub, so you can review or fork the full source.
Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh EvoSkills against similar tools.
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The official skill repository for EvoScientist. Each skill is an installable knowledge pack that extends EvoScientist with domain-specific expertise.
[!IMPORTANT] These skills are purpose-built for EvoScientist — together they amplify each other, unlocking the full potential of both the agent and the skills. Under EvoScientist, skills evolve across research cycles through persistent memory (evo-memory).
Install all skills at once:
/install-skill EvoScientist/EvoSkills@skills
Or install a single skill:
/install-skill EvoScientist/EvoSkills@skills/paper-planning
Simply ask the agent in conversation:
"Install all skills from EvoScientist/EvoSkills@skills."
[!TIP] Not using EvoScientist? These skills are compatible with any coding agent. One command via skills.sh to install on Claude Code, OpenCode, Cursor, Codex, Gemini CLI, DeepAgents, and more:
npx skills add EvoScientist/EvoSkills
| Skill | Description |
|---|---|
research-ideation |
💡 Literature grounding, tournament ranking & proposal generation |
paper-planning |
📐 Research paper planning & outline generation |
experiment-pipeline |
🧪 Structured 4-stage experiment execution |
experiment-craft |
🔧 Experiment debugging, logging & iteration |
experiment-iterative-coder |
🔄 Iterative code refinement (plan → code → evaluate → refine) |
paper-writing |
✍️ End-to-end paper writing assistance |
paper-review |
🔍 Automated paper review & feedback |
paper-rebuttal |
💬 Rebuttal writing after peer review |
paper-figures |
📊 Publication-ready matplotlib figures from tabular data |
academic-slides |
🎤 Academic presentation & research talk creation |
evo-memory |
🧠 Persistent research memory & self-evolution |
paper-navigator |
📚 Academic paper discovery, evaluation & reading |
research-survey |
📝 Structured literature survey synthesis |
paper-graph |
🌳 Lineage map of a research field as Mermaid diagrams |
nano-banana |
🍌 AI-generated presentation slides & illustrations via Gemini |
evomath-tao |
🧮 Tao-style olympiad-grade proof workflow with calibrated abstention |
Paper Suite + Self-Evolution Suite: Each skill is self-contained — use them individually or combine freely. The self-evolution loop now runs through
research-ideation,experiment-pipeline, andevo-memory.
The mcp/ directory contains a curated collection of MCP servers that extend agents with external tools — web search, academic paper retrieval, documentation lookup, and more. Browse the full list or install directly:
/install-mcp # interactive browser
EvoSci mcp install arxiv # install by name
The diagram above shows the full EvoScientist pipeline. The Researcher Agent (top, blue) runs idea tree search and Elo tournament ranking to produce a research proposal. The Engineer Agent (bottom, green) executes the 4-stage experiment pipeline. The Evolution Manager Agent (right) manages three memory evolution mechanisms — IDE, IVE, and ESE — that feed learned knowledge back into Ideation Memory (M_I) and Experimentation Memory (M_E) for future cycles.
flowchart LR
A["<b>🔬 Research Phase</b><br/>💡 research-ideation"]
--> B["<b>⚙️ Experiment Phase</b><br/>📐 paper-planning<br/>🧪 experiment-pipeline<br/>🔧 experiment-craft<br/>🔄 experiment-iterative-coder"]
--> C["<b>📝 Writing Phase</b><br/>✍️ paper-writing<br/>🔍 paper-review<br/>💬 paper-rebuttal<br/>🎤 academic-slides"]
D[("🧠 evo-memory<br/>(IDE · IVE · ESE)")] <--> A
D <--> B
E["📚 paper-navigator<br/>(standalone)"] -.-> A
E -.-> B
F["🍌 nano-banana<br/>(standalone)"] -.-> C
G["🧮 evomath-tao<br/>(standalone)"] -.-> A
G -.-> B
style A fill:#7C3AED,stroke:#5B21B6,stroke-width:2px,color:#fff
style B fill:#D97706,stroke:#B45309,stroke-width:2px,color:#fff
style C fill:#16A34A,stroke:#15803D,stroke-width:2px,color:#fff
style D fill:#475569,stroke:#334155,stroke-width:2px,color:#fff
style E fill:#0369A1,stroke:#075985,stroke-width:2px,color:#fff
style F fill:#D97706,stroke:#B45309,stroke-width:2px,color:#fff
style G fill:#BE185D,stroke:#9D174D,stroke-width:2px,color:#fff
research-ideation — Literature Grounding, Tournament & ProposalThe starting point of the research pipeline. It now covers the full path from literature grounding to ranked ideas to a concrete proposal:
evo-memory first to reuse feasible directions and avoid known dead endspaper-navigator to collect and analyze papers before generating ideasresearch-survey — Literature Survey & SynthesisDedicated skill for turning a large paper collection into a structured survey report:
paper-planning — Research Paper Planning & Outline GenerationGuides pre-writing planning before a single word is drafted. Covers four key activities:
Includes counterintuitive tactics: write your rejection letter first, narrow claims before broadening, and plan fallback narratives.
experiment-pipeline — 4-Stage Experiment ExecutionA structured framework for executing research experiments with attempt budgets and gate conditions:
evo-memoryIntegrates with experiment-craft for failure diagnosis within stages and evo-memory for cross-cycle learning.
experiment-craft — Experiment Debugging & IterationA systematic approach to experiment debugging, logging, and iterative improvement:
paper-writing for draftingexperiment-iterative-coder — Iterative Code RefinementStructured plan → code → evaluate → refine cycles for higher code quality:
Integrates with experiment-craft for stuck diagnoses and evo-memory for loading prior strategies.
paper-writing — Section-by-Section Paper DraftingA proven 11-step workflow for writing academic papers with LaTeX templates: