nanoclaw-repl

Operate and extend NanoClaw v2 REPL for persistent workflow management.

2|Updated May 11, 2026
One-click install
npx skills add https://github.com/himanshu231204/AI_Research_agent --skill nanoclaw-repl-himanshu231204
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: nanoclaw-repl
Source: https://github.com/himanshu231204/AI_Research_agent/tree/main/.opencode/skills/nanoclaw-repl
Command: npx skills add https://github.com/himanshu231204/AI_Research_agent --skill nanoclaw-repl-himanshu231204

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill eliminates the manual overhead of managing persistent, session-aware REPL workflows for NanoClaw v2, removing the need to manually track session state, switch LLM models, or export outputs across separate tasks.

Core Features & Use Cases

  • Persistent Session Storage: Markdown-backed sessions that retain full state across REPL restarts for uninterrupted iterative work.
  • Workflow Control Commands: Switch LLM models on the fly with /model, load dynamic skills with /load, branch sessions for risk-free experimentation with /branch, search cross-session history with /search, compact old sessions to reduce clutter with /compact, and export outputs to markdown, JSON, or plain text with /export.
  • Use Case: Use this Skill to run iterative AI research tasks: branch a session to test a new prompt approach without losing your original work, compact outdated sessions to save storage space, and export final research outputs to share with your team.

Quick Start

Use the nanoclaw-repl skill to launch a new NanoClaw REPL session, switch to your preferred LLM model, and begin your first iterative task with automatic persistent session tracking.

Frequently Asked Questions about nanoclaw-repl

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I manage persistent session state in a REPL without external dependencies?▼

You can manage persistent session state in a REPL by using markdown-backed session storage, which retains full workflow history across restarts without requiring external runtime dependencies.

Can I switch LLM models on the fly during an iterative AI coding session?▼

You can switch LLM models on the fly during an iterative AI coding session by using workflow control commands like /model, allowing dynamic model switching without losing your current session state.

What is the best way to branch a session for risk-free prompt experimentation?▼

Branching a session for risk-free prompt experimentation is best done using the /branch command, which creates a split session state so you can test new approaches without overwriting your original work.

How do I search cross-session history and export outputs to markdown or JSON?▼

To search cross-session history and export outputs, use the /search command to query past interactions and the /export command to save final outputs as markdown, JSON, or plain text files.

Do I need external runtime dependencies to run a session-aware REPL for local commands?▼

You do not need external runtime dependencies to run a session-aware REPL, as the system is built with zero dependencies for portable usage and handles deterministic local commands natively.

How do I reduce clutter from outdated sessions in a persistent REPL workflow?▼

To reduce clutter from outdated sessions in a persistent REPL workflow, use the /compact command to clean up and consolidate old session data, saving storage space while keeping active workflows intact.