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Research

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daveebbelaar
research

Online research for a macOS app before building it, run as parallel researcher subagents, saved to docs/research/. Use when the user asks what to build, which features matter, how the best apps in a category work, how a macOS API or integration works, or wants a feature comparison.

Overview

Publisherdaveebbelaar
Repositorybama
Skill nameresearch
Stars
952
Forks
136
Bundled files
1
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.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

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

Installation

Install the Research 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/daveebbelaar/bama/tree/main/skills/plan/research
  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/daveebbelaar/bama.git /tmp/bama
mkdir -p .claude/skills
cp -r /tmp/bama/skills/plan/research .claude/skills/research
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Research 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 Research 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 Research 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.

Research

Research answers three questions before code exists: what the best apps in the category do, what users love and hate about them, and how macOS lets you build it. The output feeds app-brief and user-stories.

1. Frame

Write the question in one line, and the decision it informs ("which features go in version one", "Carbon hotkeys or an event tap"). If the user gave only a category, the question is "what makes the best app on the Mac".

2. Fan out

Start one subagent per track, in parallel, with the brief below. Pick the tracks that fit. Four is typical. Market and Reviews always run. Reviews covers many apps: give each subagent 3 or 4 apps, or the 600 words run out.

TrackFinds
MarketThe 5 to 8 best apps in the category, their price, platform, and headline features
ReviewsPer app: what people like and what they dislike, from App Store and Mac App Store reviews, Reddit, Hacker News, forums, and "switched from X" posts
FeaturesThe full feature list of the 2 or 3 leaders, from docs, changelogs, and help pages
DesignScreens, interaction details, and keyboard shortcuts of the leaders
PlatformThe macOS APIs, permissions, and limits each feature needs, and what other apps on this Mac already use (global shortcuts, URL schemes, bundle ids)

Subagent brief (fill in the brackets):

text
Research [track] for a native macOS app that [one-line idea].
Question: [the question]. Decision it informs: [decision].
Search the web. Prefer primary sources: official docs, changelogs, Apple
developer docs, WWDC sessions, the app's own help pages. Use reviews and
forums for opinions only. If a source is blocked, say so and use another
(Hacker News, App Store reviews, Mac forums). Do not write any files.
Tag every claim: [V] verified in a source (give the URL), [K] from your
own knowledge, [U] unverified, must be tested.
Return at most 600 words: a findings list, a table where it helps, and
the 3 things that matter most for the decision. No intro, no summary.

3. Merge

Write one file per question: docs/research/<topic>.md.

markdown
# <Topic>

Research for <AppName>. Done <YYYY-MM-DD>.
[V] verified in a source, [K] from knowledge, [U] unverified, test first.

## Findings
- ... [V](url)

## Likes and dislikes
| App | People like | People dislike | Sources |
| --- | --- | --- | --- |
| App A | <3 to 5 points, most mentioned first> | <3 to 5 points> | <urls> |

Patterns: <likes that repeat across apps (must have), dislikes that repeat
(our chance to do better)>

## Feature matrix
| Feature | App A | App B | App C | Version one? |

## Decisions
1. <decision>, because <finding>. Planned test: <test, for a [U] finding>.

## Open questions
- <[U] claim and how to test it>

Keep the feature matrix: user-stories turns its "Version one?" column into stories. Every "People like" point maps to a decision or to an "accepted gap" line in the brief, so nothing loved is dropped by accident. Check whether a system store (EventKit for reminders and calendars, CloudKit) gives sync and phone capture for free. Turn each [U] that a decision depends on into a test before you trust it (a probe script, a self-test step, or a preview field in the reference app).

4. Hand off

Call the Skill tool with clone if one app stands out as the reference and you can open it. If it is not installed, or computer use and a browser are not available, link its public help pages and screenshots in the research file instead. Then call the Skill tool with app-brief.

Done when every track returned, the file has a likes and dislikes table for every app in the market list, a feature matrix, and a decision list, and every decision cites a [V] or a planned test.

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 Research AI skill do?

Online research for a macOS app before building it, run as parallel researcher subagents, saved to docs/research/. Use when the user asks what to build, which features matter, how the best apps in a category work, how a macOS API or integration works, or wants a feature comparison.

Why use Research on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/daveebbelaar/bama/tree/main/skills/plan/research. 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 Research?

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 Research?

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

Is the Research AI skill free?

Yes. It is published on GitHub by daveebbelaar 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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