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Appendix Table Writer

Community
WILLOSCAR
appendix-table-writer

Curate reader-facing survey tables for the Appendix (clean layout + high information density), using only in-scope evidence and existing citation keys.

Overview

PublisherWILLOSCAR
Repositoryresearch-units-pipeline-skills
Skill nameappendix-table-writer
Stars
513
Forks
39
Bundled files
3
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.

  • 3 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 Appendix Table Writer 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/appendix-table-writer
  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/appendix-table-writer .claude/skills/appendix-table-writer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Appendix Table Writer 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 Appendix Table Writer 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 Appendix Table Writer 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.

Appendix Table Writer (publishable survey tables)

Triggers & routing

  • Trigger: appendix tables, publishable tables, survey tables, reader tables, 附录表格, 可发表表格, 综述表格.
  • Use when: you have C4 artifacts (evidence packs + anchor sheet + citations) and want tables that look like a real survey (not internal logs).

Why this exists

The pipeline can produce index tables that are useful for planning/debugging, but read like internal artifacts.

This skill writes publishable, reader-facing tables that can live in an Appendix:

  • cleaner layout
  • higher information density
  • survey-style organization (methods/benchmarks/risks), not intermediate state

Index tables remain in outline/tables_index.md and should not be copied verbatim into the paper.

Inputs

  • outline/table_schema.md (table intent + evidence mapping)
  • outline/tables_index.md (internal index; optional but recommended)
  • outline/subsection_briefs.jsonl
  • outline/evidence_drafts.jsonl
  • outline/anchor_sheet.jsonl
  • citations/ref.bib
  • Optional: GOAL.md

Read as needed:

  • references/table_cell_hygiene.md when Appendix table cells still copy raw paper self-narration or generic result wrappers

Machine-readable assets:

  • assets/table_cell_hygiene.json

Output

  • outline/tables_appendix.md

Roles (use explicitly)

Survey Table Curator (reader lens)

Mission: choose tables a reader actually wants in a survey Appendix.

Do:

  • prefer 2-3 tables that answer big questions (methods, evaluation, risks)
  • make rows comparable (same row unit across the table)
  • make the table legible without reading the whole paper

Avoid:

  • one-row-per-H3 index dumps
  • columns named like internal axes ("axes", "blocking_missing", "evidence readiness")

Production Editor (layout)

Mission: make the table look publishable in LaTeX.

Do:

  • keep columns <= 4
  • keep cells short (phrases, not sentences)
  • use <br> sparingly (0-1 per cell; never a list dump)

Avoid:

  • 6-8 columns with tiny unreadable text
  • cells that look like notes (semicolon chains + slash lists + long parentheticals)
  • slash-separated axis markers (A/B/C) in captions/headers/cells (post-merge voice gate will flag them); use commas or 'and' instead
  • internal axis jargon that reads like an intermediate artifact once printed (e.g., calling table columns "tokens"); prefer "protocol details/metadata/assumptions"

Evidence Steward (verifiability)

Mission: prevent hallucinations.

Do:

  • every row must include citations in a dedicated column (e.g., "Key refs")
  • only restate what appears in evidence packs / anchor sheet
  • when evidence is thin, prefer fewer rows with stronger grounding

Avoid:

  • "representative works" with no supporting claim in packs/anchors
  • adding benchmark/method details not present upstream

Table contract (publishable, Appendix-ready)

outline/tables_appendix.md must:

  • contain >=1 Markdown table for course_paper, or >=2 for survey / deep
  • use a caption line before each table, e.g. **Appendix Table A1. Representative systems by method family and evaluation setting**
  • contain no headings (#, ##, ###) inside the file (the merger adds an Appendix heading)
  • contain no placeholders (TODO, TBD, FIXME, ASCII three-dot ellipsis, unicode ellipsis)
  • contain citations in rows using [@BibKey] (keys must exist in citations/ref.bib)
  • avoid pipeline jargon and index-like column names

Workflow (explicit inputs)

  • Start from GOAL.md (scope) and outline/table_schema.md (what each table must answer).
  • Read queries.md:draft_profile when present; course_paper uses one strong reader table by default, while survey / deep retain at least two.
  • Use outline/tables_index.md as a shortlist source, but do not paste it verbatim.
  • Fill rows/cells using outline/subsection_briefs.jsonl, outline/evidence_drafts.jsonl, and outline/anchor_sheet.jsonl (no guessing).
  • Validate every cited key against citations/ref.bib.

Recommended Appendix tables (default set)

If you are unsure what to build, start with these two:

  1. Method/architecture map (representative works)
  • Row unit: work/system line (not H3 id)
  • Columns (example):
    • Work (short name)
    • Core idea (1 short phrase)
    • Loop + interface assumptions (1 short phrase; reader-facing)
    • Key refs (2-4 cite keys)
  1. Evaluation protocol / benchmark map
  • Row unit: benchmark / evaluation setting (or a canonical protocol dimension if benchmarks are thin)
  • Columns (example):
    • Benchmark / setting
    • Task + metric (phrases, not definitions)
    • Key protocol constraints (budget/cost/latency/steps/tool access/threat model)
    • Key refs (2-4 cite keys)

Optional third (only if it stays clean): 3) Risk / threat-surface map

  • Row unit: threat/failure mode category
  • Columns: surface; why it matters; mitigation pattern; key refs

Positive / negative examples (style)

Bad (index table / internal notes):

  • Column: "Axes"
  • Cell: planning / memory / tools / eval / safety (slash dump)
  • Rows: every H3 id with 5+ <br> lines

Good (survey table):

  • Column labels are reader-facing ("Core idea", "Task + metric", "Constraint")
  • Cells are short phrases (no narration)
  • A reader can scan and compare rows quickly

Also good (avoid intermediate-artifact tells):

  • Don't label columns as "token(s)". If you need the idea, rewrite as "protocol details/metadata/assumptions".
  • Avoid ASCII arrows like -> inside cells; prefer natural phrasing (e.g., "interleaves reasoning traces with tool actions").

When to stop / route upstream

If you cannot fill a row without guessing:

  • remove the row (prefer fewer, solid rows), and
  • route upstream: strengthen evidence-draft / anchor-sheet for that area.

Script (generator + validator)

Quick Start

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

All Options

  • --workspace <workspace> (required)
  • --unit-id <id> (optional; used only for runner bookkeeping)
  • --inputs <a;b;c> (optional; ignored by the validator; kept for runner compatibility)
  • --outputs <relpath> (optional; defaults to outline/tables_appendix.md)
  • --checkpoint <C#> (optional; ignored by the validator)

Examples

  • Validate the default appendix tables file:

    uv run python .codex/skills/appendix-table-writer/scripts/run.py --workspace workspaces/e2e-agent-survey-latex-verify-YYYYMMDD-HHMMSS

  • Validate a workspace that writes appendix tables to a non-standard path:

    uv run python .codex/skills/appendix-table-writer/scripts/run.py --workspace <workspace> --outputs outline/tables_appendix.md

Notes:

  • This script writes outline/tables_appendix.md from the existing evidence artifacts and then validates the result.
  • It always writes a short report to output/TABLES_APPENDIX_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 Appendix Table Writer AI skill do?

Curate reader-facing survey tables for the Appendix (clean layout + high information density), using only in-scope evidence and existing citation keys.

Why use Appendix Table Writer on TypingMind?

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

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

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 Appendix Table Writer?

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

Is the Appendix Table Writer 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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