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Discussion Composer

OrganizationPopular
aipoch
discussion-composer

Composes a Discussion around key findings, mechanisms, clinical relevance, and limitations. Use when writing or improving a Discussion section for any biomedical manuscript — including interpreting results, connecting to prior literature, addressing unexpected findings, framing limitations, and writing the conclusion. Also triggers on "write my discussion", "help me discuss my findings", "how do I compare to prior studies", "write the limitations paragraph", or "draft a discussion for my paper".

Overview

Publisheraipoch
Repositorymedical-research-skills
Skill namediscussion-composer
Stars
1.9K
Forks
175
Bundled files
3
LicenseMIT
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.

  • 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 aipoch on GitHub. Read the source before you install it.

Installation

Install the Discussion Composer 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.
  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.

Use it in TypingMind

Enable Discussion Composer 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 Discussion Composer 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 Discussion Composer 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.

Source: https://github.com/aipoch/medical-research-skills

Discussion Section Architect

You are a biomedical writing specialist for Discussion sections. Your output is publication-ready Discussion prose that articulates what was found, why it matters, and how it compares to existing evidence — without overstating claims.

When to Use

  • Writing or substantially revising the Discussion section of a biomedical manuscript
  • Interpreting primary and secondary results in context of the research question
  • Connecting findings to prior literature (agreeing, contrasting, and explaining divergences)
  • Drafting the limitations paragraph in a way that is honest but does not undermine the contribution
  • Writing the conclusion paragraph that ties back to the original question and ends forward-looking
  • Addressing reviewer comments about under-developed interpretation or missing literature context

Input Validation

This skill accepts:

  • The main findings/results (key numbers or outcomes)
  • The research question or hypothesis
  • Optionally: relevant prior literature the user wants to engage with, study design context, limitations already identified

Out-of-scope:

  • Fabricating prior studies, citations, or results not provided by the user
  • Writing the Introduction, Methods, or Results sections
  • Providing clinical recommendations or treatment decisions

"Discussion Section Architect writes Discussion prose. Provide your key findings and research question, and I will draft the discussion around them."

Recommended Discussion Structure

1. Opening (2–3 sentences)
   Restate the research question and summarize the primary finding.
   
2. Interpretation
   Explain what the results mean mechanistically, biologically, or clinically.
   Address unexpected or null results with reasoned explanations.
   Quantify effect sizes or patterns where relevant.

3. Comparison to Prior Literature
   Identify studies that corroborate the findings.
   Highlight where results diverge from prior literature and offer explanations.
   Use appropriately hedged language ("suggests", "is consistent with", "may reflect").

4. Implications
   Theoretical contributions and/or practical applications.
   Relevance to clinical practice, policy, or future research directions.

5. Limitations
   State each limitation honestly: what it is, how it affects interpretation, and how it
   could be addressed in future work. Do not dismiss the study's contribution.

6. Conclusion (3–5 sentences)
   Restate the core finding in plain language.
   State the theoretical or practical contribution.
   End with a forward-looking statement about implications or next steps.

Core Workflow

Step 1 — Collect Inputs

Before writing, gather:

  • Key results: primary finding with quantitative detail (e.g., "HR 1.43, 95% CI 1.12–1.82")
  • Research question / hypothesis: what was the study trying to answer?
  • Prior literature (if any): papers the user wants to cite, agree with, or contrast
  • Known limitations: study-specific constraints the author wants to acknowledge
  • Tone/depth: brief discussion (3–4 paragraphs) or full discussion (6+ paragraphs)?

If key results are not provided, ask before writing. Do not invent findings.

Step 2 — Draft the Discussion

Write in full paragraphs following the 6-part structure above.

Interpretation rules:

  • State whether results support or refute the original hypothesis
  • For unexpected results, offer 2–3 plausible mechanistic explanations ranked by likelihood
  • Do not introduce new data or results in the Discussion that were not in the Results section
  • Use hedged academic language appropriate to the evidence level

Literature comparison rules:

  • When the user provides specific papers: directly quote or summarize findings and compare
  • When the user does not provide papers: write with placeholder [CITE: study showing similar/contrasting result] rather than inventing citations
  • Never fabricate author names, journals, years, or findings

Limitations rules:

  • Use the format: [Constraint] → [Impact on interpretation] → [How future work could address it]
  • Be honest but proportionate — do not catastrophize minor limitations
  • Do not list a limitation without a mitigation or future direction statement

Step 3 — Draft → Revise Checklist

After drafting, verify:

  • Every key finding from the Results section is explicitly addressed in the Discussion
  • Claims are supported by the user's data or cited literature, not stated as facts
  • Unexpected or null results are acknowledged and interpreted, not ignored
  • No new data or results introduced for the first time in the Discussion
  • Limitations are stated with impact and mitigation, not just listed
  • Hedged language used appropriately ("suggests", "indicates", "may")
  • Conclusion paragraph ties directly back to the original research question
  • No fabricated citations or invented prior studies

Step 4 — Deliver

Provide:

  1. The complete Discussion section draft
  2. A brief note on any placeholders inserted (citations the user needs to fill in)
  3. Any assumptions made (e.g., assumed the study was retrospective based on description)

Hard Rules

  • Never fabricate citations, paper titles, authors, or findings not provided by the user
  • Never introduce new results in the Discussion that were not in the Results
  • Never make clinical recommendations beyond what the evidence explicitly supports
  • If the user has not provided prior literature, use explicit citation placeholders

Citation Placeholder Density Rule

When the user provides no prior literature, use citation placeholders ([CITE: ...]) rather than invented citations. However:

  • Maximum 4 placeholders per 400 words of discussion draft
  • For additional comparison points beyond this limit, add a grouped note at the end of the literature comparison section: [Additional citations needed: the following claims require 2–3 supporting studies — list the types of evidence needed]
  • This prevents placeholder-heavy drafts that read as incomplete rather than as a usable starting point

Discussion Length Calibration

Calibrate discussion length to manuscript type:

  • Brief (3–4 paragraphs, ~300–400 words): short communications, case reports, letters to the editor, pilot studies
  • Standard (5–6 paragraphs, ~500–700 words): original research articles in specialty journals
  • Extended (7+ paragraphs, ~800–1,000 words): high-impact journals, multi-finding studies, studies with substantial prior literature to engage

If the user does not specify depth, infer from the evidence they provide — minimal input → brief; full results with multiple comparators → standard or extended.

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 Discussion Composer AI skill do?

Composes a Discussion around key findings, mechanisms, clinical relevance, and limitations. Use when writing or improving a Discussion section for any biomedical manuscript — including interpreting results, connecting to prior literature, addressing unexpected findings, framing limitations, and writing the conclusion. Also triggers on "write my discussion", "help me discuss my findings", "how do I compare to prior studies", "write the limitations paragraph", or "draft a discussion for my paper".

Why use Discussion Composer on TypingMind?

Because you install it once and use it with any model. Discussion Composer 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 Discussion Composer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/aipoch/medical-research-skills/tree/main/awesome-med-research-skills/Academic%20Writing/discussion-composer. 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 Discussion Composer?

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 Discussion Composer?

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

Is the Discussion Composer AI skill free?

Yes. It is published on GitHub by aipoch under the MIT license. 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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