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Shared types and utilities for Pi extensions.","maintainers":[{"name":"alexleekt","email":"stratrix@gmail.com"}],"readme":"# @alexleekt/pi-shared\n\n[![npm](https://img.shields.io/npm/v/@alexleekt/pi-shared)](https://www.npmjs.com/package/@alexleekt/pi-shared)\n[![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](LICENSE)\n\n> The glue that holds the monorepo together.\n\nShared types and utilities for Pi extensions in this monorepo.\n\n## Exports\n\n| Path | Description |\n|------|-------------|\n| `@alexleekt/pi-shared/session` | `manageSessionSubscription()` — per-session subscription lifecycle helper |\n| `@alexleekt/pi-shared/types` | Shared Pi extension type definitions |\n| `@alexleekt/pi-shared/prompt-eval` | Generalized prompt evaluation framework |\n\n## `@alexleekt/pi-shared/prompt-eval`\n\nA reusable framework for evaluating LLM prompts against test cases. Extracted from `pi-heading`'s duplicated `prompt-eval-*.ts` scripts.\n\n### Core types\n\n```typescript\nexport interface EvalSuite {\n name: string;\n testCases: T[];\n promptBuilder: (testCase: T) => PromptMessage; // { system, user, maxTokens }\n extractMode: \"json\" | \"raw\";\n scorers: Scorer[];\n modelConfig?: ModelConfig;\n}\n\nexport type Scorer = (params: {\n text: string; // extracted result\n raw: string; // raw LLM output\n testCase: T;\n}) => ScoreResult;\n```\n\n### Key functions\n\n| Function | Purpose |\n|----------|---------|\n| `runSuite(suite, model)` | Evaluate all test cases, score outputs, print progress |\n| `generateReport(results, suite, model)` | Build markdown report string |\n| `callLLM(system, user, model, config?)` | Call local proxy with retry |\n| `extractResult(raw)` | Parse `{\"result\": \"...\"}` or fall back to raw |\n| `optimizeSuite(factory, promptPath, config)` | Iterative prompt optimization loop |\n\n### Built-in scorers\n\n```typescript\nscorers.wordCount(max)\nscorers.withinLimit(max)\nscorers.noMetaCommentary()\nscorers.noQuotes()\nscorers.noMarkdown()\nscorers.validJson()\nscorers.presentContinuous()\nscorers.pastTense()\nscorers.noTrailingPeriod()\nscorers.concise(threshold)\nscorers.alignsWithExpected(field, threshold)\nscorers.echoesGoal(goalField, threshold)\nscorers.specificConcrete()\nscorers.noVagueFiller()\n```\n\n### Iterative optimization\n\n```typescript\nimport { optimizeSuite } from \"@alexleekt/pi-shared/prompt-eval\";\n\nconst result = await optimizeSuite(\n (promptText) => mySuiteFactory(promptText), // factory builds EvalSuite from prompt text\n \"/path/to/prompt.md\", // file to mutate in-place\n {\n desirableOutcome: \"Concise past-tense summaries under 12 words, no meta-commentary\",\n targetPassRate: 85,\n maxIterations: 5,\n evalModel: \"firepass\",\n criticModel: \"firepass\",\n }\n);\n```\n\nThe optimization loop:\n1. Load prompt from file\n2. Run suite → check pass rate\n3. If below target, collect top N failing cases\n4. Call critic LLM with failures + desirable outcome description\n5. Write revised prompt back to file\n6. Repeat until target met or max iterations\n\n## License\n\nMIT\n","readmeFilename":"README.md"}