Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/raja21068/AutoResearchnpx agentmods add skills/raja21068/autoresearch/openalexWrote 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/raja21068/autoresearch/openalex)<a href="https://agentmods.dev/skills/raja21068/autoresearch/openalex"><img src="https://agentmods.dev/badge/skills/raja21068/autoresearch/openalex/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/raja21068/autoresearch/openalex"><img src="https://agentmods.dev/badge/skills/raja21068/autoresearch/openalex.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 | $0.00055 | $0.02222 |
| Opus 5 | $0.00028 | $0.01111 |
| Sonnet 5 | $0.00011 | $0.00444 |
| Haiku 4.5 | $0.00006 | $0.00222 |
Grade B, and why
openalex scanned grade B with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Enumerates other installed skillsmediumAgent snooping
Other skills' SKILL.md files reveal prompts, capabilities and secrets that should be invisible to peers.
[ -z "$SCRIPT" ] && SCRIPT=$(find ~/.claude/skills/openalex/ -name "openalex_fetch.py" 2>/dev/null | head -1) This is a copy
89% identical to openalex — 50 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 218 lines — stays where its author put it; the contents beside it link to each section on GitHub.
OpenAlex Academic Search
Search query: $ARGUMENTS
Role & Positioning
This skill uses OpenAlex as a comprehensive open academic graph source:
| Skill | Source | Best for |
|---|---|---|
/arxiv |
arXiv API | Latest preprints, cutting-edge unrefereed work |
/semantic-scholar |
Semantic Scholar API | Published venue papers (IEEE, ACM, Springer) with citation counts |
/openalex |
OpenAlex API | Open citation graph, institutional affiliations, funding data, comprehensive metadata |
/deepxiv |
DeepXiv CLI | Layered reading: search, brief, section map, section reads |
/exa-search |
Exa API | Broad web search: blogs, docs, news, companies, research papers |
/gemini-search |
Gemini MCP / CLI | AI-powered broad literature discovery |
Use OpenAlex when you want:
- Open citation data — fully open citation graph (no API key required for basic use)
- Institutional affiliations — author institutions and collaborations
- Funding information — NSF, NIH, and other funding sources
- Comprehensive metadata — topics, keywords, abstract, open access status
- Cross-database coverage — indexes 250M+ works from multiple sources
Constants
- MAX_RESULTS = 10 — Default number of results. Override with
— max: 20. - DEFAULT_SORT = relevance — Sort by relevance. Override with
— sort: citationsor— sort: date. - FETCH_SCRIPT —
tools/openalex_fetch.pyrelative to the project root.
Overrides (append to arguments):
/openalex "topic" — max: 20— return up to 20 results/openalex "topic" — year: 2023-— papers from 2023 onward/openalex "topic" — year: 2020-2023— papers from 2020 to 2023/openalex "topic" — type: article— only journal articles/openalex "topic" — type: preprint— only preprints/openalex "topic" — open-access— only open access papers/openalex "topic" — min-citations: 50— minimum 50 citations/openalex "topic" — sort: citations— sort by citation count (descending)/openalex "topic" — sort: date— sort by publication date (newest first)
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.
- 7d ago First seen · 218 lines · 55 tokens per session scan B 888b2d70fb4e
openalex is a skill published in the GitHub repository raja21068/AutoResearch (2 stars, last pushed 3mo ago), licensed MIT. It adds 55 tokens to every session and 2,222 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it B with 1 finding (enumerates other installed skills). It is 89% identical to openalex, differing in 50 lines, and is treated as a copy.
Other skills, from other repositories
content-refinement-agent
Step 5 of the PaperOrchestra pipeline (arXiv:2604.05018). Iteratively refine drafts/paper.tex by simulating peer review and applying targeted revisions, with strict accept/revert halt rules, deterministic 0-100 decision bands (Accept/Minor/Major/Reject) that drive a target-met early stop, and a Devil's Advocate…
section-writing-agent
Step 4 of the PaperOrchestra pipeline (arXiv:2604.05018). ONE single multimodal LLM call that drafts the remaining paper sections (Abstract, Methodology, Experiments, Conclusion), extracts numeric values from experimentallog.md into LaTeX booktabs tables, splices the generated figures from Step 2, and merges…
outline-agent
Step 1 of the PaperOrchestra pipeline (arXiv:2604.05018). Convert (idea.md, experimentallog.md, template.tex, conferenceguidelines.md) into a strict JSON outline containing a plotting plan, literature search plan (Intro + Related Work), and section-level writing plan with citation hints. TRIGGER when the orchestrator…
paper-autoraters
Run the four paper-quality autoraters from PaperOrchestra (arXiv:2604.05018, App. F.3) — Citation F1 (P0/P1 partition + Precision/Recall/F1), Literature Review Quality (6-axis 0-100 with anti-inflation rules), SxS Overall Paper Quality (side-by-side), and SxS Literature Review Quality (side-by-side). TRIGGER when the…
plotting-agent
Step 2 of the PaperOrchestra pipeline (arXiv:2604.05018). Execute the visualization plan from outline.json — render plots and conceptual diagrams from experimentallog.md and idea.md, optionally refine via VLM critique loop, and produce context-aware captions. Runs in parallel with the literature-review-agent. TRIGGER…
paper-writing-bench
Reverse-engineer raw materials (Sparse idea, Dense idea, experimental log) from an existing AI research paper to build a benchmark case for evaluating paper-writing pipelines. Replicates the PaperWritingBench dataset construction procedure from arXiv:2604.05018 §3 / App. C. TRIGGER when the user asks to "build a…