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Amj Tables Figures

Community
franklee16
amj-tables-figures

Use when building or cleaning the tables and figures for an Academy of Management Journal (AMJ) manuscript — correlation tables, regression/SEM/HLM result tables, the theoretical-model figure, and interaction plots in AOM house style. Finalizes exhibits; it does not run the analysis (amj-data-analysis) or frame the contribution (amj-contribution-framing).

Overview

Publisherfranklee16
Repositoryacademic-research-skills
Skill nameamj-tables-figures
Stars
225
Forks
34
Bundled files
Instructions only
Links
  • 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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by franklee16 on GitHub. Read the source before you install it.

Installation

Install the Amj Tables Figures 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/franklee16/academic-research-skills/tree/master/Awesome-Journal-Skills-main/Academy-of-Management-Journal-Skills/skills/amj-tables-figures
  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/franklee16/academic-research-skills.git /tmp/academic-research-skills
mkdir -p .claude/skills
cp -r /tmp/academic-research-skills/Awesome-Journal-Skills-main/Academy-of-Management-Journal-Skills/skills/amj-tables-figures .claude/skills/amj-tables-figures
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Amj Tables Figures 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 Amj Tables Figures 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 Amj Tables Figures 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.

Tables & Figures (amj-tables-figures)

When to trigger

  • Result tables are cluttered, hard to read, or not self-explanatory
  • The correlation/descriptives table is missing reliabilities or has inconsistent decimals
  • Interaction effects are reported only in text, not plotted
  • The theoretical model has no figure, or the figure does not match the hypotheses
  • Tables/figures are off AOM house style (verify the current AMJ Style Guide for Authors)

The exhibits an AMJ paper expects

  1. Means, SDs, and correlations table — typically Table 1; reliabilities on the diagonal in parentheses; note significance.
  2. Main results table(s) — regression/SEM/HLM, organized by nested models (controls → main effects → interactions), with standardized or unstandardized coefficients consistently labeled.
  3. Theoretical model figure — boxes and arrows mapping one-to-one to the hypotheses (H1, H2, …).
  4. Interaction/simple-slopes plots — one figure per significant interaction, with high/low moderator lines and axis labels in construct terms.
  5. Mediation/path figure where SEM is used — coefficients on paths, fit indices noted.

House-style discipline (verify against the current AOM Style Guide)

  • Self-contained: a reader should understand each table/figure from its title and notes alone.
  • Title above the table; comprehensive note below defines abbreviations, N, SE type, and significance markers.
  • Significance: use consistent markers (e.g., † p<.10, * p<.05, ** p<.01, *** p<.001) and define them in the note; state whether tests are one- or two-tailed.
  • Standard errors / CIs: report SEs (or CIs) in parentheses; be consistent across tables.
  • Decimals: consistent precision (commonly two decimals); align on the decimal point.
  • Nested models: report model fit / ΔR² / Δ-2LL across steps so the incremental contribution of interactions is visible.
  • Exhibits do not count toward the page limit. AOM's 40-page main-body maximum excludes tables and figures (it includes text, references, and appendices), so move dense supporting material into well-built exhibits rather than crowding the prose — but keep each exhibit genuinely necessary, not padding.
  • Confirm current limits on number/format of exhibits and figure resolution against the AMJ Style Guide for Authors and the ScholarOne portal.

Figures

  • Theoretical-model figure: clean boxes/arrows; label each path with its hypothesis number; no decorative clutter.
  • Interaction plots: plot predicted values of the DV across the predictor at ±1 SD of the moderator; label lines; keep the y-axis in the DV's metric.
  • Use grayscale-safe designs; ensure figures are legible in print and meet the resolution the portal requires.

Checklist

  • Table 1 = means, SDs, correlations, with reliabilities on the diagonal
  • Result tables use nested models with ΔR²/fit reported across steps
  • SEs/CIs in parentheses; consistent decimals and alignment
  • Significance markers defined in notes; one-/two-tailed stated
  • Every significant interaction has a simple-slopes plot
  • Theoretical-model figure maps one-to-one to the hypotheses
  • Each exhibit is self-contained (title + complete note)
  • Exhibit count/format checked against the AMJ Style Guide (tables/figures are excluded from the 40-page limit)

Anti-patterns

  • Wall-of-numbers tables with no nested structure and no fit statistics.
  • Undefined abbreviations or missing N/SE-type in notes.
  • Interactions reported in text only — reviewers expect a plot.
  • Model figure that contradicts the hypotheses (extra/missing arrows).
  • Inconsistent decimals/significance markers across tables.
  • Color-only figures that are unreadable in grayscale or below required resolution.

Output format

【Table 1】means/SD/correlations + reliabilities on diagonal? yes/no
【Result tables】nested models + ΔR²/fit reported? yes/no
【Interaction plots】one per significant interaction? yes/no
【Model figure】matches hypotheses one-to-one? yes/no
【Self-contained】all titles/notes complete? yes/no
【House-style check】markers/SE/decimals/resolution vs. AOM guide: pass/fix
【Next step】amj-writing-style

Frequently asked questions

What does the Amj Tables Figures AI skill do?

Use when building or cleaning the tables and figures for an Academy of Management Journal (AMJ) manuscript — correlation tables, regression/SEM/HLM result tables, the theoretical-model figure, and interaction plots in AOM house style. Finalizes exhibits; it does not run the analysis (amj-data-analysis) or frame the contribution (amj-contribution-framing).

Why use Amj Tables Figures on TypingMind?

Because you install it once and use it with any model. Amj Tables Figures 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 Amj Tables Figures in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/franklee16/academic-research-skills/tree/master/Awesome-Journal-Skills-main/Academy-of-Management-Journal-Skills/skills/amj-tables-figures. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Amj Tables Figures?

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 Amj Tables Figures?

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

Is the Amj Tables Figures AI skill free?

It is published on GitHub by franklee16. 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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