AI SDLC Scaffold, repo template for AI-assisted software development

Generates project structure, configuration files, and boilerplate code by accepting natural language project descriptions and converting them into a complete repository layout. Uses prompt engineering to guide LLMs through multi-step generation of directory hierarchies, dependency manifests, and starter code, with support for multiple tech stacks and frameworks through template composition patterns.

Provides a structured framework for integrating LLM-assisted development into the SDLC by defining prompt templates, execution patterns, and integration points for common development tasks (code review, testing, documentation). Uses a template-based approach where developers define workflows as configuration files that route code through LLM pipelines with context injection and output validation.

Implements error handling patterns for LLM failures (rate limits, timeouts, invalid responses) with configurable fallback strategies (retry with backoff, use alternative provider, use cached response, manual intervention). Uses a resilience pattern where each workflow step has defined failure modes and recovery strategies, ensuring workflows degrade gracefully rather than failing completely.

Validates LLM outputs against defined schemas (JSON, code syntax, format requirements) and quality criteria (length, complexity, coverage) before accepting them into workflows. Uses a validation layer where outputs are checked against schemas and rules, with failures triggering re-generation, manual review, or fallback strategies. Supports structured outputs (JSON, code) with schema validation and unstructured outputs (text) with regex or semantic validation.

Enables team collaboration on AI workflows by providing shared prompt libraries, version control for prompts and configurations, and audit trails showing who made what changes and when. Uses a centralized repository pattern where prompts, workflows, and configurations are stored with metadata (author, timestamp, change description), enabling teams to collaborate on AI development similar to code collaboration.

Automatically extracts and injects relevant project context (architecture docs, code examples, style guides, dependency information) into LLM prompts to improve code generation quality. Uses file-based context selection patterns where developers specify which files/directories are relevant to a task, and the system prepends them to prompts with structural markers to help LLMs understand project conventions.

Breaks down complex development tasks (e.g., 'implement authentication system') into smaller LLM-solvable steps with validation gates between each step. Uses a chain-of-thought pattern where each step produces intermediate artifacts (design docs, code sketches, test plans) that are validated before proceeding to the next step, reducing hallucinations and improving overall quality.

Analyzes code changes against project conventions, best practices, and custom rules by feeding diffs and context to LLMs, which generate structured feedback with specific line-by-line comments and suggestions. Uses a template-based approach where review criteria (security, performance, style, testing) are defined as prompts that guide the LLM to produce consistent, actionable feedback.

Generates unit tests, integration tests, and edge case tests by analyzing code structure and requirements, then producing test code that covers specified coverage targets. Uses LLM-based test generation where prompts include the function/module to test, existing tests as examples, and coverage goals, producing executable test code in the project's test framework.

Automatically generates README files, API documentation, and architecture guides by analyzing code structure, comments, and project metadata. Uses LLM-based documentation generation where the system extracts code structure (functions, classes, modules), existing comments, and project context, then generates human-readable documentation with examples and usage patterns.

Integrates AI analysis into Git workflows by analyzing commits, pull requests, and branches to generate commit messages, detect breaking changes, and suggest refactoring opportunities. Uses Git hooks and metadata to trigger LLM analysis at key points (pre-commit, pre-push, PR creation), producing structured outputs that inform development decisions.

Abstracts LLM provider differences (OpenAI, Anthropic, local models) behind a unified interface, allowing workflows to switch providers via configuration without code changes. Uses a provider adapter pattern where each LLM provider implements a standard interface (prompt submission, response parsing, token counting), and a configuration layer routes requests to the appropriate provider based on task requirements.

Enables version control and experimentation for prompts by storing prompt templates with metadata (version, author, performance metrics) and supporting A/B testing workflows where different prompt versions are tested against the same input. Uses a prompt registry pattern where prompts are stored as versioned artifacts with associated metrics, enabling data-driven prompt optimization.