Litllm

litllm — Literature Search & Related-Work Generation

Standalone Claude Code skill backed by a pip-installable Python CLI. Speaks any OpenAI-compatible LLM endpoint (OpenAI, vLLM, Ollama, OpenRouter, Together, Groq, LM Studio, ...).

When to invoke

Activate this skill when the user:

• shares a PDF or markdown paper and asks for "related work", "prior art", "citations", "literature review", "what's been done on this"
• asks to "rank these candidate citations against my paper"
• asks for "deep research" / "expand the citation graph" / "find references of references"
• asks to "extract the bibliography from this PDF"

Do not invoke for:

• single-paper summarization (use a generic summarization skill)
• writing the related-work prose (use a writing skill — this skill produces ranked candidates + summaries, not the polished section)

Setup (one-time, per machine)

curl -sSL https://raw.githubusercontent.com/LitLLM/LitLLM/main/skill/install.sh | bash

That single line drops SKILL.md into ~/.claude/skills/litllm/ and installs the litllm CLI on PATH. The package is named litllm-skill on PyPI to disambiguate from the popular litellm package.

Set env vars (any OpenAI-compatible endpoint works):

export LITLLM_API_KEY="sk-..."
export LITLLM_BASE_URL="https://api.openai.com/v1"   # default
export LITLLM_MODEL="gpt-4o-mini"                    # default
export LITLLM_CONTACT_EMAIL="you@example.com"        # OpenAlex/S2 politeness
export LITLLM_S2_API_KEY="..."                       # optional, higher S2 rate

For embedding-based deep research:

pip install 'litllm-skill[embeddings]'

Commands

# Full 4-step pipeline: keywords → fetch → rank+filter → summarize
litllm related-work paper.pdf --out ./out

# Citation-graph expansion (BFS, depth 2, ≤1000 papers)
litllm related-work paper.pdf --deep-research --selection-mode abstract

# Pick the search backend
litllm related-work paper.pdf --api arxiv          # arxiv | openalex | semanticscholar

# Tune ranking
litllm related-work paper.pdf --ranking-threshold 70 --limit-per-query 15

# Single-step utilities
litllm keywords paper.pdf                # → JSON list of search queries
litllm rank paper.pdf --candidates c.json   # → debate-ranking output with scores
litllm bib paper.pdf                     # → JSON list of cited paper titles

Output

related-work writes a phased tree to --out. Each step is cached so re-runs skip completed work.

out/
├── 1_generated_queries.md      # JSON list of search queries
├── 2_fetched_papers.md         # JSON of search results (post-dedup)
├── 3_ranked_papers.md          # Debate-ranking arguments + scores per paper
├── 3.5_filtered_papers.md      # JSON of papers above ranking threshold
├── 3.5_bibfile.md              # Filtered candidates as @article BibTeX
└── 4_related_papers_summary.md # Concatenated per-paper summaries

How it works (so you can explain the output)

1. Keyword extraction — LLM reads the paper and emits 8 diverse search queries.
2. Fetch — runs queries against Semantic Scholar (default), arXiv, or OpenAlex. With --deep-research, BFS-walks the citation graph (depth 2, ≤1000 papers), picking expansions per --selection-mode:
   • abstract (default): batched LLM debate ranking on abstracts, ≥70/100 keeps
   • full-text: per-candidate LLM full-text comparison, ≥70/100 keeps
   • embedding: SPECTER cosine similarity ≥0.80, top-10 keeps
3. Rank — batched debate ranking of fetched papers vs. the query paper. Each candidate gets arguments-for, arguments-against, and a 0-100 score.
4. Filter & summarize — drops candidates below --ranking-threshold, downloads the survivors' PDFs, summarizes each in parallel.

Selection-mode trade-offs

Related skills

• huggingface-papers — fetch individual paper metadata; pairs well as a pre-step ("look up this arXiv ID, then run litllm on its PDF")
• agent-research-skills/literature-search — alternative with heuristic ranking (citations + recency + venue), no LLM debate

Source: LitLLM/LitLLM — distributed by TomeVault.