Capn-hook: coding agent tool to avoid grepping the same mystery twice
Capn-hook is a tool for coding agents that records and indexes grep results so that when a developer encounters the same error or mystery again, the agent can recall the previous investigation. It uses LLMs to summarize and store grep outcomes, saving time by preventing repeated searches.
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Git-aware AI debugger that checks out old commits to fix production bugs
A tool that makes AI coding assistants (like Cursor or Claude Code) automatically checkout the git commit corresponding to a production error before debugging, preventing the agent from analyzing current code that has shifted. It solves the problem of AI agents hallucinating fixes because they look at the present state of files while the bug existed in a past commit.
Kote: Capture and reuse engineering context from AI chats and Git
Kote automatically captures engineering context from AI assistant chats and Git activity, storing it for later retrieval during pull requests or quick notes. It solves the problem of losing valuable debugging or architectural context by eliminating the need for manual documentation. Targeted at developers who use AI coding assistants.
Snitch: deterministic claim verifier for AI agent transcripts
Snitch is a tool that watches AI agent transcript files (Cursor, Claude Code, Codex, Pi, OpenCode) and verifies claims made in prose against actual evidence like tool calls, shell output, filesystem changes, git history, and session context. It uses deterministic regex patterns to extract claims and cross-references them, flagging inconsistencies. It helps developers trust their coding agents by catching when an agent's description doesn't match reality.
Logbook: git history miner that writes CLAUDE.md to prevent LLM rework
Logbook mines local git history to produce a LOGBOOK.md file documenting reverted approaches, skipped tests, silenced warnings, and hotspots. It wires this into CLAUDE.md so that Claude Code reads it in future sessions, reducing the chance of suggesting already-tried or already-finished work. For developers using Claude Code, it solves the problem of LLMs repeating past mistakes or redoing reverted changes.
ContextOps: open-source tool to audit and optimize LLM prompt context
ContextOps is an open-source tool that analyzes LLM prompts to detect token waste such as duplicated retrieval chunks, bloated system prompts, oversized conversation history, and repeated tool outputs. It helps developers reduce costs and improve model consistency by auditing what goes into the prompt before inference.
