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\n

PipelAIn

\n

Type-safe AI-powered execution pipelines

\n\n \n\n
\n
\n\n ![License](https://img.shields.io/badge/License-MIT-yellow.svg)\n ![npm version](https://badge.fury.io/js/@n1k1t%2Fpipelain.svg)\n ![Dynamic XML Badge](https://img.shields.io/badge/dynamic/xml?url=https%3A%2F%2Fgithub.com%2Fn1k1t%2Fpipelain%2Fblob%2Fmaster%2Fcoverage%2Fcobertura-coverage.xml%3Fraw%3Dtrue&query=round(%2Fcoverage%2F%40line-rate%20*%201000)%20div%201000&label=coverage)\n
\n\nPowerful utility to build and execute type-safe AI pipelines with structured outputs and tool integration.\n\n- [Features](#features)\n- [Installation](#installation)\n- [First Steps](#first-steps)\n- [Add Skills](#add-skills)\n- [Simple Example](#simple-example)\n- [Environment Variables](#environment-variables)\n- [Pipeline Step Utilities](#pipeline-step-utilities)\n - [factory](#factory)\n - [utils](#utils)\n - [context](#context)\n - [utils.content (ContentFactory)](#utilscontent-contentfactory)\n - [factory.tools (LlmToolsFactory)](#factorytools-llmtoolsfactory)\n - [factory.skills (LlmSkillsFactory)](#factoryskills-llmskillsfactory)\n- [Usage](#usage)\n - [Logging and Events](#logging-and-events)\n - [Structured Prompt Content](#structured-prompt-content)\n - [AI Step with LLM Configuration](#ai-step-with-llm-configuration)\n - [AI Step with Fallback](#ai-step-with-fallback)\n - [Debugging AI Steps](#debugging-ai-steps)\n - [Execution Reports](#execution-reports)\n - [Combining self and ai Steps](#combining-self-and-ai-steps)\n - [Parallel Execution with swarm](#parallel-execution-with-swarm)\n - [Iterative Execution with loop](#iterative-execution-with-loop)\n - [Multi-Provider Pipelines](#multi-provider-pipelines)\n - [Custom Model Routing](#custom-model-routing)\n - [MCP (Model Context Protocol) Integration](#mcp-model-context-protocol-integration)\n - [Using Skills](#using-skills)\n- [Extensions](#extensions)\n - [Custom LLM Tools](#custom-llm-tools)\n - [LLM Tool Hooks](#llm-tool-hooks)\n - [Custom LLM Skills](#custom-llm-skills)\n - [Registering Skills from Markdown](#registering-skills-from-markdown)\n- [License](#license)\n\n## Features\n\n- **Type-safe**: Built with TypeScript and Zod for full type safety of inputs, outputs, and intermediate states.\n- **Structured Outputs**: Enforce AI responses to match specific Zod schemas.\n- **Tool Integration**: Easily provide tools (web search, file system, etc.) to the AI.\n- **Step-by-step Execution**: Define complex workflows as a series of steps.\n- **Context-aware**: Shared context and state across all pipeline steps.\n\n## Installation\n\n```bash\nnpm install @n1k1t/pipelain zod\n```\n\n## First Steps\n\nTo use the library, you need to provide an API key for the LLM provider. Create a `.env` file in your project root and add your key:\n\n```env\nPIPELAIN_API_KEY=your_api_key_here\n```\n\n## Add Skills\n\nIf you are using the [skills](https://www.npmjs.com/package/skills) package, you can add `n1k1t/pipelain` to your project using the following command:\n\n```bash\nnpx skills add n1k1t/pipelain\n```\n\n## Simple Example\n\nA basic pipeline that translates text and returns a structured response:\n\n```ts\nimport z from 'zod/v3';\nimport { PipelineCompiler } from '@n1k1t/pipelain';\n\nconst translate = PipelineCompiler\n .build('Translator')\n .input(z.string())\n .step('translated', ({ factory, context }) => factory\n .ai('Translating')\n .schema(z.object({\n ru: z.string().describe('Russian translation'),\n es: z.string().describe('Spanish translation'),\n en: z.string().describe('English translation'),\n }))\n .prompt([\n `Translate this text to Russian, Spanish and English: ${context.input}`,\n ])\n );\n\n(async () => {\n const compiled = await translate.compile();\n const result = await compiled.run('Hello');\n\n console.log(result.translated.es); // Hola\n})();\n```\n\n## Environment