generate-from-spec

Generate Taxonomy from Spec

Build a custom taxonomy from a user's natural-language or structured specification. Elicit the domain, scope, schema, and generate a structured, reviewed taxonomy ready for load or export.

When to use

• "Create a taxonomy of LLM evaluation types"
• "I need product categories for [domain]"
• "Generate a content classification scheme for…"
• "Build me a list of [entity types] with [fields]"

Inputs to gather

• Domain/purpose: What is this taxonomy for? (e.g., "types of software testing", "product categories for an e-commerce site")
• Expected size: How many entries? (rough order of magnitude)
• Fields per entry: What columns? (e.g., code, label, description, parent_code, metadata JSON)
• Hierarchy? Flat or parent/child tree? (defer to hierarchical-taxonomies if complex)
• Required exhaustiveness: Complete/canonical list vs. representative sample?

Procedure

1. Elicit and confirm the spec: Ask clarifying questions about domain, scope, fields. Write a brief spec block to show the user you understand the requirement.
2. Generate the taxonomy:
   • For small sets (≤50 entries): Write directly as a structured list (dict of code → label, description, optional parent_code).
   • For larger or specialized sets (>50 entries, domain-specific): Suggest spawning a research subagent or conducting web research to ensure comprehensive coverage; do not guess.
3. Assign stable codes: Use snake_case slugs derived from the label (e.g., "Instruction Following" → instruction_following), or suggest numeric IDs if the user prefers. Ensure codes are URL-safe and unique.
4. Structure the output:
   • Write to data/<name>/<name>.csv with columns: code (PK), label, description, source (how the entry was generated/sourced), generated_at (ISO 8601 timestamp).
   • Also write data/<name>/<name>.json (array of objects, same fields).
   • Include deterministic ordering: alphabetical by code, or by user-specified priority.
5. Provide review guidance: Print the generated list for the user to review before load. Highlight any entries that seem uncertain or out-of-scope. Never auto-load without explicit approval.

Output / side effects

• CSV + JSON in data/<name>/ with full taxonomy and metadata.
• User review checkpoint before any downstream load.

Safety / constraints

• No auto-load: Always require explicit user approval before writing to a database.
• Sourcing: Document the source for each entry (user-specified, web research, inference).
• Reversible: Generated taxonomies are non-binding until loaded; user can discard or edit them freely.