smithery/hummat

papi

This skill should be used when the user wants to interact with their paper database — listing papers, searching content, showing paper details, adding papers, or exporting context.

Installation

$ npx skills add smithery/hummat --skill papi

Summary

  • This skill should be used when the user wants to interact with their paper database — listing papers, searching content, showing paper details, adding papers, or exporting context.
  • Matches queries like "search papers for X", "add this arXiv paper", "show equations from paper Y", "what papers do I have".
  • Prefer CLI over MCP RAG tools for direct lookups.

Similar popular skills

Related neighbors and high-traction skills in the same topics — useful to compare before installing.

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More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

Claude Code Not declared
Cursor Not declared
Codex Not declared
GitHub Copilot Not declared
Windsurf Not declared
Gemini CLI Not declared
Cline Not declared
OpenCode Not declared

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 4,133 B
  • docs SUMMARY.md 121 B

History

  1. First recorded snapshot · 0 installs

SKILL.md

Paper Reference Assistant (CLI)

Entry point skill. Use papi CLI first; MCP RAG tools only when CLI is insufficient.

For specialized workflows, invoke dedicated skills:

  • /papi-ask — RAG queries requiring synthesis
  • /papi-verify — verify code against paper
  • /papi-compare — compare papers for decision
  • /papi-ground — ground responses with citations
  • /papi-curate — create project notes

Setup

papi path   # DB location (default ~/.paperpipe/; override via PAPER_DB_PATH)
papi list   # available papers
papi list | grep -i "keyword"  # check if paper exists before searching

When NOT to Use MCP RAG

  • Paper name known → papi show <paper> -l summary
  • Exact term search → papi search --rg "term"
  • Checking equations → papi show <paper> -l eq
  • Only use RAG when above methods fail or semantic matching required

Decision Tree

Question Tool
"What does paper X say about Y?" papi show X -l summary, then papi search --rg "Y"
"Does my code match the paper?" /papi-verify skill
"Which paper mentions X?" papi search --rg "X" first, then leann_search() if no hits
"Compare approaches across papers" /papi-compare skill or papi ask
"Need citable quote with page number" retrieve_chunks() (PQA MCP)
"Cross-paper synthesis" papi ask "..."

Search Commands

papi search --rg "query"                        # literal text match (fast, no LLM)
papi search --rg --regex "term one|term two"    # regex or OR match; --regex is required
papi search "query"                   # ranked BM25
papi search --hybrid "query"          # ranked + exact boost
papi search "query" -p paper1,paper2  # limit search to specific papers
papi ask "question"                   # PaperQA2 RAG
papi ask "question" --backend leann   # LEANN RAG
papi notes {name}                     # open/print implementation notes

Search Escalation (cheapest first)

  1. papi search --rg "X" — exact text, fast, no LLM
  2. papi search "X" — ranked BM25 (requires papi index --backend search first)
  3. papi search --hybrid "X" — ranked + exact boost
  4. leann_search() — semantic search, returns file paths for follow-up
  5. retrieve_chunks() — formal citations (DOI, page numbers)
  6. papi ask "..." — full RAG synthesis

MCP Tool Selection (when papi CLI insufficient)

Tool Speed Output Best For
leannsearch(indexname, query, top_k) Fast Snippets + file paths Exploration, finding which paper to dig into
retrievechunks(query, indexname, k) Slower Chunks + formal citations Verification, citing specific claims
papi ask "..." Slowest Synthesized answer Cross-paper questions, "what does literature say"
  • Check available indexes: leannlist() or listpqa_indexes()
  • Embedding priority: Voyage AI → Google/Gemini → OpenAI → Ollama

Adding Papers

papi add 2303.13476                   # arXiv ID
papi add https://arxiv.org/abs/...    # URL
papi add 2303.13476 1706.03762 "Attention Is All You Need"  # multiple at once (mixed sources OK)
papi add --pdf /path/to.pdf           # local PDF
papi add --pdf "https://..."          # PDF from URL
papi add --from-file papers.bib       # bulk import

Per-Paper Files

Located at {db}/papers/{name}/: equations.md, summary.md, source.tex, notes.md, paper.pdf, figures/.

If agent can't read ~/.paperpipe/, export to repo: papi export <papers...> --level equations --to ./paper-context/ Use --figures to include extracted figures in export.

See references/commands.md for full command reference and per-file details.