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Asq Topic Selection

Community
franklee16
asq-topic-selection

Use when judging whether a research idea fits Administrative Science Quarterly (ASQ) and has the *surprising* theoretical insight ASQ demands. Sharpens the puzzle and the contribution claim; it does not design the study or write theory sections.

Overview

Publisherfranklee16
Repositoryacademic-research-skills
Skill nameasq-topic-selection
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 Asq Topic Selection 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/Administrative-Science-Quarterly-Skills/skills/asq-topic-selection
  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/Administrative-Science-Quarterly-Skills/skills/asq-topic-selection .claude/skills/asq-topic-selection
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Asq Topic Selection 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 Asq Topic Selection 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 Asq Topic Selection 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.

Topic Selection & Fit (asq-topic-selection)

When to trigger

  • You have an interesting phenomenon but are unsure it rises to an ASQ-level puzzle
  • The idea feels descriptive ("X happens in organizations") rather than surprising
  • You can describe a finding but not yet say why a smart org-theory reader would be wrong about it
  • You are choosing between ASQ and AMJ/SMJ/Organization Science as a home

What ASQ rewards

ASQ publishes deep, often counterintuitive insight about organizations and the social structures around them. Since its 1956 founding (by James D. Thompson at Cornell), the journal has prized organizational ideas drawn from sociology, psychology, political science, economics, anthropology, and management — not a single home discipline. The bar is not "is this interesting?" but "does this change how we understand organizing?" A finding that merely raises firm performance (the SMJ test) or cleanly tests a hypothesis in a new sample (a common AMJ profile) is not yet an ASQ paper.

Fit test — a strong ASQ paper usually has all four:

  1. A puzzle, not a topic. There is a tension, anomaly, or contradiction that existing theory cannot explain or predicts the opposite of what you find.
  2. An organizational/sociological core. The phenomenon is about organizations, organizing, fields, categories, identity, status, networks, institutions, careers, or coordination — not a generic psychology or pure-economics question.
  3. A surprise. The reader's prior is overturned or meaningfully complicated.
  4. Generativity. The insight opens new questions; it is a theoretical lever, not a one-off finding.

Sharpening the puzzle

Run the idea through these prompts before any data work:

  • Anomaly framing: "Theory T predicts A, but in setting S we observe not-A. Why?"
  • Inversion: "Everyone assumes mechanism M produces outcome O. When does M produce the opposite?"
  • Boundary breach: "Construct C is treated as fixed/given. What if it is produced, contested, or fragile?"
  • Cross-level surprise: "An individual-level intuition fails at the organizational or field level (or vice versa)."

If the best you can do is "no one has studied X in setting S," that is a gap, not a puzzle — keep digging or move to a different journal.

Method follows the question

Decide which engine the puzzle needs — this previews asq-methods:

  • How / why a process unfolds, identity/meaning, emergent dynamics → inductive / qualitative (grounded-theory, ethnographic, historical, comparative case). ASQ has published field-defining inductive work — e.g., Barley's (1986) observational study of CT scanners and the social order of radiology departments, and Weick's (1993) interpretive analysis of the Mann Gulch disaster.
  • Whether and how much a relationship holds across many cases, with identification → quantitative (panel, event-history, network, archival, experiment)

Do not pick method first and reverse-engineer a question. Unlike journals where the quantitative hypothesis-testing template is the default expectation, at ASQ a single deep ethnography or a historical case can be the whole paper if the insight is profound and the craft is high.

Contribution claim (one paragraph)

Draft the claim now, in this shape:

"We study [puzzle] in [setting]. Existing work in [conversation] expects [prior]. We show instead that [surprising insight], because [mechanism]. This reframes [construct/process] and matters for [organizational consequence]."

If you cannot fill every slot, the topic is not ready.

Checklist

  • The idea is stated as a puzzle/anomaly, not a topic or gap
  • The core is organizational/sociological, in ASQ's wheelhouse
  • There is a genuine surprise that overturns or complicates a prior
  • The insight is generative (opens questions), not a dead-end finding
  • Method is chosen because the puzzle demands it (qualitative is fully first-class here)
  • A one-paragraph contribution claim is drafted with every slot filled
  • You have 2–3 recent ASQ articles you can name as conversation partners (not just AMJ/SMJ pieces)

Anti-patterns

  • "Gap-spotting": "X has not been studied in context Y" with no theoretical tension
  • Phenomenon-chasing: a timely topic (AI, gig work, ESG) with no organizational mechanism
  • Confirmation papers: confirming established theory in a new sample — no surprise
  • Method-led framing: "we have great data/an elegant model, now what can we ask?"
  • Wrong wheelhouse: a pure micro-cognition or pure-finance question dressed in org language

Output format

【Puzzle】one-sentence anomaly/tension
【ASQ fit】organizational core? (yes/no + why)
【Surprise】what prior is overturned
【Engine needed】qualitative / quantitative + why
【Contribution claim】the filled one-paragraph claim
【Next step】asq-theory-development

Frequently asked questions

What does the Asq Topic Selection AI skill do?

Use when judging whether a research idea fits Administrative Science Quarterly (ASQ) and has the *surprising* theoretical insight ASQ demands. Sharpens the puzzle and the contribution claim; it does not design the study or write theory sections.

Why use Asq Topic Selection on TypingMind?

Because you install it once and use it with any model. Asq Topic Selection 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 Asq Topic Selection 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/Administrative-Science-Quarterly-Skills/skills/asq-topic-selection. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Asq Topic Selection?

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 Asq Topic Selection?

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

Is the Asq Topic Selection 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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