cognifity-verdict-inspect 0.1.0a1

Verdict Inspect — analyze chat exports for drift, hedging, refusals, and quality. Drop in a conversations.json from ChatGPT/Claude/Cursor, get a report.

Verdict Inspect

PyPI distribution: cognifity-verdict-inspect. Command: verdict-inspect.

One-shot drift analysis on a chat export. Drop in a conversations.json from ChatGPT, a Claude.ai export, a Cursor .jsonl, or any OpenAI-format messages dump, and get back a rigorous drift / quality report.

Why this exists

Continuous LLM observability (the SDK + monitoring path) is the right answer for production agent traffic. But before a team commits to instrumenting their stack, they want to know: does Verdict actually find anything interesting in my data?

verdict-inspect answers that in 60 seconds against a file the user already has on their laptop. Local-first; nothing leaves the machine.

Usage

# Auto-detect format
verdict-inspect analyze ~/Downloads/conversations.json

# Force a format
verdict-inspect analyze --format chatgpt ~/Downloads/conversations.json

# Specify report output
verdict-inspect analyze --report ./drift_report.md ~/Downloads/chatlog.jsonl

# JSON output for piping
verdict-inspect analyze --json ~/Downloads/conversations.json | jq .

Supported formats (v0)

• ChatGPT data export — the conversations.json from Settings → Data Controls → Export
• Claude.ai data export — the conversations.json from Settings → Account → Export
• Generic OpenAI messages JSONL — one JSON object per line, each with messages: [{role, content}]
• Agent-session JSONL — type-tagged local agent session logs
• Auto-detect — looks at file structure and picks a parser

Planned (v1): Cursor .cursor/chats/, Gemini Takeout, LangChain message history files, Llama Index conversation logs.

What you get back

For any conversation longer than ~30 substantive turns:

1. Semantic drift — embedding-distribution shifts across temporal windows
2. Judge sample — PASS/FAIL by dimension on stride-sampled turns (requires ANTHROPIC_API_KEY)
3. Structural metrics — response length, hedge density, refusal rate, apology rate per window

Semantic drift runs key-free. By default it tries the optional sentence-transformers/all-MiniLM-L6-v2 embedder when installed, then falls back to the built-in HashingEmbedder if the dependency/model is unavailable. Install the heavier local embedder with pip install -e "packages/verdict_eval[semantic]".

For shorter conversations: just structural metrics (the others need ~30 turns/window for meaningful statistics).

Privacy

Everything runs on your machine. No data is uploaded anywhere. The judge layer makes API calls to Anthropic if you set ANTHROPIC_API_KEY, but you can run without it to get drift + structural metrics only.