Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add jxtse/scientific-research-skills --skill paper-readinggit clone --depth 1 https://github.com/jxtse/scientific-research-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/jxtse/scientific-research-skills/paper-reading)<a href="https://agentmods.dev/skills/jxtse/scientific-research-skills/paper-reading"><img src="https://agentmods.dev/badge/skills/jxtse/scientific-research-skills/paper-reading/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/jxtse/scientific-research-skills/paper-reading"><img src="https://agentmods.dev/badge/skills/jxtse/scientific-research-skills/paper-reading.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00154 | $0.01003 |
| Opus 5 | $0.00077 | $0.00502 |
| Sonnet 5 | $0.00031 | $0.00201 |
| Haiku 4.5 | $0.00015 | $0.00100 |
Grade A, and why
paper-reading scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 121 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Paper Reading
Structured workflow for reading academic papers efficiently.
When to Use
- User shares an arXiv link or PDF and asks to read/summarize it
- User asks about a specific paper's contributions, methods, or results
- User wants a reading digest for their records
- User asks to compare a paper against related work
Reading Levels
Level 1: Quick Skim (2 min)
When: User just wants to know if a paper is worth reading
Output:
- Paper title, authors, venue, year
- One-paragraph summary (what problem, what method, what result)
- Key contribution in one sentence
- Relevance assessment to user's work
- Recommendation: Read / Skip / Skim only
Level 2: Standard Read (10 min)
When: User wants to understand the paper's approach
Output:
- Problem: What gap does this address?
- Method: How do they solve it? (with key technical details)
- Key innovation: What's genuinely new vs. incremental?
- Results: Main numbers + comparison to baselines
- Limitations: What they don't do, acknowledged or not
- Connections: How does this relate to user's active projects?
Level 3: Deep Analysis (30 min)
When: User is seriously considering building on this paper
Output:
- Everything from Level 2, plus:
- Detailed methodology: Step-by-step technical walkthrough
- Reproducibility assessment: Can you implement this from the paper alone?
- Experimental design critique: Are the baselines fair? Metrics appropriate?
- Hidden assumptions: What are they not saying?
- Extension opportunities: How could this be improved or adapted?
- Key equations/algorithms: Extracted and explained
- Figure analysis: What do the key figures actually show?
Workflow
Step 1: Obtain Paper
arXiv link → Download PDF, extract text
PDF file → Extract text directly
Paper title → Search Semantic Scholar → get arXiv link → download
Zotero item → Get from local library
Step 2: Read at Requested Level
Follow the appropriate level template above. When in doubt, start with Level 2.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 121 lines · 154 tokens per session scan A e790c723ee35
paper-reading is a skill published in the GitHub repository jxtse/scientific-research-skills (68 stars, last pushed 3mo ago), licensed MIT. It adds 154 tokens to every session and 1,003 once invoked, about $0.0008 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
keshav-three-pass
Tactic: Read one paper by Keshav's three-pass method — a shallow skim, a contribution-grasping full read, then a deep virtual re-implementation. Use when the goal is understanding a paper rather than extracting a fixed schema.
challenge-operation
Non-threatening 'Why?' questioning of current practices (de Bono Challenge).
first-pass-skim
Keshav's first pass over one paper — a 5-10 minute skim of title, abstract, headings, figures, and conclusion only, producing skim notes and a read-deeper judgment. Use this as the first step whenever a paper is being read via the Keshav three-pass method; always precedes second-pass-grasp and never reads section…
qalmri-worksheet
Tactic: Fill a six-slot QALMRI worksheet for one paper: Question, Alternatives, Logic, Method, Results, and Inference. Use for a structured reading worksheet rather than a graded evaluation.
analogy-chain
Chain analogies to deeper levels (3-5 layers). Each layer reveals new aspects and insights not visible at the surface.
challenge-questioning
Non-threatening 'Why?' questioning of current practices to reveal historical accidents vs. genuine constraints.