bahayonghang/paper-workbench

Paper Workbench

Unified entrypoint for paper intake, strategic reading, multi-paper synthesis, and review construction.

Keep paper-record as the normalization layer. Do not merge high-level analysis back into the normalized record.

When to use

Use this skill when the job is to:

• read one paper quickly
• deeply deconstruct one paper
• compare or synthesize multiple papers
• build a review outline or gap map
• normalize paper sources into reusable machine-readable artifacts

Do not use this skill when the primary job is to implement a paper. In that case, route to paper2code.

Public interfaces

• paper-record — normalized single-paper facts
• researcher-profile — user research anchor
• paper-deep-read — single-paper strategic analysis artifact
• literature-synthesis — cross-paper integration artifact
• review-outline — literature-review planning artifact

Accepted inputs

• arXiv IDs and arXiv URLs
• AlphaXiv URLs
• DOI strings or doi.org/... URLs
• local academic PDFs or text files
• remote PDF URLs
• paper landing pages that expose a PDF
• existing paper-record JSON
• existing researcher-profile, paper-deep-read, literature-synthesis, or review-outline JSON

Routing workflow

1. Resolve the input class from $ARGUMENTS, the latest user message, or a pasted JSON artifact.
2. If the request is paper-level and not already normalized, run scripts/normalize_paper.py first.
3. Determine the mode from explicit user intent or the defaulting rules below.
4. If the chosen mode is profile-sensitive, load the supplied researcher-profile or collect only the missing fields.
5. Produce the requested mode output.
6. Persist artifacts only when the user asked to save them.

Mode quick guide

Single-paper modes

• scan
  • Use for “先快速扫一下”, “预判”, or fast worth-reading decisions
• deep-read
  • Use for “精读这篇”, “深度阅读”, “解构这篇”
• card
  • Use for “只做卡片”
• interpret
  • Compatibility path for a lightweight explanation
• xray
  • Compatibility path for compact critique
• json
  • Return the normalized paper-record

Cross-paper modes

• synthesis
  • Use for “整合这几篇”, “对比分析”, “找研究空白”
• review
  • Use for “搭综述框架”, “写这一段”

Defaulting rules

• If the user explicitly asks for a machine-readable or saved schema artifact, default to json
• If the user provides a single paper and asks to read or analyze it without a more specific mode, default to scan
• If the user provides 3 or more papers and asks for integration, default to synthesis
• If the user provides exactly 2 papers and asks for integration, run a comparison-oriented synthesis and mark any gap mapping as provisional

Normalize first

For any paper-like input, run:

python "$SKILL_DIR/scripts/normalize_paper.py" \
  --source "<paper-source>" \
  --lang "<lang>" \
  --fulltext "<auto|prefer|never>"

Use --save only when the user asked to persist the normalized JSON.

Profile workflow

Before deep-read, card, synthesis, or review, prefer a researcher-profile.

If missing, collect only these fields:

• research_field
• core_question
• thesis (optional)
• target_tier
• stage

If the user clearly wants no back-and-forth, proceed with a generic profile-light analysis and explicitly mark that personalization is limited.

If the user wants persistence, create or update the profile with:

python "$SKILL_DIR/scripts/workbench_io.py" init-profile \
  --path "<profile-path>" \
  --research-field "<field>" \
  --core-question "<question>" \
  --thesis "<optional-thesis>" \
  --target-tier "<target-tier>" \
  --stage "<stage>"

Artifact persistence

When the user asks to save a deep read, synthesis, or review plan, write a JSON artifact plus an optional Markdown or Org sidecar:

python "$SKILL_DIR/scripts/workbench_io.py" save-artifact \
  --workspace "<workspace-dir>" \
  --artifact-type "<paper-deep-read|literature-synthesis|review-outline>" \
  --title "<artifact-title>" \
  --payload-file "<json-payload-file>" \
  --profile-path "<optional-profile-path>" \
  --source-record "<path-to-paper-record>" \
  --sidecar-file "<optional-md-or-org>"

Output rules

• Separate 作者观点 from 系统分析
• Never invent page numbers, quotations, or empirical details
• If a requested quote or page anchor is missing, use [信息待核实]
• synthesis and review must integrate arguments across papers rather than serially summarizing each paper
• review paragraphs must use PEEL as a micro-argument structure, not a citation list
• If the input evidence is too thin for the requested mode, downgrade the claim strength instead of pretending full coverage

Edge cases

• Mixed raw sources + existing JSON artifacts:
  • normalize raw sources first, then merge at the artifact layer
• More than one paper but user asks for deep-read:
  • either choose the clearly primary paper or ask which one to focus on
• DOI metadata only and no reachable full text:
  • return the strongest metadata available and mark missing full-text facts

References

• references/routing.md — source classification and routing logic
• references/schema.md — canonical paper-record contract
• references/artifacts.md — researcher-profile and higher-level artifacts
• references/migration.md — compatibility and alias mapping
• references/modes/json.md — machine-readable output rules
• references/modes/interpret.md — lightweight explanation path
• references/modes/xray.md — compact critique path
• references/modes/scan.md — single-paper quick triage
• references/modes/deep-read.md — full single-paper deconstruction
• references/modes/card.md — literature card only
• references/modes/synthesis.md — cross-paper integration
• references/modes/review.md — literature-review planning and writing