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Research Brief Skill

Community
mingchen666
research-brief-skill

Lightweight source-backed research and synthesis for normal Reviva agents. Use when the user asks to summarize materials, compare sources, verify claims, collect evidence, create a short research brief, prepare slide/document inputs, or produce a Markdown/HTML brief without running the full deep-researcher pipeline.

Overview

Publishermingchen666
RepositoryReviva
Skill nameresearch-brief-skill
Stars
293
Forks
31
Bundled files
1
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 mingchen666 on GitHub. Read the source before you install it.

Installation

Install the Research Brief Skill 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/mingchen666/Reviva/tree/main/electron/builtin-assets/skills/research-brief-skill
  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/mingchen666/Reviva.git /tmp/Reviva
mkdir -p .claude/skills
cp -r /tmp/Reviva/electron/builtin-assets/skills/research-brief-skill .claude/skills/research-brief-skill
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Research Brief Skill 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 Brief Skill 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 Brief Skill 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 Brief Skill

Use this skill to produce a reliable, compact research brief from local files, knowledge base hits, and optional web search. It is for normal chat agents and task agents that need evidence-backed synthesis without the cost of the full deep-research workflow.

When To Use

Use this skill for:

  • summarizing uploaded documents into a cited brief;
  • comparing several sources, documents, policies, products, papers, notes, or lesson materials;
  • checking whether a claim, number, requirement, exam detail, curriculum point, or public fact has enough support;
  • preparing source-backed content for PPT, lesson plans, review notes, office documents, reports, or HTML pages;
  • creating a concise Markdown report and optional HTML display page.

Do not use this skill when the user explicitly asks for a long-form deep research report, multi-round investigation, full literature review, or exhaustive market/industry research. Route those tasks to the built-in deep-researcher agent with deep-research.

Core Principle

Prefer trustworthy evidence over volume. A short answer with clear source boundaries is better than a long report that hides uncertainty.

Always distinguish:

  • User materials: files or attachments supplied in the current task.
  • Knowledge base: cloud KB search results selected by the user.
  • Web sources: search results from web_search_bing, mcp:exa, or another available search tool.
  • Model knowledge: general background that is not directly sourced; mark it as such when used.

Source Intake

Handle files by type:

TypeToolRule
.md, .txt, .csv, .json, source snippetsfile_readRead directly, in chunks when large.
.docx, .pptx, .xlsxdocument_readUse overview first, then text/targeted reads. Never use file_read on Office binaries.
.pdfdocument_readUse overview first, then page chunks. If unavailable, mark the PDF as not parsed; do not install dependencies.
Cloud KB docskb_searchSearch the KB; do not pretend cloud KB docs have local paths.
Web pages/search resultsweb_search_bing / mcp:exaOptional. Use only available tools and respect the parent agent's web-search setting.

Do not auto-install Python packages, ASR models, browsers, scraping tools, or MCP servers.

Workflow

  1. Clarify target briefly

    • Identify the topic, output purpose, audience, deadline/currentness needs, and whether web search is allowed.
    • If the task is understandable, proceed without asking extra questions.
  2. Build an evidence map

    • Read local/user materials first.
    • Search the selected knowledge base when the user has selected KB materials or asks to use workspace knowledge.
    • Use web search only when the user asks for current/public verification, the task requires facts not present in local materials, or the parent agent has already chosen web research.
    • Keep search light: normally 2-5 focused queries; stop when 3-6 useful sources cover the question.
  3. Assess source quality

    • Prefer official, primary, recent, specific, and reproducible sources.
    • Treat blogs, forums, social posts, AI-generated pages, and unsourced summaries as weak evidence unless the task is about public opinion.
    • Mark outdated, inaccessible, paywalled, conflicting, or low-specificity sources.
  4. Synthesize

    • Extract claims, facts, examples, definitions, numbers, and caveats.
    • Compare sources instead of merging conflicts silently.
    • Explain what is well-supported, what is uncertain, and what still needs user confirmation.
  5. Choose output

    • Default: reply inline with a concise brief and source table.
    • If the user asks to save, share, hand off to PPT/DOCX, or says "报告/简报/展示页/HTML/Markdown/文件", write files.
    • For file output, generate Markdown first, then an HTML display page when useful.

