preprompt

# PrePrompt [](https://pypi.org/project/preprompt/) [](https://github.com/Preprompt-ai/preprompt/actions) [](LICENSE) > Your prompts, battle-tested. An MCP server that intercepts and optimizes > prompts in Claude Code and Cursor before they reach the LLM. ## What it does Most prompts sent to an LLM are underspecified — they're missing context, output format expectations, or technical constraints that the developer has in their head but didn't type. PrePrompt sits between your keyboard and the model, scores every prompt with a heuristic classifier, and rewrites the complex ones using Claude Haiku before the main model ever sees them. Simple prompts ("what is jwt") pass through untouched in under 1ms. No API cost, no latency, no noise. ## How it works ``` BEFORE write a function that handles youtube oauth token refresh and manages expired credentials with error handling AFTER Write a Python function for FastAPI that handles YouTube OAuth 2.0 token refresh. The function should: (1) detect expired credentials by checking the expiry timestamp, (2) use the refresh token to obtain a new access token via the YouTube API, (3) update and persist the new credentials (consider using a database or file storage), and (4) include comprehensive error handling for invalid refresh tokens, network failures, and API errors with appropriate logging and exception types. ``` When a prompt is intercepted you see this in your terminal: ``` ╔═ PrePrompt +58 ════════════════════════════════════════════╗ ║ The rewritten prompt specifies the technical ║ ║ implementation details, clarifies the complete workflow, ║ ║ and adds concrete error scenarios and storage ║ ║ considerations relevant to FastAPI applications. ║ ╠════════════════════════════════════════════════════════════╣ ║ ORIGINAL write a function that handles youtube oauth t... ║ ║ OPTIMIZED Write a Python function for FastAPI that handles ║ ║ YouTube OAuth 2.0 token refresh. The function ║ ║ should: (1) detect expired credentials by ║ ║ checking the expiry timestamp... ║ ╚════════════════════════════════════════════════════════════╝ ``` ## Install **pip (recommended):** ```bash pip install preprompt python scripts/setup_global_hook.py # Restart Claude Code or Cursor ``` **From source:** ```bash git clone https://github.com/Preprompt-ai/preprompt cd preprompt && ./scripts/install.sh ``` ## How the smart classifier works - **Pure heuristics, zero API calls** — runs on every prompt in under 1ms - Scores each prompt based on: ambiguity verbs, multi-requirement density, turn depth, and missing output format signals - **Only intercepts when score ≥ 38** — simple prompts always pass through - Negative signals: short prompts, lookup questions (`what is`, `what does`), already-structured prompts all score low and get skipped ``` SCORE INTERCEPT PROMPT 48 YES write me a middleware that validates tokens and handles refresh -35 no what is jwt -45 no thanks 65 YES refactor this to handle edge cases and manage errors properly -20 no add tests 70 YES implement a rate limiter that tracks requests, manages quotas... ``` ## Stack memory PrePrompt learns your stack as you work. After a few sessions it knows your language, framework, and style preferences and injects that context into every optimization automatically. ``` $ preprompt-memory PrePrompt — learned stack memory ────────────────────────────────────────────────────── language python confidence: 0.92 (seen 47x) framework fastapi confidence: 0.88 (seen 31x) database postgresql confidence: 0.74 (seen 12x) ``` ## CLI ```bash preprompt-install # one-command setup (API key + hooks) preprompt-history # recent prompt events across all sessions preprompt-stats # optimization stats (total, intercepted, avg score) preprompt-memory # learned stack context preprompt-test-classifier # test classifier on sample prompts preprompt-feedback # rate recent optimizations (builds accept/reject stats) preprompt-watch # live feed of interceptions in a second terminal preprompt-clip # optimize clipboard (works on macOS, Windows, Linux) preprompt-optimize "..." # optimize a prompt from the command line ``` ## Cost PrePrompt uses `claude-haiku-4-5` for optimization — the cheapest Claude model. Typical cost: ~$0.001 per intercepted prompt. At 20 complex prompts per day that's roughly **$0.60/month**. Simple prompts are never sent to the API. ## Architecture ``` Claude Code / Cursor │ ▼ UserPromptSubmit hook pre_prompt.py ├── classify_prompt() ← pure heuristic, <1ms, no API ├── [score < 38] ──────► pass through unchanged └── [score ≥ 38] ├── optimize() ← Haiku API call with stack context ├── write sidecar ← ~/.preprompt/pending/<uuid>.json └── return optimized prompt │ ▼ MCP server (on next tool call) flush_pending_hook_events() ← sidecars → SQLite save_prompt_event() update_memory_from_prompt() ``` The hook never holds a database connection — it writes a small JSON sidecar file so there's no lock conflict with the MCP server. ## MCP Tools | Tool | Parameters | Returns | |------|-----------|---------| | `optimize_prompt` | `user_prompt`, `conversation_history`, `turn_number` | `{optimized_prompt, was_intercepted, score, reason}` | | `get_prompt_history` | `limit` (default 20) | list of recent prompt events for this session | ## Environment variables | Variable | Default | Description | |----------|---------|-------------| | `ANTHROPIC_API_KEY` | — | Required for optimizer | | `MCP_TRANSPORT` | `stdio` | `stdio` or `sse` | Storage is always at `~/.preprompt/history.db` (created automatically). ## Requirements - Python 3.11+ - Anthropic API key (get one at [console.anthropic.com](https://console.anthropic.com)) - Claude Code and/or Cursor ## License MIT — see [LICENSE](LICENSE)