Research Summarizer

Community

oxbshw

research-summarizer

Use when the user asks for a sourced briefing on a topic that spans multiple web sources and requires citations.

Overview

Publisher	oxbshw
Repository	`LLM-Agents-Ecosystem-Handbook`
Skill name	`research-summarizer`
Stars	552
Forks	93
Bundled files	1
License	MIT
Links	Skill source Repository

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 oxbshw on GitHub. Read the source before you install it.

Installation

Install the Research Summarizer AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/oxbshw/LLM-Agents-Ecosystem-Handbook/tree/main/skills/examples/research-summarizer
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/oxbshw/LLM-Agents-Ecosystem-Handbook/tree/main/skills/examples/research-summarizer

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

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/oxbshw/LLM-Agents-Ecosystem-Handbook.git /tmp/LLM-Agents-Ecosystem-Handbook
mkdir -p .claude/skills
cp -r /tmp/LLM-Agents-Ecosystem-Handbook/skills/examples/research-summarizer .claude/skills/research-summarizer

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Research Summarizer 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 Summarizer 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 Summarizer 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 Summarizer

When to use

User asks "research X" / "what's the state of Y" / "summarize the literature on Z"
The answer requires multiple sources, not one
The user expects citations

When NOT to use

Single-source summarization (use a plain summarize tool)
Code search (use repo-auditor or grep)
Opinion / recommendation requests (this skill produces neutral briefings)

Inputs

Name	Type	Required	Notes
`topic`	string	yes	the question to research
`depth`	"shallow" \| "deep"	no	default "shallow" (5–7 sources); "deep" → 10–15
`audience`	string	no	shapes vocabulary level (e.g., "executive", "engineer")

Outputs

A Markdown file briefing.md following the structure in references/report-template.md.

Workflow

Plan: produce 3–7 sub-questions covering breadth and depth
Search + fetch: for each sub-question, run web_search then fetch_url for top 2–3 hits
Cluster: group findings by claim; load references/clustering-rules.md
Draft: fill references/report-template.md — every claim needs a [n] citation
Validate: every citation resolves; no domain dominates (≥ 3 distinct domains for shallow, ≥ 5 for deep)
Self-review against success criteria below

References

references/report-template.md — the output structure
(add clustering-rules.md, domain-allowlist.md as the skill matures)

Success criteria

≥ 5 sources for shallow, ≥ 10 for deep
≥ 3 distinct domains
0 unsourced claims (or all flagged (unsourced))
Open Questions section populated

Failure modes

Fewer sources available than required → return partial result, flag at top
All sources from one domain → reject, retry plan with broader scope
Network error → fail loudly, don't fabricate

Examples

See references/examples/ (TBD) for one shallow and one deep run

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

references/report-template.md1 KB

Frequently asked questions

What does the Research Summarizer AI skill do?

Use when the user asks for a sourced briefing on a topic that spans multiple web sources and requires citations.

Why use Research Summarizer on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/oxbshw/LLM-Agents-Ecosystem-Handbook/tree/main/skills/examples/research-summarizer. 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 Summarizer?

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 Summarizer?

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

Is the Research Summarizer AI skill free?

Yes. It is published on GitHub by oxbshw 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Research Summarizer

When to use

When NOT to use

Inputs

Outputs

Workflow

References

Success criteria

Failure modes

Examples

Bundled files

Frequently asked questions

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