Variables\n\nThe following environment variables are supported:\n\n| Variable | Description | Default |\n| --- | --- | --- |\n| `PIPELAIN_API_KEY` | API key for the LLM provider. | - |\n| `PIPELAIN_API_URL` | Custom API URL for the LLM provider. | - |\n| `PIPELAIN_SKILLS_PATHS` | Directories paths where LLM skills are stored (separated by `;`). | `./.agents/skills;~/.agents/skills` |\n| `PIPELAIN_MODEL` | Default LLM model to use. | `gemini-flash-latest` |\n| `PIPELAIN_PROVIDER` | LLM provider name (e.g., `google`, `openai`). | - |\n| `PIPELAIN_FLAGS_REPORT` | Save an HTML report of the pipeline execution. | `false` |\n| `PIPELAIN_FLAGS_DEBUG` | Enable debug mode for all AI steps. | `false` |\n| `EXA_API_KEY` | API key for Exa web search tools. | - |\n\n## Pipeline Step Utilities\n\nEach pipeline step receives a set of utilities to interact with the environment, AI, and context.\n\n### `factory`\nUsed to create different types of steps:\n- `ai(description?)`: Creates a step that executes on the AI side.\n- `self(description?)`: Creates a step that executes on the local machine.\n- `swarm(description?)`: Executes multiple steps or pipelines in parallel.\n- `loop(description?)`: Creates a loop of steps.\n- `tools`: Access to LLM tools factory (e.g., `factory.tools.web()`).\n- `skills`: Access to LLM skills factory (e.g., `factory.skills.all()`).\n\n### `utils`\nGeneral-purpose utilities:\n- `content`: `ContentFactory` instance to create structured prompt content (articles, tasks, rules, attachments).\n- `bash`: Execute shell commands on the local machine.\n- `log`: Emit `INFO` log events for the current pipeline session (shown in stdout and HTML reports).\n\n### `context`\nShared state and configuration:\n- `input`: The input data provided to the pipeline.\n- `state`: Shared state across all steps.\n- `llm`: Current LLM provider configuration.\n- `project`: Information about the current project.\n\n### `utils.content` (ContentFactory)\nUsed to create structured prompt content. These methods are available via `utils.content` in pipeline steps.\n\n| Method | Description | Example |\n| --- | --- | --- |\n| `article(title, content)` | Creates a `## Title` section with nested markdown content. | `utils.content.article('Context', [{ p: 'Some text' }])` |\n| `rules(list)` | Creates a bulleted list of rules/constraints. | `utils.content.rules(['Use JSON', 'Be concise'])` |\n| `tasks(list)` | Creates a numbered list of tasks for the AI. | `utils.content.tasks(['Analyze data', 'Write summary'])` |\n| `sources(list)` | Creates a list of source links. | `utils.content.sources(['https://google.com'])` |\n| `attachment(title, payload)` | Attaches data (object, string) as a file-like block. | `utils.content.attachment('Data', { content: { id: 1 } })` |\n| `file(title, path)` | Reads a local file and attaches it as context. | `await utils.content.file('Config', 'package.json')` |\n| `glob(title, pattern)` | Reads multiple files by pattern and attaches them. | `await utils.content.glob('Source', 'src/**/*.ts')` |\n| `plain(text)` | Adds raw markdown text. | `utils.content.plain('### Subtitle\\nText')` |\n| `user(content)` | Forces content to be placed into the `user` message. | `utils.content.user('Say hi')` |\n| `system(content)` | Forces content to be placed into the `system` message. | `utils.content.system('You are a helpful assistant')` |\n\nEach content kind has a default location it is rendered into (`tasks` and plain strings default to `user`, everything else defaults to `system`). Wrap any content in `utils.content.user()` / `utils.content.system()` to override that placement explicitly.\n\n### `factory.tools` (LlmToolsFactory)\nUsed to provide tools to the AI. These methods are available via `factory.tools` in pipeline steps.