Business Analyst For Coding Agents

The problem

Vague project descriptions lead to scope creep and implementation errors. Translating a napkin sketch into a developer-ready technical specification takes hours of manual documentation.

What it does

• Classifies system types to apply relevant architectural patterns automatically.
• Fills missing requirements with deterministic defaults for retries, timeouts, and auth.
• Generates comprehensive Markdown specs including data flows and error handling tables.
• Writes implementation-ready files directly to your workspace using bash tools.
• Enforces strict algorithmic logic for every conditional branch in the processing steps.

Frameworks & tools

Markdown, Bash, Filesystem, AI Agents.

Why this beats prompting it yourself

A standard LLM prompt often yields prose-heavy, ambiguous summaries. This skill uses a multi-step pipeline and a fixed resolution order to ensure every edge case has a defined behavior and every table is cross-validated for consistency.

Use cases

• Formalize a Slack conversation into a technical requirements document.
• Generate structured specifications for MCP servers or API wrappers.
• Create consistent documentation for legacy scripts with missing specs.
• Draft unambiguous prompts for other AI agents to execute.

Known limitations

Persists data in-memory by default unless you explicitly request a database. Generated files are saved to a specific ai-ba subfolder.