Add derivation ai

Add an ai derivation to a Folio sheet

Author a derivations/<target>.yaml of kind: ai, declare the contract field as x-derived: true, and verify with one folio materialize call.

When this skill applies

• The user says "make Folio fill this column with an LLM" or "classify / summarize / extract / translate every row".
• The answer is fundamentally fuzzy — free-text classification, summarization, extraction from messy inputs.
• The user wants Folio to manage the cache, retries, prompt versioning, and cost reporting — i.e. they don't just want a for loop calling the SDK.

This skill does not apply when:

• The answer is deterministic (use kind: python or kind: sql).
• The data already exists in a CSV / JSON file (use kind: import).
• The data lives in another Folio sheet keyed by the same PK (use kind: cross_sheet — see the add-derivation-cross-sheet skill).

Prerequisites

• A working Folio sheet (see folio-quickstart). You should already have contract.yaml and records.jsonl and folio validate <sheet> exits 0.
• An ANTHROPIC_API_KEY (or whichever provider your AIClient targets) in the environment, or a StubAIClient for offline runs.
• folio --help shows the materialize subcommand.

Procedure

1. Pick the target field name — what column the LLM will fill. Convention: snake_case ASCII, the same as other fields. Add it to contract.yaml with x-derived: true so it's clearly not a human-edited column:

   - name: industry_tag
     logicalType: string
     x-derived: true
     x-inputs: [company_name]      # mirrors `inputs:` in the derivation
   

2. List inputs — every field the prompt reads. The cache hashes these, so an honest list is what makes the cache correct. If the prompt reads no field (very rare for ai), use inputs: [].

3. Choose output: text or output: json. One target → text. Multi-target → json plus output_schema. There is no "list" or "tuple" output — you express that as a JSON object.

4. Write the prompt. Use prompt: for one-liners; prompt_ref: for anything multi-paragraph. prompt_ref paths are relative to the sheet root and the file's bytes go into input_hash, so editing the prompt invalidates the cache (correct behaviour).

5. Create derivations/<target>.yaml. Skeleton (single target, text output):

   # derivations/industry_tag.yaml
   targets: [industry_tag]
   inputs: [company_name]
   kind: ai
   model: claude-sonnet-4-6
   prompt: |
     Industry of {{ company_name }} in one word.
   output: text
   

   Multi-target with structured JSON output:

   # derivations/enrich.yaml
   targets: [industry, employee_count]
   inputs: [company_name, country]
   kind: ai
   model: claude-sonnet-4-6
   prompt_ref: prompts/enrich.md
   output: json
   output_schema:
     industry: string
     employee_count: integer
   

6. (Optional) Tune the loop. materialization: is a sub-block:

   materialization:
     respect_human_override: true   # default — skip cells a human edited
     retries: 0                     # default
     retry_delay_seconds: 1.0
   

   Retries cover AIClient errors only (timeouts, rate-limits). A missing output_schema key is a deterministic content error — it does not retry.

7. Validate, then run materialize on one record. Always smoke-test on a single record before letting the LLM loose on every row:

   folio validate ./customers
   folio materialize ./customers industry_tag \
     --actor agent:demo \
     --ids cust_001
   

   folio materialize takes the target as a positional argument (one at a time; omit it to materialize every derivation), and --ids is comma-separated or repeated.

   The output is the §10.6 envelope:

   {"materialized": 1, "skipped": 0, "failures": [], "total_cost": 0.0021}
   

8. Inspect the value & provenance. Read it back:

   folio list ./customers --filter "id = ?" --param cust_001
   folio provenance ./customers cust_001 industry_tag
   

   folio provenance takes the record ID and field as positional arguments. The provenance line includes model, input_hash, and cost_usd.

Verify

folio validate <sheet>
folio materialize <sheet> <field> --actor agent:demo --ids <one_id>

Both should exit 0 and the envelope's failures should be [].

Tips & idioms

• Substitution shape. {{ field }} substitutes the JSON-encoded value — strings get quotes, integers stay bare, arrays become bracket lists. This keeps prompts safe against quotes / newlines in the data.
• Deterministic first, AI fallback. Common pattern: a python derivation that handles the easy cases, then an ai derivation on the long tail. Keep the AI rows scarce — the cache is the savings.
• Prompts in their own file. Once a prompt is more than ~3 lines, use prompt_ref: and put the file under prompts/. Reviewers hate diffs of multi-line YAML strings.
• Multi-target = one cache key. All targets in a multi-target derivation share an input_hash. They update together or stay cached together — that's the invariant.

Common mistakes (don't make them)

• Forgetting x-derived: true in contract.yaml. The materialize loop still runs, but folio status and human editors will treat the field as a normal user-editable column.
• output: text with multiple targets. Folio will reject the contract — text output writes one cell. Use output: json + output_schema.
• Listing inputs the prompt doesn't actually read. The cache re-runs every time those (irrelevant) fields change. Keep inputs: honest.
• Editing the prompt without expecting a re-run. The prompt body is in the input_hash. That's the design — it means an old, outdated answer cannot stick around silently.
• Hardcoding an API key in derivations/*.yaml. Folio reads keys from the environment. The YAML stays clean and shippable.