\n\n| Method | Description | Included Tools |\n| --- | --- | --- |\n| `all()` | Includes all available tools. | -|\n| `web()` | Tools for web interaction. | `search`, `fetch` |\n| `files('read')` | Read-only file system tools. | `read`, `grep`, `glob`, `ls` |\n| `files('read-write')` | Read and write file system tools. | `read`, `grep`, `glob`, `ls`, `mkdir`, `write`, `edit`, `rm` |\n| `commands(['npm', 'npx'])` | Package manager tools. | `npm`, `npx` |\n| `custom(tools)` | Includes custom tool implementations. | - |\n\n### `factory.skills` (LlmSkillsFactory)\nUsed to provide domain-specific instructions and workflows to the AI. These methods are available via `factory.skills` in pipeline steps.\n\n| Method | Description |\n| --- | --- |\n| `all()` | Includes all registered skills. |\n| `match(pattern)` | Includes skills matching a minimatch pattern. |\n| `register(raw)` | Registers a new skill from raw markdown content (with frontmatter). |\n| `custom(skills)` | Includes custom skill objects. |\n| `provide()` | Returns the list of included skills for LLM configuration. |\n\n---\n\n## Usage\n\n### Logging and Events\n\nYou can use `stdout.console` to output pipeline progress and logs to `stdout`:\n\n```ts\nimport { stdout } from '@n1k1t/pipelain';\n\n(async () => {\n const compiled = await translate.compile({ stdout: stdout.console });\n await compiled.run('Hello');\n})();\n```\n\nTo use a custom logger or override specific event handlers, use `PipelineStdout`:\n\n```ts\nimport { PipelineStdout } from '@n1k1t/pipelain';\n\nconst customStdout = PipelineStdout\n .build(console) // Pass any logger with .info, .warn and .debug methods\n .override('log', ({ level, message }) => {\n console.log(`[${level}]`, ...message);\n })\n .override('step:run', ({ step }) => {\n if (step.meta.is('DONE')) {\n console.log(`Step ${step.title} finished in ${step.meta.spent}ms`);\n }\n });\n\n(async () => {\n const compiled = await translate.compile({ stdout: customStdout });\n await compiled.run('Hello');\n})();\n```\n\nEach pipeline, step and AI action has a `meta` object with its execution state:\n\n| Property / Method | Description |\n| --- | --- |\n| `state` | `'INIT' \\| 'PENDING' \\| 'DONE' \\| 'ERROR'` |\n| `spent` | Time spent since creation (ms). |\n| `timestamp` | Creation timestamp. |\n| `is(state \\| state[])` | Checks the current state. |\n\nThe `log` event has a `level` field: `'DEBUG' | 'INFO' | 'WARN'`. `utils.log(...)` emits `INFO`.\n\n### Structured Prompt Content\n\nYou can combine multiple content types to create a rich, structured prompt for the AI:\n\n```ts\n.step('complex_task', async ({ factory, utils, context }) => factory\n .ai('Complex Processing')\n .prompt([\n // 1. Global rules for the AI\n utils.content.rules([\n 'Use professional tone',\n 'Output must be valid JSON'\n ]),\n\n // 2. Structured article with nested content\n utils.content.article('Project Overview', [\n { p: 'This project is a type-safe pipeline builder.' },\n { h2: 'Key Goals' },\n { ul: ['Safety', 'Performance', 'Flexibility'] }\n ]),\n\n // 3. Attachments (data or files)\n utils.content.attachment('User Data', {\n content: { id: 1, name: 'John Doe' }\n }),\n\n // 4. Local files and glob patterns\n await utils.content.file('Package Info', 'package.json'),\n await utils.content.glob('Source Code', 'src/utils/*.ts'),\n\n // 5. External sources\n utils.content.sources(['https://github.com/n1k1t/pipelain']),\n\n // 6. Raw markdown\n utils.content.plain('> Note: This is a critical task.'),\n\n // 7. Specific tasks for the AI to complete\n utils.content.tasks([\n 'Review the `Project Overview` article',\n 'Analyze `User Data` and `Source Code`',\n 'Generate a summary based on the rules'\n ])\n ])\n)\n```\n\n### AI Step with LLM Configuration\n\nYou can customize the LLM behavior for a specific step, such as changing the temperature or providing tools. You can also restrict file tools to specific paths using `allowed` patterns:\n\n```ts\n.step('research', ({ factory, context, utils }) => factory\n .ai('Researching')\n .llm(({ context }) => context.llm.assign({\n temperature: 0.7,\n limit: 10, // Max tool execution attempts\n tools: factory.tools\n .web()\n .files('read-write', {\n allowed: {\n // Restrict 'write' tool to specific directory\n write: ['src/generated/*.ts'],\n // Restrict 'edit' tool to all TypeScript files\n edit: ['src/**/*.ts'],\n // Restrict 'rm' tool to temporary files\n rm: ['temp/**/*']\n }\n })\n .provide(),\n }))\n .schema(z.object({\n summary: z.string(),\n links: z.array(z.string())\n }))\n .prompt([\n utils.content.tasks([\n `Find information about: ${context.input}`,\n 'Summarize findings and provide source links'\n ])\n ])\n)\n```\n\n### AI Step with Fallback\n\nYou can configure fallback providers for an AI step to automatically switch to alternative models or providers if the primary one fails. This is useful for increasing reliability or using a more powerful model only as a backup:\n\n```ts\nimport { llm } from '@n1k1t/pipelain';\n\n.step('translation', ({ factory }) => factory\n .ai('Translating with fallback')\n .llm(({ context }) => context.llm.assign({\n temperature: 0.3,\n fallback: {\n // 'continue' - resumes the session with the new provider keeping existing tool calls and reasoning results.\n // 'restart' - restarts the step execution from scratch using the fallback provider.\n strategy: 'continue',\n providers: [\n // Backup 1: If primary provider fails, try this model\n llm.providers.LlmGoogleProvider.build('gemini-1.5-pro', {\n connection: { key: process.env.GOOGLE_API_KEY! }\n }),\n // Backup 2: If the first backup also fails, try this model\n llm.providers.LlmOpenAiProvider.build('gpt-4o', {\n connection: { key: process.env.OPENAI_API_KEY! }\n })\n ]\n }\n }))\n .schema(z.object({ text: z.string() }))\n .prompt(['Translate this text into Spanish...'])\n)\n```\n\n### Debugging AI Steps\n\nYou can use the `.debug()` method to inspect the prompts sent to the AI. When debug mode is enabled, the step will mock the AI response and save the generated prompt into a markdown file in the `.pipelain` directory:\n\n```ts\n.step('debug_example', ({ factory }) => factory\n .ai('Debugging Step')\n .debug() // Enables debug mode for this step\n .schema(z.object({ result: z.string() }))\n .prompt(['Analyze this complex data...'])\n)\n```\n\nThe prompt will be saved to: `.pipelain/debug/YYYY-MM-DD--HH-mm-ss--/..md`.\n\n### Execution Reports\n\nUse `.report()` on the pipeline compiler (or set `PIPELAIN_FLAGS_REPORT=true`) to save an HTML report of the AI steps execution:\n\n```ts\nconst pipeline = PipelineCompiler\n .build('Translate')\n .report() // Enables HTML report\n .input(z.string())\n .step('translated', ({ factory, context }) => factory\n .ai('Translating')\n .prompt([`Translate \"${context.input}\" into Spanish`])\n );\n```\n\nThe report is saved into `.pipelain/reports/YYYY-MM-DD--HH-mm-ss--.html` and includes:\n\n- AI steps with messages, actions (tools, reasoning, fallbacks), output and timing;\n- `INFO` and `WARN` logs emitted via `utils.log`;\n- token usage per provider and model (prompt, cached, completion) and total session time.\n\n### Combining `self` and `ai` Steps\n\nYou can use `self` steps to perform local computations, logging, or data transformation between AI steps:\n\n```ts\nconst pipeline = PipelineCompiler\n .build('Data Processor')\n .input(z.string())\n .step('extracted', ({ factory, context }) => factory\n .ai('Extracting data')\n .schema(z.object({\n items: z.array(z.string())\n }))\n .prompt([`Extract items from: ${context.input}`])\n )\n // Local step to process data or log progress\n .step(({ context, utils }) => {\n utils.log(`Extracted ${context.state.extracted.items.length} items`);\n\n // You can also modify the state or context here\n context.merge({\n state: {\n processedCount: context.state.extracted.items.length\n }\n });\n })\n .step('summary', ({ factory, context }) => factory\n .ai('Summarizing')\n .schema(z.object({ text: z.string() }))\n .prompt([\n `Summarize these ${context.state.processedCount} items:`,\n context.state.extracted.items.join(', ')\n ])\n );\n```\n\n### Parallel Execution with `swarm`\n\nUse `swarm` to execute multiple AI tasks in parallel. You can define subtasks as a `list` (array) or as a `map` (keyed object), optionally limiting how many run concurrently with `.limit()`.