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Counterargument

Community
richard-kim-79
counterargument

Generates rigorous devil's advocate critiques from methodological, theoretical, and practical perspectives. Use when the user asks to challenge their own work, find weaknesses, stress-test assumptions, anticipate reviewer objections, or argue against their research claims. Run BEFORE writing a draft for stronger papers.

Overview

Publisherrichard-kim-79
Repositoryarchora-skills
Skill namecounterargument
Stars
50
Forks
12
Bundled files
Instructions only
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.

  • Self-contained

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

  • Open source

    Published by richard-kim-79 on GitHub. Read the source before you install it.

Installation

Install the Counterargument 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/richard-kim-79/archora-skills/tree/main/skills/counterargument
  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/richard-kim-79/archora-skills.git /tmp/archora-skills
mkdir -p .claude/skills
cp -r /tmp/archora-skills/skills/counterargument .claude/skills/counterargument
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Counterargument 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 Counterargument 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 Counterargument 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.

Counterargument Generation

Generate adversarial critiques to stress-test research claims before writing or submitting.

Perspectives

PerspectiveIconFocus
Methodological🔬Study design, measurement validity, statistical approach, reproducibility
Theoretical📚Conceptual framework, alternative explanations, literature gaps
Practical⚙️Feasibility, generalizability, real-world applicability, ethical concerns

Workflow

  1. Identify the core claims — extract the main hypotheses or arguments
  2. Generate 4–6 critiques — at least one per perspective, more for weaker claims
  3. Rate severity — HIGH (fatal flaw), MEDIUM (significant concern), LOW (minor point)
  4. Provide rebuttal strategy — for each critique, suggest how to respond or strengthen the research

Output Format

markdown
# ⚔️ Counterargument Analysis

## Summary
[2–3 sentences: overall assessment of the research's vulnerability to critique]

## Counterarguments

### 📚 Theoretical Critique — [SHORT TITLE]

**Challenged Claim:** [Exact claim being challenged]

**Critique:** [Specific, evidence-grounded critique. Reference timescales, measurement limits,
alternative frameworks, or published contradictory evidence where possible.]

**Rebuttal Strategy:** [Concrete suggestion: what experiment, analysis, or argument would
address this critique]

---

### 🔬 Methodological Critique — [SHORT TITLE]
...

Quality criteria

  • Specific: Reference exact claims, timescales, measurements — not vague "this is uncertain"
  • Grounded: Connect to actual limitations in the content, not generic criticism
  • Expert-level: Write as a senior reviewer at a top journal would
  • Constructive: Every critique ends with a path forward (rebuttal strategy)

Examples of strong vs weak critiques

Weak: "The sample size might be too small."

Strong: "The proposed sample of n=15 animals achieves 80% power only for effect sizes of η²≥0.25. Given the typical effect sizes in STDP studies (η²=0.10–0.15, e.g., Bi & Poo 1998), this is underpowered. A power analysis targeting η²=0.12 would require n=38 per condition."

Frequently asked questions

What does the Counterargument AI skill do?

Generates rigorous devil's advocate critiques from methodological, theoretical, and practical perspectives. Use when the user asks to challenge their own work, find weaknesses, stress-test assumptions, anticipate reviewer objections, or argue against their research claims. Run BEFORE writing a draft for stronger papers.

Why use Counterargument on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/richard-kim-79/archora-skills/tree/main/skills/counterargument. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Counterargument?

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 Counterargument?

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

Is the Counterargument AI skill free?

Yes. It is published on GitHub by richard-kim-79 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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