Copilot-Powered Agentic Pipeline Extension
Budget: ₹1,500 – ₹2,000 INR
I want a VS Code extension called “Agentic Pipeline” that lets me build complete data-or code-processing pipelines simply by chatting with Copilot. The user experience must feel like “vibe coding”: I open the workspace, start a conversation, describe what I need, and the extension does the rest while asking follow-up questions only when clarification is essential.
Core flow the extension must own
1. Collect the conversational requirements and match them to a metadata file stored in a dedicated directory (feel free to propose the file format).
2. Validate every new requirement against that metadata so the conversation always stays consistent.
3. Generate the actual pipeline code in whichever language the user requests—Python, Shell, Spark, or any other mainstream option.
4. Detect missing packages and install them automatically.
5. Execute the pipeline; on any error (syntax, logic, even performance regressions) run a self-healing cycle that fixes the problem or, if needed, asks the user for extra context, then retries.
6. Create unit/integration tests, run them, and loop back to code-generation if any test fails.
7. Produce a test report.
8. Generate end-user documentation explaining the final pipeline, including how to rerun or modify it.
Self-healing expectations
• Fixes syntax, logical, and basic performance issues without manual intervention.
• When automatic repair is impossible, prompt the user in the same chat and continue once clarified.
Extension internals I expect
• Written in TypeScript using the official VS Code Extension API.
• Uses the existing GitHub Copilot chat endpoint so no custom model hosting is needed.
• Clean, modular code so new languages or stages can be plugged in later.
• Minimal external dependencies—only what the VS Code marketplace allows.
Deliverables
• Source repository with clear README and setup instructions.
• Packaged .vsix ready for Marketplace submission.
• Demo workspace that shows the end-to-end flow.
• Short screencast (optional but appreciated) proving the self-healing loop works.
-Should deploy in my machine.
Acceptance criteria
• A conversational session can generate, execute, fix, test, and document a simple Python, Shell, and Spark pipeline without manual edits.
• All generated tests pass on the second run at worst.
• No unhandled errors appear in the VS Code console.
If you already have something similar, I’m open to reviewing it; otherwise, build from scratch following the outline above.
Core flow the extension must own
1. Collect the conversational requirements and match them to a metadata file stored in a dedicated directory (feel free to propose the file format).
2. Validate every new requirement against that metadata so the conversation always stays consistent.
3. Generate the actual pipeline code in whichever language the user requests—Python, Shell, Spark, or any other mainstream option.
4. Detect missing packages and install them automatically.
5. Execute the pipeline; on any error (syntax, logic, even performance regressions) run a self-healing cycle that fixes the problem or, if needed, asks the user for extra context, then retries.
6. Create unit/integration tests, run them, and loop back to code-generation if any test fails.
7. Produce a test report.
8. Generate end-user documentation explaining the final pipeline, including how to rerun or modify it.
Self-healing expectations
• Fixes syntax, logical, and basic performance issues without manual intervention.
• When automatic repair is impossible, prompt the user in the same chat and continue once clarified.
Extension internals I expect
• Written in TypeScript using the official VS Code Extension API.
• Uses the existing GitHub Copilot chat endpoint so no custom model hosting is needed.
• Clean, modular code so new languages or stages can be plugged in later.
• Minimal external dependencies—only what the VS Code marketplace allows.
Deliverables
• Source repository with clear README and setup instructions.
• Packaged .vsix ready for Marketplace submission.
• Demo workspace that shows the end-to-end flow.
• Short screencast (optional but appreciated) proving the self-healing loop works.
-Should deploy in my machine.
Acceptance criteria
• A conversational session can generate, execute, fix, test, and document a simple Python, Shell, and Spark pipeline without manual edits.
• All generated tests pass on the second run at worst.
• No unhandled errors appear in the VS Code console.
If you already have something similar, I’m open to reviewing it; otherwise, build from scratch following the outline above.