\n\n#### `list`\n\n```ts\n.step('analysis', ({ factory }) => factory\n .swarm('Parallel Analysis')\n .list([\n factory\n .ai('Sentiment Analysis')\n .schema(z.object({ score: z.number() }))\n .prompt(({ context }) => [`Analyze sentiment of: ${context.input}`]),\n\n factory\n .ai('Keyword Extraction')\n .schema(z.object({ tags: z.array(z.string()) }))\n .prompt(({ context }) => [`Extract keywords from: ${context.input}`]),\n ])\n .limit(2) // Optional: limit parallel executions\n)\n// Results will be available in context.state.analysis as an array of PromiseSettledResult\n```\n\n> `subtasks(...)` is a deprecated alias of `list(...)` kept for backward compatibility — prefer `list`.\n\n#### `map`\n\nUse `map` when you need to address each subtask result by name instead of by array index:\n\n```ts\n.step('analysis', ({ factory }) => factory\n .swarm('Parallel Analysis')\n .map({\n sentiment: factory\n .ai('Sentiment Analysis')\n .schema(z.object({ score: z.number() }))\n .prompt(({ context }) => [`Analyze sentiment of: ${context.input}`]),\n\n keywords: factory\n .ai('Keyword Extraction')\n .schema(z.object({ tags: z.array(z.string()) }))\n .prompt(({ context }) => [`Extract keywords from: ${context.input}`]),\n })\n .limit(2) // Optional: limit parallel executions\n)\n// context.state.analysis.sentiment -> PromiseSettledResult<{ score: number }>\n// context.state.analysis.keywords -> PromiseSettledResult<{ tags: string[] }>\n```\n\n### Iterative Execution with `loop`\n\nUse `loop` for tasks that require multiple iterations or validation, such as self-correction:\n\n```ts\n.step('refined_answer', ({ factory }) => factory\n .loop('Self-Correction Loop')\n .limit(3) // Max 3 attempts\n .action(({ factory, context, verdict }) => factory\n .ai('Answering')\n .schema(z.object({\n answer: z.string(),\n isCorrect: z.boolean().describe('Self-check result')\n }))\n .prompt([\n `Question: ${context.input}`,\n // If this is a retry, include previous feedback\n verdict.status === 'pending' ? verdict.content : ''\n ])\n )\n .condition(({ result }) => {\n if (result.isCorrect) {\n return { status: 'fulfilled' };\n }\n\n return {\n status: 'pending',\n content: 'Your previous answer was incorrect. Please try again and be more specific.'\n };\n })\n)\n// Result will contain { status: 'fulfilled' | 'voided', value: { answer, isCorrect } }\n```\n\n### Multi-Provider Pipelines\n\nYou can use different LLM providers for different steps in the same pipeline:\n\n```ts\nimport { PipelineCompiler, llm } from '@n1k1t/pipelain';\n\nconst pipeline = PipelineCompiler\n .build('Multi-Provider')\n .input(z.string())\n .step('initial_analysis', ({ factory }) => factory\n .ai('Google Analysis')\n .llm(() => llm.providers.LlmGoogleProvider.build('gemini-1.5-flash', {\n connection: { key: process.env.GOOGLE_API_KEY! }\n }))\n .prompt(({ context }) => [`Analyze this: ${context.input}`])\n )\n .step('final_summary', ({ factory }) => factory\n .ai('Anthropic Summary')\n .llm(() => llm.providers.LlmAnthropicProvider.build('claude-3-5-sonnet-20240620', {\n connection: { key: process.env.ANTHROPIC_API_KEY! }\n }))\n .prompt(({ context }) => [\n `Summarize the analysis: ${context.state.initial_analysis}`\n ])\n );\n```\n\n### Custom Model Routing\n\n`LlmRouter` picks a provider by model name. Use `register` to route models matched by a [minimatch](https://github.com/isaacs/minimatch) pattern to your own provider:\n\n```ts\nimport { LlmRouter, llm } from '@n1k1t/pipelain';\n\nconst router = LlmRouter\n .build()\n .register('corp-*', (model) => llm.providers.LlmProxyProvider.build(model, {\n name: 'corp', // Shown in usage stats and reports instead of \"proxy\"\n connection: { key: process.env.CORP_API_KEY!, url: 'https://llm.corp.local/v1' },\n }));\n\npipeline.step('analysis', ({ factory }) => factory\n .ai('Corp Analysis')\n .llm(() => router.provide('corp-gpt-4o'))\n .prompt(['...'])