Vibeharness
Description
Hackable, interactive LLM agent harness for the terminal — streaming REPL, tool-using agent loop, persistent PTY shells, sub-agents, planning, hooks, prompt caching, rate-limit-aware retry, mid-turn-checkpointed sessions. Anthropic + OpenAI.
Installation
This entry records only its repository, not the path inside it, so there is no
exact command to give. Open the source below and copy the folder into
~/.claude/skills/, or the file into ~/.claude/agents/.
README
🌀 vibeharness
**An interactive, tool-using LLM agent harness for the terminal** — the hackable middle ground between a 100-line ReAct toy and a closed-source product like Claude Code or Copilot CLI. ~2.4k lines of Python you can actually read end-to-end.
What you get when you launch `vibe`:
- A stateful REPL that holds a multi-turn conversation with the model
and renders it live: streaming token-by-token output, a thinking spinner,
rich diff/grep/bash panels, and a magenta
◆ vibe/ green❯ youspeaker layout you can scan at a glance. - A tool-using agent loop that the model drives autonomously inside each turn: read/write/edit files (with strict-match diffs), grep, glob, list, run bash in persistent PTY sessions (and background processes), spawn isolated sub-agents for self-contained subtasks, and update an externalized planning checklist as it works.
- A provider abstraction over Anthropic and OpenAI with token
streaming, prompt caching (cache_control breakpoints on the system
prompt + tool defs → ~90% discount on repeated turns), and a
rate-limit-aware retry layer that honors
Retry-After, falls back to exponential backoff with full jitter, and surfaces each retry in the UI. - A policy + extensibility surface: three-mode permission policy
(
auto/ask/deny) with per-tool approval memory, plus user-definedbefore_tool/after_toolhooks in~/.vibe/hooks.pythat can mutate args, deny calls, or post-process results (auto-format, gate commands, log to disk). - A session layer that JSON-serializes the conversation after every tool call (mid-turn checkpointing — Ctrl-C never loses progress) and re-renders the full transcript on resume so you pick up exactly where you left off.
- Context management that token-counts every turn and triggers an auto-summarization compactor when the window crosses 75% so long sessions don't blow out the model's context lim
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