Show HN: MindCheck – Analyze your AI coding logs for over-delegation

MindCheck Are you using AI as a tool — or becoming dependent on it? MindCheck analyses your AI conversation logs and measures your cognitive engagement — not how much you use AI, but how you use it. The problem AI tools are powerful. But there's a risk: the easier it gets to offload thinking, the less thinking you do. You might not notice it happening — until one day you can't solve problems without asking AI first. MindCheck gives you a mirror. What it measures Signal What it detects Hypothesis level Do you form a hypothesis before asking, or just dump the problem? (0–4 scale) Ownership Are you driving the conversation, or just reacting to AI output? Critical engagement Do you push back on AI answers, or accept everything? Self-reliance Do you attempt problems before asking for help? Metacognition Do you reflect on your own approach and blind spots? Delegation How often are you handing off thinking entirely? Score bands Score Meaning 70–100 Strong engagement — driving, hypothesising, thinking critically 50–69 Moderate — solid in places, room to push deeper before asking 30–49 Passive — leaning on AI for direction more than thinking first 0–29 Heavy delegation — most asks hand off the thinking entirely How it works Three-tier signal extraction — designed to be cheap and private: Tier 1: Structural rules (free, offline) — ratios, counts, patterns Tier 2: Semantic embeddings (free, offline) — meaning, not just keywords Tier 3: LLM classification (~$0.01/month) — ambiguous edge cases only Only your messages are analysed — AI responses are discarded. Results are cached locally so re-running is instant. Install pip install mindcheck Or from source: git clone https://github.com/PatrickSqx/MindCheck.git cd MindCheck pip install -e . First run: Tier 2 downloads a ~118 MB multilingual embedding model automatically. This only happens once. Usage # Score a single session file mindcheck score session.jsonl # Score with Tier 3 LLM refinement mindcheck score session.jsonl --tier 3 # Analyse all sessions in a folder mindcheck analyze ./sessions/ # Auto-discover and report on last 30 days mindcheck report --last 30d # Show all auto-discovered session directories on this machine mindcheck scan # Cache management mindcheck cache # show cache stats mindcheck cache --clear # clear all cached scores Tier 3 setup (optional) Tier 3 uses a cheap LLM to resolve messages that Tier 2 was uncertain about. It's optional — Tier 2 handles most sessions well on its own. # Anthropic (key auto-detected from sk-ant- prefix) mindcheck config --key sk-ant-xxxx # OpenAI (key auto-detected from sk- prefix) mindcheck config --key sk-xxxx # Gemini (key auto-detected from AIza prefix) mindcheck config --key AIzaxxxx # Local Ollama (free, no key needed) mindcheck config --provider ollama # Choose a specific model mindcheck config --model gemini-2.5-flash-lite # Show current config and available models mindcheck config --show Supported formats Tool Auto-discovered Claude Code ✅ ~/.claude/projects/ Cursor ✅ ~/.cursor/projects/ Codex CLI ✅ ~/.codex/sessions/ Gemini CLI ✅ ~/.gemini/tmp/ Agent/subagent sessions are automatically filtered — only human conversations are scored. Scoring methodology The composite score (0–100) is a weighted blend of semantic signals extracted from your messages: Signal Weight How it's measured Hypothesis quality 25% Each message is classified on a 0–4 scale: 0 = no attempt ("fix this"), 1 = symptom only ("it's broken"), 2 = locates the problem ("fails…

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