\n);\n```\n\nRegistrations are checked in order and the first match wins. Unmatched models fall back to the default routing.\n\n### MCP (Model Context Protocol) Integration\n\nYou can integrate MCP servers into your pipeline steps. This allows the AI to use tools provided by external MCP servers. You can also filter which tools are enabled:\n\n```ts\nimport { LlmMcp } from '@n1k1t/pipelain';\n\npipeline.step('mcp_research', ({ factory, context }) => factory\n .ai('Researching with MCP')\n .llm(({ context }) => context.llm.assign({\n mcp: [\n LlmMcp.build({\n transport: {\n type: 'stdio',\n command: 'npx',\n args: ['-y', '@modelcontextprotocol/server-gdrive'],\n },\n tools: {\n // Enable only specific tools using names or minimatch patterns\n enabled: ['list-files', 'read-file'],\n // Or disable specific tools\n // disabled: ['delete-file'],\n\n // Add prefix to tool names (e.g. \"gdrive_read-file\")\n prefix: 'gdrive_',\n },\n }),\n ],\n }))\n .prompt(['List my recent files in Google Drive and summarize them.'])\n);\n```\n\nUse `tools.prefix` to avoid name conflicts between MCP servers or with built-in tools. The `enabled` and `disabled` patterns match tool names without the prefix. Hooks by name (`LlmHook.build('gdrive_read-file')`) match tool names with the prefix.\n\n### Using Skills\n\nSkills allow you to inject reusable domain-specific instructions or workflows into your AI steps. You can manage them via `factory.skills`:\n\n```ts\n.step('specialized_task', ({ factory, context }) => factory\n .ai('Executing with skills')\n .llm(({ context }) => context.llm.assign({\n // Include all registered skills from PIPELAIN_SKILLS_PATHS or ~/.agents/skills\n skills: factory.skills.all().provide(),\n\n // OR: Include only specific skills by pattern\n // skills: factory.skills.match('coding-*').provide(),\n }))\n .prompt([\n `Perform this task using available skills: ${context.input}`\n ])\n)\n```\n\n## Extensions\n\n### Custom LLM Tools\n\nYou can create custom tools for the AI using `LlmToolCompiler`. This allows the AI to interact with your own services or perform specialized tasks:\n\n```ts\nimport z from 'zod/v3';\nimport { LlmToolCompiler, LlmToolExecutionError } from '@n1k1t/pipelain';\n\n// 1. Define the tool\nconst weatherTool = LlmToolCompiler\n .build('Get current weather for a location')\n .input(z.object({\n city: z.string().describe('The city name')\n }))\n .output(z.object({\n temperature: z.number(),\n condition: z.string()\n }))\n .execute(() => async ({ city }) => {\n if (!city.trim()) {\n // Errors are reported back to the AI as \"Execution failed: \"\n throw LlmToolExecutionError.build('City name is empty');\n }\n\n // Your implementation here\n return { temperature: 22, condition: 'Sunny' };\n });\n\n// 2. Use it in a step\npipeline.step('weather_report', ({ factory, context }) => factory\n .ai('Checking weather')\n .llm(({ context }) => context.llm.assign({\n tools: factory.tools\n .web() // Include standard web tools\n .custom({ weather: weatherTool }) // Add your custom tool\n .provide()\n }))\n .prompt([`Check weather in ${context.input}`])\n);\n```\n\n### LLM Tool Hooks\n\nUse `LlmHook` to intercept tool calls inside an `ai` step. With a hook you can validate or modify the input, override the output, skip the original execution or handle errors. Provide hooks to the step with the `.hooks([...])