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Asq Contribution Framing

Community
franklee16
asq-contribution-framing

Use when sharpening the theoretical contribution and the "so what" for an Administrative Science Quarterly (ASQ) manuscript once results exist. Frames the contribution; it does not develop the underlying theory or run analysis.

Overview

Publisherfranklee16
Repositoryacademic-research-skills
Skill nameasq-contribution-framing
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 Contribution Framing 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-contribution-framing
  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-contribution-framing .claude/skills/asq-contribution-framing
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Asq Contribution Framing 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 Contribution Framing 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 Contribution Framing 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.

Contribution Framing (asq-contribution-framing)

When to trigger

  • Results exist but you cannot state the contribution in one crisp sentence
  • The intro/discussion reads as "we found X" rather than "we change how the field thinks"
  • A reviewer says "interesting findings, but what's the theoretical contribution?"
  • The paper risks reading as a method demonstration rather than a theory advance

The ASQ standard: a theoretical contribution, not a finding

At ASQ the deliverable is a change in how organization theorists understand something. A finding answers a question; a contribution reframes the question or overturns a prior. Frame around the surprise identified in asq-topic-selection and the mechanism from asq-theory-development. The ASQ archetype is a paper that gives the field a new lens it cannot un-see — the garbage-can model of organizational choice (Cohen, March & Olsen 1972, ASQ) or Barley's (1986, ASQ) reconception of technology as an occasion for structuring. Note what is not the ASQ deliverable: an improvement in firm performance (the SMJ register) or a well-powered confirmation of a moderated hypothesis (a common AMJ profile) is not, by itself, a theoretical contribution here.

Types of contribution (name yours)

  • Reframing: you reconceptualize a construct or process (the field was looking at it wrong)
  • Mechanism: you reveal how/why something happens that was previously a black box
  • Boundary/contingency: you show when an established effect reverses or breaks
  • Integration: you reconcile two conversations that appeared to conflict
  • New construct/process model: you name and theorize something the field lacked language for

Avoid the weakest "contribution" types ASQ tends to reject: "we replicate prior work in a new setting," "we add a moderator," "we study an understudied topic."

The contribution paragraph

Place a sharp statement early (intro) and revisit it in the discussion. Shape:

"We contribute to [conversation] by showing [surprising insight]. Whereas prior work assumed [prior], we demonstrate [reframing/mechanism/boundary], driven by [mechanism]. This implies [non-obvious consequence] and reopens [new question]."

Discussion section that earns ASQ acceptance

  • Lead with the theoretical contribution, not a summary of findings.
  • Generativity: spell out the new questions and research directions your insight opens — ASQ values ideas that travel.
  • Scope and limits: state boundary conditions and what the study cannot claim; this strengthens, not weakens, the contribution.
  • Implications for theory dominate; practical implications, if included, are brief and genuine.
  • Connect back to the surprise: remind the reader why the prior was wrong.

Calibrate the claim

  • Too modest → "so what?" rejection. Too grand → "overclaiming" rejection.
  • The claim must be exactly as large as the evidence supports — no larger, no smaller.

Checklist

  • Contribution stated in one sentence a non-specialist organization theorist would grasp
  • Contribution type is named (reframing / mechanism / boundary / integration / new model)
  • It is a change in understanding, not just a new finding
  • Discussion leads with theory, not a findings recap
  • Generativity is explicit (the new questions opened)
  • Boundary conditions and limits stated honestly
  • Claim size matches evidence (no over/under-claiming)

Anti-patterns

  • "We found X" framing with no reframing of the conversation
  • Listing 4–5 weak contributions instead of one strong one
  • Contribution = "we added a moderator / new setting / new sample"
  • Practical implications padded to substitute for a thin theoretical contribution
  • Burying the contribution under a findings summary in the discussion
  • Method-in-search-of-theory: the paper's "point" is the technique

Output format

【Contribution (one sentence)】...
【Type】reframing / mechanism / boundary / integration / new model
【Prior overturned】what the field assumed
【Generativity】new questions opened
【Claim calibration】matches evidence? (yes/no)
【Next step】asq-tables-figures

Frequently asked questions

What does the Asq Contribution Framing AI skill do?

Use when sharpening the theoretical contribution and the "so what" for an Administrative Science Quarterly (ASQ) manuscript once results exist. Frames the contribution; it does not develop the underlying theory or run analysis.

Why use Asq Contribution Framing on TypingMind?

Because you install it once and use it with any model. Asq Contribution Framing 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 Contribution Framing 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-contribution-framing. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Asq Contribution Framing?

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 Contribution Framing?

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

Is the Asq Contribution Framing 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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