KogCat

AI makes the answer smoother. KogCat makes the judgment sound.

English | 中文 · Website: https://www.kogcat.com

A local-first judgment calibration layer for Claude Code and Codex. Before you act on an AI answer, it surfaces the counterexamples, the boundaries, the blind spots — drawn from a knowledge base that lives on your machine. It won't replace your model. It won't slow you down. The call stays yours.

See the difference

"I read 30 minutes a day but nothing sticks. Should I take more detailed notes?"

Plain AI — Try the Cornell method, highlight key passages, add Anki for spaced repetition.

KogCat — More notes will likely make it worse. The bottleneck isn't capture. It's retrieval. Your knowledge base holds a claim you marked high-confidence: the "I get it" feeling while re-reading is the least reliable signal of real recall. So try this once — finish a section, close the book, write what you remember. Then compare it to what you thought you had.

Related MCP server: Kluster.ai Verify MCP

When it speaks up

Built for judgment, not lookup. KogCat speaks up for the calls that cost you when they're wrong — decisions, tradeoffs, comparisons, critiques, strategy. It stays quiet for the rest: lookups, definitions, code, summaries, translation. And even on a judgment call, it adds a note only when it sees something the model didn't.

• Automatic. Ask a judgment question in conversation. A note appears only when there's something worth saying — and your original answer is never touched.

• On demand. /kogcat:query <question> puts your knowledge base first: a conclusion, the conditions that change it, a next step.

Install

Claude Code

/plugin marketplace add KogCat/cc-kogcat
/plugin install kogcat

Codex

codex plugin marketplace add KogCat/cc-kogcat
codex plugin add kogcat@kogcat

After installing, fully quit and reopen Claude Code (or restart Codex). The first-run download only starts on the next fresh session — the install command alone won't begin it.

On first launch, KogCat quietly downloads its engine (~40 MB) and embedding model (~90 MB). Once. About a minute on a good connection. Keep working while it does — run /kogcat:status anytime to watch each piece come online.

Use it from any MCP client

KogCat's calibration engine runs as a local sidecar; the Claude Code / Codex plugin is just one client of it. Any MCP-capable tool — Cursor, Cline, Zed, VS Code, Claude Desktop — can use the same engine through a standalone stdio MCP server.

Add this to your client's MCP config (field names vary slightly by client; most use an mcpServers map):

{
  "mcpServers": {
    "kogcat": {
      "command": "uvx",
      "args": ["kogcat-mcp"]
    }
  }
}

Requires uv, on macOS (Apple Silicon) or Windows x86_64 (same engine, same platforms as below). On first run it downloads the engine + embedding model and registers a background sidecar — the same one-time setup as the plugin, shared by every client on the machine. It exposes the knowledge-base tools (search, node, edges, calibrate, calibrate_review, and the memory_* family) for your model to call.

What you give up vs. the plugin. The Claude Code / Codex plugin adds two host conveniences a generic MCP client has no hook for: calibration that fires automatically on judgment questions, and a memory index injected into context at session start. With a standalone server your model reaches the same knowledge base, but it's the model that decides to call those tools — or you ask it to "use KogCat" — rather than a hook firing them for you. You never type the tool names (search, calibrate_review, memory_*); they're the model's to call.

Privacy

• Your knowledge base stays on your machine. KogCat reads only the folder you point it at.

• Your conversation goes to the same Claude or Codex you already use. Nothing extra, nowhere else.

• Calibration happens in a local process. The results never leave.

• The engine comes from a public release channel, checked against a sha256 manifest before it ever runs.

No account. No subscription. No one else holding your knowledge.

Commands

Automatic calibration needs no command.

Requirements

• Claude Code or Codex

• macOS (Apple Silicon) or Windows x86_64 — Intel Mac and Linux not yet supported

• Python 3 on PATH — already there on macOS; on Windows, install it yourself (tick Add python.exe to PATH)

License

FSL-1.1-MIT — see LICENSE. Converts to MIT two years after each release.