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 DeepXiv/deepxiv_sdk --skill deepxiv-baseline-tablegit clone --depth 1 https://github.com/DeepXiv/deepxiv_sdkWrote 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/deepxiv/deepxiv_sdk/deepxiv-baseline-table)<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.
<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>- NVIDIA SkillSpector pass
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 | $0.00050 | $0.01500 |
| Opus 5 | $0.00025 | $0.00750 |
| Sonnet 5 | $0.00010 | $0.00300 |
| Haiku 4.5 | $0.00005 | $0.00150 |
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.
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:
- Search recent papers with
deepxiv search - Brief all candidates with
deepxiv paper <id> --brief - Keep the relevant papers, prioritizing papers with GitHub/code
- Inspect promising papers with
deepxiv paper <id> --head - Read experiment-related sections with
deepxiv paper <id> --section ... - Extract datasets, evaluation setup, and reported scores
- 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
--limithigh 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
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.
- 10d ago First seen · 236 lines · 50 tokens per session scan A 12231fed9b33
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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