deepxiv-baseline-table

deepxiv-baseline-table is a skill for Claude Code, Codex from DeepXiv/deepxiv_sdk. It costs 50 tokens per session (1,500 once invoked), scanned A, original, MIT.

A research-paper comparison table built from DeepXiv, a paper-search and reading tool. It lists papers, links, code availability, datasets, benchmark results, and other details needed to compare methods.

In plain words
What is it for?
Use it to survey baselines for a topic, find recent papers, or create a Markdown table showing which methods used which datasets, what scores they reported, and whether their code is open.
Why use it?
It saves you from opening papers one by one and manually collecting inconsistent information. It also focuses reading on experiments and reported results.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to survey baselines for a topic, find recent papers, or create a Markdown table showing which methods used which datasets, what scores they reported, and whether their code is open.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/deepxiv/deepxiv_sdk/deepxiv-baseline-table
Install

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.

Any agent
npx skills add DeepXiv/deepxiv_sdk --skill deepxiv-baseline-table
Clone the repo
git clone --depth 1 https://github.com/DeepXiv/deepxiv_sdk

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for deepxiv-baseline-table

README.md
[![agentmods](https://agentmods.dev/badge/skills/deepxiv/deepxiv_sdk/deepxiv-baseline-table/github.svg)](https://agentmods.dev/skills/deepxiv/deepxiv_sdk/deepxiv-baseline-table)
Your own site
<a href="https://agentmods.dev/skills/deepxiv/deepxiv_sdk/deepxiv-baseline-table"><img src="https://agentmods.dev/badge/skills/deepxiv/deepxiv_sdk/deepxiv-baseline-table/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.

agentmods 80×15 button for deepxiv-baseline-table

Your own site · 80×15
<a href="https://agentmods.dev/skills/deepxiv/deepxiv_sdk/deepxiv-baseline-table"><img src="https://agentmods.dev/badge/skills/deepxiv/deepxiv_sdk/deepxiv-baseline-table.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 50 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,500 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00050 $0.01500
Opus 5 $0.00025 $0.00750
Sonnet 5 $0.00010 $0.00300
Haiku 4.5 $0.00005 $0.00150

Measured 10d ago against content hash 12231fed9b33, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

deepxiv-baseline-table 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 10d 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.

skills/deepxiv-baseline-table/SKILL.md · 236 lines

How it starts

The opening of the file, as written. The whole thing — 236 lines — stays where its author put it; the contents beside it link to each section on GitHub.

DeepXiv Baseline Table

Use this skill when the user wants to map a topic into a comparison table, baseline survey, benchmark roundup, or "what papers evaluated on which datasets with what scores and whether code is open".

Typical requests:

  • "Find recent baseline papers on agentic memory"
  • "What papers in the last month evaluated on dataset X?"
  • "Make me a markdown table of methods, datasets, and scores"

Goal

Turn a topic search into a structured markdown table:

  1. Search recent papers with deepxiv search
  2. Brief all candidates with deepxiv paper <id> --brief
  3. Keep the relevant papers, prioritizing papers with GitHub/code
  4. Inspect promising papers with deepxiv paper <id> --head
  5. Read experiment-related sections with deepxiv paper <id> --section ...
  6. Extract datasets, evaluation setup, and reported scores
  7. Write a markdown table summarizing the baselines

Default Workflow

1. Search by topic and date range

Use a broad search first.

deepxiv search "agentic memory" --date-from 2026-03-01 --limit 100 --format json

Default heuristics:

  • Use the user’s exact topic phrase first
  • Keep --limit high enough to avoid missing relevant papers
  • If results are noisy, refine the query with close variants

Examples:

deepxiv search "agentic memory" --date-from 2026-03-01 --limit 100 --format json
deepxiv search "memory agents long-horizon" --date-from 2026-03-01 --limit 100 --format json
deepxiv search "agent memory benchmark" --date-from 2026-03-01 --limit 100 --format json

2. Brief all candidates

For each arXiv ID, fetch:

deepxiv paper <arxiv_id> --brief

Capture:

  • title
  • arXiv ID
  • publish date
  • TLDR
  • keywords
  • GitHub URL
  • PDF/source URL

This is the screening step. Do not read full sections yet.

3. Filter and prioritize

Keep papers that are actually about the topic, not just adjacent terms.

Prioritize:

  • papers directly centered on the topic
  • empirical papers over purely conceptual ones
  • papers with GitHub/code
  • benchmark or comparison papers
  • papers with clear experiment sections

Read the full file on GitHub · 236 lines

Changes

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.

  1. 10d ago First seen · 236 lines · 50 tokens per session scan A 12231fed9b33

Subscribe to this mod's changes

deepxiv-baseline-table is a skill published in the GitHub repository DeepXiv/deepxiv_sdk (781 stars, last pushed 5d ago), licensed MIT. It adds 50 tokens to every session and 1,500 once invoked, about $0.0003 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.

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