Markdown Brief

Use this structure unless the parent agent needs a different format:

markdown
# [简报标题]

## 结论摘要
- [核心结论 1] [来源/资料标记]
- [核心结论 2] [来源/资料标记]

## 依据梳理
| 要点 | 依据 | 来源类型 | 可靠性 | 注意事项 |
| --- | --- | --- | --- | --- |

## 分析

## 不确定点与建议补充

## 来源表
| 编号 | 标题/文件 | 机构/作者 | 日期 | 类型 | 链接/路径 |
| --- | --- | --- | --- | --- | --- |

For education and learning tasks, add grade/subject/curriculum assumptions when relevant. For office tasks, add action-oriented recommendations and next steps.

HTML Display Page

Generate HTML when:

  • the user explicitly asks for HTML, webpage, visualization, display, shareable brief, or preview;
  • the parent task is preparing a presentation, teaching material, briefing page, or report artifact;
  • a compact visual summary would materially help the user review the evidence.

HTML is a display layer, not the full research archive. Keep it concise and self-contained.

HTML requirements:

  • Single .html file, all CSS/JS inline.
  • No CDN, external fonts, remote images, external scripts, trackers, or network requests.
  • System font stack only.
  • Responsive layout with no horizontal overflow on mobile.
  • Include: title, 3-6 key findings, evidence matrix, limitations, and source table.
  • Use simple CSS cards/tables; add inline SVG or CSS charts only when the data is real and sourced.
  • Do not hide uncertainty behind polished visuals.

Suggested HTML sections:

text
Header: title, purpose, date, source scope
Key findings: 3-6 cards with source marks
Evidence matrix: claim / evidence / reliability / caveat
Implications or next steps
Limitations
Sources

Reliability Rules

  • Do not fabricate URLs, document titles, page numbers, official wording, dates, authors, or statistics.
  • If search is unavailable or GitHub/official sites are inaccessible, say so and continue with local materials, KB, or clearly marked general knowledge.
  • If local documents and web sources conflict, surface the conflict and recommend which source should be treated as authoritative.
  • If the task needs current facts but web search is unavailable, label the answer as "未联网核验".
  • Avoid over-searching. For normal agents, this skill should stay lightweight.

Handoff Rules

  • For PPT or lesson slides: hand the synthesized source notes to pptx-deck-skill, html-ppt-skill, ai-animation-skill, or officecli-skills.
  • For teacher-specific resource gathering, coordinate with teaching-resource-research-skill when the task is about curriculum standards, teaching cases, public classroom resources, or educational media.
  • For final review/exam tasks, hand concepts, source-backed scope, and uncertain exam claims back to review-plan, knowledge-organize, practice-quiz, or exam-prep.
  • For long, high-stakes research, escalate to deep-researcher.

Final Response

When files are produced, list:

  • Markdown path
  • HTML path, if generated
  • 2-4 sentence summary
  • important limitations or sources that could not be verified

Do not include temporary paths.

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 Brief Skill AI skill do?

Lightweight source-backed research and synthesis for normal Reviva agents. Use when the user asks to summarize materials, compare sources, verify claims, collect evidence, create a short research brief, prepare slide/document inputs, or produce a Markdown/HTML brief without running the full deep-researcher pipeline.

Why use Research Brief Skill on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mingchen666/Reviva/tree/main/electron/builtin-assets/skills/research-brief-skill. 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 Brief Skill?

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 Brief Skill?

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

Is the Research Brief Skill AI skill free?

It is published on GitHub by mingchen666. 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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