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 TobiasBlask/open-paper-machine --skill literature-enginegit clone --depth 1 https://github.com/TobiasBlask/open-paper-machineWrote 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/tobiasblask/open-paper-machine/literature-engine)<a href="https://agentmods.dev/skills/tobiasblask/open-paper-machine/literature-engine"><img src="https://agentmods.dev/badge/skills/tobiasblask/open-paper-machine/literature-engine/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/tobiasblask/open-paper-machine/literature-engine"><img src="https://agentmods.dev/badge/skills/tobiasblask/open-paper-machine/literature-engine.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 | $0.00093 | $0.03124 |
| Opus 5 | $0.00046 | $0.01562 |
| Sonnet 5 | $0.00019 | $0.00625 |
| Haiku 4.5 | $0.00009 | $0.00312 |
Grade A, and why
literature-engine 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 — 347 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Orchestration Log: When this skill is activated, append a log entry to
outputs/orchestration_log.md:### Skill Activation: Literature Engine **Timestamp:** [current date/time] **Actor:** AI Agent (literature-engine) **Input:** [brief description of the search/synthesis request] **Output:** [brief description of results — e.g., "47 papers found across 4 databases, deduplicated to 38"]
Literature Engine
Data Source Priority — STRICT
- Academic APIs via
scripts/academic_search.py— ALWAYS first- Semantic Scholar (200M+ papers): citation graphs, TLDR, snowballing
- OpenAlex (474M+ works): broadest coverage, German publications, CC0
- CrossRef (150M+ works): DOI resolution, journal metadata
- arXiv (2.4M+ preprints): CS/AI/ML cutting-edge
- Web search — ONLY for: VHB-JOURQUAL rankings, specific CFPs, conference info
- Firecrawl — LAST RESORT for sources without APIs (AIS eLibrary, specific repositories)
Search Strategy
Step 1: Query Construction
For any research topic, construct 4-6 search queries:
| Query Type | Purpose | Example (GenAI/Agents paper) |
|---|---|---|
| Core English | Main topic | "generative AI enterprise implementation" |
| Synonym English | Alternative terms | "large language models organizational adoption" |
| Narrow English | Specific aspect | "autonomous AI agents business process" |
| Adjacent English | Related field | "AI transformation strategy organizational change" |
| German | German publications | "generative KI Unternehmen Implementierung" |
| Theoretical | Theory-specific | "sociotechnical systems artificial intelligence" |
Step 2: Execute Search
from scripts.academic_search import search_all, search_semantic_scholar, search_openalex, snowball, deduplicate_papers, papers_to_csv, papers_to_bibtex_file
# Round 1: Broad multi-API search
papers = search_all("generative AI enterprise implementation",
max_results_per_source=20, year_from=2020)
papers += search_all("autonomous AI agents organizational",
max_results_per_source=20, year_from=2020)
papers += search_all("LLM adoption business strategy",
max_results_per_source=15, year_from=2022)
# Round 2: German sources via OpenAlex
papers += search_openalex("generative KI Implementierung Unternehmen",
max_results=15, year_from=2020)
# Deduplicate
papers = deduplicate_papers(papers)
print(f"After dedup: {len(papers)} unique papers")
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 · 347 lines · 93 tokens per session scan A cab64f7c7852
literature-engine is a skill published in the GitHub repository TobiasBlask/open-paper-machine (18 stars, last pushed 5mo ago), licensed MIT. It adds 93 tokens to every session and 3,124 once invoked, about $0.0005 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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