paper-reproduction

name	paper-reproduction
description	This skill should be used when the user asks to "reproduce a paper", "implement paper code", "paper2code", "replicate research results", "run experiment from paper", "implement the algorithm from this paper", or wants to locate, understand, and plan implementation of a specific academic paper using PaperBot MCP tools.
tools	["paper_search","paper_judge","paper_summarize","export_to_obsidian","save_to_memory"]

Paper Reproduction Workflow

Reproduce or implement a paper: locate it, assess reproducibility, understand its contributions, save an implementation plan, and export a paper note.

Workflow

Step 1: Find the paper

Call paper_search with the paper title, topic, or known identifier.

• Parameters: query (required; include ArXiv ID or DOI if known for direct lookup), max_results (default 10; use 3–5 for a known paper to minimize noise)
• Returns: list of paper dicts with title, abstract, authors, year, venue, arxiv_id, doi
• Select the most specific match if multiple results are returned

Step 2: Judge reproducibility

Call paper_judge with rubric="reproducibility" to assess implementation feasibility.

• Parameters: title, abstract, full_text (optional; include if available for richer analysis), rubric="reproducibility"
• Returns: dimension scores (1–5) including rigor, clarity, novelty, reproducibility, overall_score, and recommendation
• Favorable signals: high rigor and clarity scores
• Unfavorable signals: low clarity score may indicate reproduction difficulty; low reproducibility score indicates missing implementation details (pseudocode, datasets)

Step 3: Summarize paper contributions

Call paper_summarize to extract key contributions, methods, and findings.

• Parameters: title, abstract
• Returns: dict with summary key (concise string covering contributions and approach)
• Use the summary to inform the implementation plan in Step 4

Step 4: Save reproduction plan to memory

Call save_to_memory with an outline of the planned implementation steps.

• Parameters: content (implementation plan text), kind ("project" for structured plans or "decision" for approach decisions), user_id (default "default"), scope_type ("global" or "track" if this paper belongs to a research track), scope_id (track ID if scope_type="track"), confidence (0.0–1.0)
• Include: key algorithms to implement, datasets needed, evaluation metrics, dependencies

Step 5: Export paper note

Call export_to_obsidian to create a structured Obsidian note for the paper.

• Parameters: title, abstract, authors (list), year, venue, arxiv_id, doi (provide all available identifiers)
• Returns: dict with markdown key — YAML-frontmattered note ready for Obsidian vault
• The note provides a permanent reference alongside the implementation

Implementation Guidance

After completing the above workflow, proceed with code implementation using available tools (Bash, Write, etc.). The Paper2Code pipeline in PaperBot (src/paperbot/repro/) provides deeper multi-stage analysis (Planning → Blueprint → Environment → Generation → Verification) for complex reproductions requiring the full PaperBot backend.

For simpler reproductions:

1. Use the summary from Step 3 and the plan from Step 4 as starting context
2. Implement iteratively, checking against paper details in the Obsidian note
3. Store implementation decisions in memory with kind="decision" as the work progresses

Degraded Mode

paper_judge and paper_summarize require a configured LLM API key. paper_search works without LLM.

When LLM-backed tools return degraded=True:

• The response also contains an error key describing the issue
• Set OPENAI_API_KEY or ANTHROPIC_API_KEY and restart the MCP server
• In degraded mode, use paper_search to locate the paper and proceed to implementation using the raw abstract and metadata; skip Steps 2 and 3