Paper Summarizer
SkillAI & modelsAgents 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.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Paper Summarizer skill
What this skill tells your AI
The instructions your AI receives, as published by waybarrios/opencode-power-pack in skills/paper-summarizer/SKILL.md and read by ahel’s review.
Extract actionable insights from academic and technical papers.
Summary Format
# 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 against the project's own research notes. 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 the current stack: [Specific applicability to Rust/TS/Python work]
- For current 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
- Abstract — Get the overview
- Introduction (last paragraph) — Usually states the contribution
- Figures and tables — Often convey key results
- Conclusion — Summary of findings and limitations
- 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
| Factor | Assessment |
|---|---|
| Peer review status | Published / Preprint / Workshop |
| Replication | Replicated / Single study / Theoretical |
| Sample size | Adequate / Small / N/A |
| Methodology rigor | Strong / Moderate / Weak |
| Author credibility | Established / New / Anonymous |
| Conflicts of interest | None apparent / Funded by [X] / Vendor paper |
Signals
- GitHub stars
- 501
- Forks
- 40
- Last commit
- Sep 2026
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paper-summarizer- Source
- github.com/waybarrios/opencode-power-pack