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Table Filler

Community
WILLOSCAR
table-filler

Fill `outline/tables_index.md` from `outline/table_schema.md` + evidence packs (NO PROSE in cells; citation-backed rows).

Overview

PublisherWILLOSCAR
Repositoryresearch-units-pipeline-skills
Skill nametable-filler
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 Table Filler 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/table-filler
  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/table-filler .claude/skills/table-filler
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Table Filler 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 Table Filler 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 Table Filler 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.

Table Filler (index tables, evidence-first)

Triggers & routing

  • Trigger: table filler, fill tables, evidence-first tables, index tables, 表格填充, 索引表.
  • Use when: table schema exists and evidence packs are ready; you want a compact, citation-backed index table to support later writing and Appendix table curation.

Goal: produce outline/tables_index.md as an internal, citation-backed index.

This file is a planning/traceability artifact:

  • it is useful for debugging coverage and for quickly seeing what evidence exists
  • it should NOT be pasted into the paper verbatim

Reader-facing tables belong in outline/tables_appendix.md and are curated by appendix-table-writer.

Default mode: semantic (LLM-first)

Treat this as filling a table artifact from evidence packs:

  • restate evidence pack content in compact cells
  • attach citations in the row (avoid cite-dump paragraphs)

Workflow (explicit inputs)

  • Read outline/table_schema.md first (it defines what each table must answer).
  • Read queries.md:draft_profile when present (course_paper requires one index table; survey / deep require two).
  • Use outline/subsection_briefs.jsonl + outline/evidence_drafts.jsonl to fill the subsection map and pick in-scope citations.
  • Use outline/anchor_sheet.jsonl to fill the anchor-fact table (prefer quant/eval hooks).
  • Validate cite keys against citations/ref.bib.

Inputs

  • outline/table_schema.md
  • outline/subsection_briefs.jsonl
  • outline/evidence_drafts.jsonl
  • outline/anchor_sheet.jsonl
  • citations/ref.bib

Output

  • outline/tables_index.md

Output format contract

outline/tables_index.md must:

  • contain >=1 Markdown table for course_paper, or >=2 for survey / deep
  • use a caption line before each table, e.g. **Index Table 1. ...**
  • contain no Markdown headings (#, ##, ###) unless you explicitly want them for internal readability
  • include citations in rows using [@BibKey]
  • avoid placeholders (TODO, ..., …, scaffold comments)
  • avoid paragraph cells (short phrases; use <br> sparingly)

What to fill (recommended defaults)

  1. Subsection map (axes + representative works)
  • Axes come from subsection_briefs.axes.
  • Representative works come from citations in the evidence pack.
  1. Concrete anchors (benchmarks / numbers / caveats)
  • Anchor facts come from outline/anchor_sheet.jsonl (curated, citation-backed).
  • Representative works come from citations in the anchor sheet (or the evidence pack as fallback).

Common failure modes

  • Cells become long prose

    • Fix: compress into short phrases; move narrative explanation into prose sections.

  • Rows cannot be filled without guessing

    • Fix: treat as an evidence gap (route to evidence-selfloop / evidence-draft) OR narrow the schema.

Script (optional bootstrap)

Quick Start

  • uv run python .codex/skills/table-filler/scripts/run.py --help
  • uv run python .codex/skills/table-filler/scripts/run.py --workspace <workspace>

All Options

  • --workspace <workspace> (required)
  • --unit-id <id> (optional; used only for runner bookkeeping)
  • --inputs <schema;briefs;packs;anchors;bib> (optional; override inputs)
  • --outputs <relpath> (optional; defaults to outline/tables_index.md)
  • --checkpoint <C#> (optional; ignored by the bootstrapper)

Examples

  • Bootstrap index tables (default output path):

    uv run python .codex/skills/table-filler/scripts/run.py --workspace <workspace>

  • Write to a custom index file (rare):

    uv run python .codex/skills/table-filler/scripts/run.py --workspace <workspace> --outputs outline/tables_index.md

Notes:

  • The script is deterministic bootstrap; treat the result as an internal index artifact.
  • Curate reader-facing Appendix tables separately via appendix-table-writer.

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 Table Filler AI skill do?

Fill `outline/tables_index.md` from `outline/table_schema.md` + evidence packs (NO PROSE in cells; citation-backed rows).

Why use Table Filler on TypingMind?

Because you install it once and use it with any model. Table Filler 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 Table Filler in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/WILLOSCAR/research-units-pipeline-skills/tree/main/.codex/skills/table-filler. 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 Table Filler?

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 Table Filler?

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

Is the Table Filler 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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