` method.\n\nA hook matches a tool by one of these targets:\n\n- `LlmHook.build('name')` matches a tool by its name (built-in, custom or MCP tool).\n- `LlmHook.build(compiler)` matches a tool compiled from the same `LlmToolCompiler` instance or its clones (for example, built-in tools with options).\n- `LlmHook.build(tool)` matches the same `Tool` instance.\n\nWhen a hook has a name (`LlmHook.build(compiler, { name: 'weather' })`), the name has priority over the compiler or tool.\n\nEach hook has two handlers:\n\n- `before({ input, next, session, context, step })` runs instead of the original `execute`. The original `execute` runs only when you call `next(input)`. If you do not call `next`, the returned value becomes the tool output.\n- `after({ output, input, session, context, step })` runs after `before` is settled. `output` is a `PromiseSettledResult`, so you can handle errors. The returned value becomes the final tool output and must match the tool output type. By default, `after` rethrows the error or returns the value.\n\n```ts\nimport { LlmHook, LlmToolExecutionError } from '@n1k1t/pipelain';\n\n// Restrict paths of the built-in \"write\" tool\nconst guard = LlmHook\n .build<{ input: { path: string; content: string } }>('write')\n .before(({ input, next }) => {\n if (input.path.startsWith('/etc')) {\n throw LlmToolExecutionError.build('Writing to /etc is not allowed');\n }\n\n return next(input);\n });\n\n// Cache results of the custom tool and convert errors for the AI\nconst cache = new Map();\nconst weather = LlmHook\n .build(weatherTool)\n .before(async ({ input, next }) => {\n const cached = cache.get(input.city);\n if (cached) {\n return cached;\n }\n\n const output = await next(input);\n\n cache.set(input.city, output);\n return output;\n })\n .after(({ output }) => {\n if (output.status === 'rejected') {\n throw LlmToolExecutionError.build(output.reason);\n }\n\n return output.value;\n });\n\npipeline.step('hooked', ({ factory, context }) => factory\n .ai('Executing with hooks')\n .llm(({ context }) => context.llm.assign({\n tools: factory.tools\n .files('read-write')\n .custom({ weather: weatherTool })\n .provide(),\n }))\n .hooks([guard, weather])\n .prompt([`Check weather in ${context.input} and save it to weather.md`])\n);\n```\n\nWhen several hooks match the same tool, they run in the order of the array: the `before` handler of the first hook runs first, and its `after` handler runs last.\n\n> Built-in tools with options (for example, `write` in `files('read-write')`) are clones of the original compiler. A hook built from `llm.tools.write` matches them too.\n\n### Custom LLM Skills\n\nYou can also provide custom skills programmatically:\n\n```ts\npipeline.step('custom_skills', ({ factory, context }) => factory\n .ai('Using custom skills')\n .llm(({ context }) => context.llm.assign({\n skills: factory.skills\n // Provide custom skill objects\n .custom([{\n name: 'manual-skill',\n description: 'Manually defined skill',\n content: 'Manual skill content'\n }])\n .provide()\n }))\n .prompt(['...'])\n);\n```\n\n### Registering Skills from Markdown\n\nYou can register new skills from raw markdown content (with frontmatter) using `LlmSkillsFactory`:\n\n```ts\nimport { LlmSkillsFactory } from '@n1k1t/pipelain';\n\nconst rawSkill = `---\nname: custom-skill\ndescription: A custom skill description\n---\nSkill content goes here.`;\n\n// 1. Create a skills factory (optionally with existing skills)\nconst skills = LlmSkillsFactory.build();\n\n// 2. Register new skills from raw markdown\nskills.register(rawSkill);\n\n// 3. Use in a pipeline step\npipeline.step('specialized_task', ({ factory, context }) => factory\n .ai('Executing with registered skill')\n .llm(({ context }) => context.llm.assign({\n skills: skills.all().provide(),\n }))\n .prompt(['...'])\n);\n```\n\n---\n\n## License\n\nMIT\n","readmeFilename":"README.md"}