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Paper Summarizer

Community
Firstp1ck
paper-summarizer

Agents should invoke this skill for academic or technical papers, arXiv/PubMed/IEEE/ACM links, PDFs, methodology review, limitations, practical implications, or extracting findings for engineering decisions.

Overview

PublisherFirstp1ck
Repositorypi-coding-agent-forge
Skill namepaper-summarizer
Stars
93
Forks
22
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 Firstp1ck on GitHub. Read the source before you install it.

Installation

Install the Paper Summarizer 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/Firstp1ck/pi-coding-agent-forge/tree/main/pi-skill-paper-summarizer/skills/paper-summarizer
  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/Firstp1ck/pi-coding-agent-forge.git /tmp/pi-coding-agent-forge
mkdir -p .claude/skills
cp -r /tmp/pi-coding-agent-forge/pi-skill-paper-summarizer/skills/paper-summarizer .claude/skills/paper-summarizer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Paper 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 Paper 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 Paper 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.

Paper Summarizer

Extract actionable insights from academic and technical papers.

Summary Format

markdown
# Paper Summary: <Paper Title>

**Authors:** [Author list]
**Published:** [Journal/Conference, Date]
**Link:** [URL]
**Quality:** Peer-reviewed / Preprint / Workshop paper

## TL;DR

[1-2 sentence summary of the key contribution]

## Problem

[What problem does this paper address? Why does it matter?]

## Approach

[Methodology in plain language — what did they do?]

## Key Findings

Anchor each finding to the paper so readers can verify. Use **§** for sections, **Fig.** / **Table** when the evidence is visual or tabular.

1. **[Finding 1]:** [Description with key metrics/numbers] — *Evidence:* §[N] [section name]; [Fig. X / Table Y if applicable]
2. **[Finding 2]:** [Description] — *Evidence:* §[N] …
3. **[Finding 3]:** [Description] — *Evidence:* §[N] …

## Claim–evidence map

| # | Claim (one line) | Where in paper | Type |
|---|------------------|----------------|------|
| 1 | [Claim] | §3.2 Results, Table 2 | Empirical |
| 2 | [Claim] | §1 Introduction | Stated goal |
| 3 | [Claim] | Fig. 4 | Qualitative |

Use this table for **citation audit** alignment (`workspace-researcher/AGENTS.md`). If the PDF has no section numbers, use **page** or **heading text** instead of §.

## Practical Implications

[What does this mean for practitioners? How can we use these findings?]
- For our stack: [Specific applicability to Rust/TS/Python work]
- For our projects: [How this might inform current work]

## Limitations

- [Limitation 1: e.g., small sample size, specific domain]
- [Limitation 2: e.g., not replicated, theoretical only]

## Related Work

- [Paper 1] — [How it relates]
- [Paper 2] — [How it relates]

## Verdict

**Reliability:** High / Medium / Low
**Relevance to us:** High / Medium / Low
**Action:** Apply directly / Consider for future / Interesting but not actionable

Process

Step 1 — Access the Paper

  • Check arXiv, PubMed, Google Scholar for open-access versions
  • If behind a paywall, note this and work with the abstract and any available supplementary material
  • Check for author's personal page (often has preprints)

Step 2 — Read Strategically

  1. Abstract — Get the overview
  2. Introduction (last paragraph) — Usually states the contribution
  3. Figures and tables — Often convey key results
  4. Conclusion — Summary of findings and limitations
  5. Methodology — If the findings are relevant enough to dig deeper

Step 3 — Extract claims with locations

Before paraphrasing implications, list atomic claims the paper makes (results, bounds, contributions). For each: section / figure / table reference (or page). Prefer primary evidence (results section) over abstract-only restatement.

Step 4 — Extract Practical Value

The most important question: "What can we do differently because of this paper?"

  • If the answer is "nothing" — still summarize, but note low actionability
  • If the answer is specific — tie each implication to a row in Claim–evidence map where possible

Step 5 — Assess Reliability

FactorAssessment
Peer review statusPublished / Preprint / Workshop
ReplicationReplicated / Single study / Theoretical
Sample sizeAdequate / Small / N/A
Methodology rigorStrong / Moderate / Weak
Author credibilityEstablished / New / Anonymous
Conflicts of interestNone apparent / Funded by [X] / Vendor paper

Scout skill — Academic paper summarization and insight extraction · P1 claim–evidence map: 2026-04-10

Frequently asked questions

What does the Paper Summarizer AI skill do?

Agents should invoke this skill for academic or technical papers, arXiv/PubMed/IEEE/ACM links, PDFs, methodology review, limitations, practical implications, or extracting findings for engineering decisions.

Why use Paper Summarizer on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Firstp1ck/pi-coding-agent-forge/tree/main/pi-skill-paper-summarizer/skills/paper-summarizer. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Paper 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 Paper Summarizer?

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

Is the Paper Summarizer AI skill free?

Yes. It is published on GitHub by Firstp1ck 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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