Skillget

codehamr

agent
Guvenlik Denetimi
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  • License — License: MIT
  • Description — Repository has a description
  • Active repo — Last push 0 days ago
  • Community trust — 14 GitHub stars
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  • rm -rf — Recursive force deletion command in install.sh
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Bu listing icin henuz AI raporu yok.

SUMMARY

A minimal, local-first coding agent for the terminal.

README.md

codehamr

A minimal coding agent for the terminal. Built for local LLMs, also
runs on OpenAI compatible endpoints.

Simplicity

A coding agent built for local LLMs has to make different decisions
than one built for frontier cloud models. Context is precious. Every
tool call has to earn its place. codehamr picks simplicity over
complexity, on purpose. The agent stays small so the context window
stays yours.

Three slash commands, one embedded system prompt, no router, no
sub-agents, no skill system, no MCP. That's it.

The agent runs in one deterministic loop, internally called GYSD
(Get Your Shit Done), where every turn ends with one of three tools:
verify (run a check), done (claim completion, must quote a passing
verify as proof), or ask (yield back to you). No hallucinated success.

Install

Linux, macOS:

curl -fsSL https://codehamr.com/install.sh | bash

Windows:

curl -fsSL https://codehamr.com/install.cmd -o install.cmd && install.cmd

Then run codehamr in your project.

Warning: AI systems like codehamr run model-generated shell commands with full filesystem access. Best run inside safe sandboxes like devcontainers or isolated VMs.

Config

On first run codehamr creates .codehamr/config.yaml for your
profiles. The system prompt is embedded in the binary, not on disk.
Project specific rules go straight into the chat: tell the agent
what matters, the conversation carries it.

# codehamr configuration
#
# Running codehamr in a devcontainer / WSL2 with Ollama on the host:
# swap 'http://localhost:11434' with 'http://host.docker.internal:11434' below.

active: local
models:
    local:
        llm: qwen3.6:27b
        url: http://localhost:11434
        key: ""
        context_size: 131072
    openai:
        llm: gpt-5.5
        url: https://api.openai.com
        key: sk-...
        context_size: 131072
    hamrpass:
        llm: hamrpass
        url: https://codehamr.com
        key: hp_...

/models lists profiles, /models <name> switches.

Hardware

Local LLMs finally caught up, and we love it. For the best experience we recommend the ~30B class like qwen3.6:27b on 32 GB+ unified RAM / VRAM, fully local and a real alternative to expensive cloud subscriptions.

Info for Ollama users: Ollama Desktop may silently cut context to 4k. Open settings and lift the Context length slider to 64k+, depending on your RAM / VRAM.

Compare

Tool Pick if
Frontier you want commercial heavyweight polish from Claude Code or Codex and accept the subscription cost and session timeouts
opencode you want a great, loaded Swiss army knife and embrace plugin complexity
pi-agent you want something lighter than opencode and accept configuring your own extensions, skills, and themes
codehamr you want the lightest take on simplicity over complexity and accept no plugins, skills, or sub-agents

HamrPass

We love local LLMs and always will. codehamr is built fully open
source with an MIT license and always will be. Connect to your
local Ollama models, or bring your own key with OpenRouter, OpenAI,
whatever you like.

HamrPass is an optional alternative. It's there if you want to
support the project, or if you'd rather not spend your weekend
benchmarking the latest open weight model and tuning every
parameter. We do that work and ship it as one endpoint with sensible
defaults, so you can just hamr code and get your shit done.

There's a waitlist at codehamr.com. HamrPass only gets built if real demand shows up there. Otherwise it doesn't. Local-first stays the focus.

License

MIT. Do whatever you want with it. Star it if it earned one.

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