Individual skill
structured-extraction
Use when text must become a typed, schema-conformant object you can trust — pulling fields into a fixed JSON shape, extracting line items as typed records, classifying into enums, and building the Pydantic or Zod model plus the validate-and-retry loop. Covers extractors that throw parse errors, leak markdown fences, or fabricate a value where the field is absent instead of returning null. NOT getting the text out of a PDF, scan or DOCX first (that is `document-processing`), NOT general prompt craft untied to a schema (that is `prompt-engineering`).
skills/structured-extraction/SKILL.md
Source commit d9d6bb338f66cc1e23998585e580a0ea45bbcb82
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- Matches the observed repository HEAD
- Yes
- Scanner rules are current
- No
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- Observed behavior
- Not checked yet
- Last checked
- Nov 6, 2026
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Repository context
- Repository stars (not skill ratings)
- 561
- Repository last updated
- Nov 4, 2026
- License
- MIT
Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.
- agent-skills Repository topic
- agentic-ai Repository topic
- ai-agents Repository topic
- ai-coding Repository topic
- claude-code Repository topic
- cli Repository topic
- cline Repository topic
- codex Repository topic
- coding-assistant Repository topic
- cursor Repository topic
- structured-outputs Declared skill tag
- json-schema Declared skill tag
- pydantic Declared skill tag
- extraction Declared skill tag
- validation Declared skill tag
- llm Declared skill tag
- engineering Derived category
Files and excerpt
File metadata and permitted, redacted excerpts come from the recorded scan. Source links are pinned to that commit.
Browse files
SKILL.mdcore · 14,463 bytesSKILL.mdcore · 14,463 bytesSKILL.mdcore · 14,463 bytesSKILL.mdcore · 14,463 bytesSKILL.mdcore · 14,463 bytesSKILL.mdcore · 14,463 bytesSKILL.mdcore · 14,463 bytesevals/README.mddoc · 1,153 bytesevals/cases.yamlasset · 4,826 bytesreferences/providers.mddoc · 6,716 bytesscripts/verify.shruns · 5,913 bytes
Read excerpt
# Structured extraction — text in, a typed object you can trust out
The deliverable is **a typed object that conforms to a schema you defined** — not prose, not "roughly JSON."
The whole skill rests on one distinction the rest of the file keeps returning to:
> Native structured outputs make the JSON **valid and typed**. They never make the values **correct**.
Constrained decoding guarantees the model cannot emit a token that breaks your schema, so `JSON.parse`
errors, missing keys, wrong types, and stray markdown fences disappear at the source. It does **nothing**
to stop the model from putting a plausible-but-wrong email in a `string` field, snapping a fuzzy category to
the wrong enum, or coercing `"$1,200"` into `1200.0` when the currency mattered. Owning both halves — the
shape (decoding) and the values (validation) — is this skill. If you only do the first half you ship a
database full of well-typed lies.
