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Schema Normalizer

Community
WILLOSCAR
schema-normalizer

Normalize cross-skill JSONL interfaces (ids + titles + citation key formats) so downstream skills do not rely on best-effort joins.

Overview

PublisherWILLOSCAR
Repositoryresearch-units-pipeline-skills
Skill nameschema-normalizer
Stars
513
Forks
39
Bundled files
1
Links
Skill sourceRepository
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by WILLOSCAR on GitHub. Read the source before you install it.

Installation

Install the Schema Normalizer AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/WILLOSCAR/research-units-pipeline-skills/tree/main/.codex/skills/schema-normalizer
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/WILLOSCAR/research-units-pipeline-skills.git /tmp/research-units-pipeline-skills
mkdir -p .claude/skills
cp -r /tmp/research-units-pipeline-skills/.codex/skills/schema-normalizer .claude/skills/schema-normalizer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Schema Normalizer in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Schema Normalizer on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Schema Normalizer is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Schema Normalizer (NO PROSE)

Triggers & routing

  • Trigger: schema normalize, jsonl contract, interface drift, join drift, 字段不一致, schema 规范化.
  • Use when: you have generated C2-C4 JSONL artifacts (outline/briefs/bindings/packs/anchors) and want deterministic, stable fields before self-loops/writing.

Purpose: close a common failure mode in skills-first pipelines: schema drift across JSONL artifacts.

When fields are inconsistent (missing ids/titles, mixed citation-key formats), downstream skills start doing best-effort joins and fragile parsing. This skill makes the interface explicit and deterministic.

Inputs

  • outline/outline.yml (source of truth for section/subsection ids + titles)
  • Optional (for citation-key sanity): citations/ref.bib
  • Default JSONL artifacts to normalize (arxiv-survey(-latex) C4 bridge):
    • outline/subsection_briefs.jsonl
    • outline/chapter_briefs.jsonl
    • outline/evidence_bindings.jsonl
    • outline/evidence_drafts.jsonl
    • outline/anchor_sheet.jsonl
  • Optional (run after writer packs are generated):
    • outline/writer_context_packs.jsonl

Outputs

  • output/SCHEMA_NORMALIZATION_REPORT.md (always written; PASS/FAIL + what changed)
  • The processed JSONL files are normalized in place (a .bak.* is created if changes are applied).

What gets normalized

1) IDs + titles (join keys)

For any record with sub_id: "<H2>.<H3>":

  • Ensure section_id exists (derived from the prefix before the dot)
  • Ensure title, section_title exist (filled from outline/outline.yml)

For any record with section_id: "<H2>":

  • Ensure section_title exists (filled from outline/outline.yml)

2) Citation key format (reduce parsing drift)

Within these C2-C4 JSONL artifacts, normalize citation keys so they are raw BibTeX keys (no @ prefix):

  • "citations": ["smith2023", "jones2024"]

Notes:

  • Final prose still uses Markdown citations: [@smith2023].
  • This skill does not add/remove citations; it only normalizes formatting.

When to run

Recommended placement in arxiv-survey(-latex):

  • Run after evidence-draft + anchor-sheet and before writer-context-pack + evidence-selfloop.
  • This ensures outline/evidence_drafts.jsonl and outline/anchor_sheet.jsonl are schema-stable before drafting packs are built.

Failure modes

  • If outline/outline.yml is missing or cannot be parsed, the skill FAILs.
  • If any target JSONL contains invalid JSON lines, the skill reports them and FAILs (do not proceed on corrupted artifacts).

Script (optional)

Quick Start

  • uv run python .codex/skills/schema-normalizer/scripts/run.py --help
  • Normalize the C4 bridge artifacts:
    • uv run python .codex/skills/schema-normalizer/scripts/run.py --workspace <workspace>

All Options

  • --workspace <dir>
  • --unit-id <U###>
  • --inputs <semicolon-separated>
  • --outputs <semicolon-separated>
  • --checkpoint <C#>

Examples

  • Normalize the default C4 artifacts (ids/titles + citations format):

    • uv run python .codex/skills/schema-normalizer/scripts/run.py --workspace <workspace> --inputs outline/outline.yml;citations/ref.bib;outline/subsection_briefs.jsonl;outline/chapter_briefs.jsonl;outline/evidence_bindings.jsonl;outline/evidence_drafts.jsonl;outline/anchor_sheet.jsonl --outputs output/SCHEMA_NORMALIZATION_REPORT.md

  • Normalize writer packs too (if you are running this after writer-context-pack):

    • uv run python .codex/skills/schema-normalizer/scripts/run.py --workspace <workspace> --inputs outline/outline.yml;citations/ref.bib;outline/writer_context_packs.jsonl --outputs output/SCHEMA_NORMALIZATION_REPORT.md

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Schema Normalizer AI skill do?

Normalize cross-skill JSONL interfaces (ids + titles + citation key formats) so downstream skills do not rely on best-effort joins.

Why use Schema Normalizer on TypingMind?

Because you install it once and use it with any model. Schema Normalizer is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Schema Normalizer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/WILLOSCAR/research-units-pipeline-skills/tree/main/.codex/skills/schema-normalizer. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Schema Normalizer?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Schema Normalizer?

As many as you like. As long as a model supports skills, you can use Schema Normalizer with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Schema Normalizer AI skill free?

It is published on GitHub by WILLOSCAR. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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