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.

> In the python commands below, `` is this skill's base directory, > announced when the skill loads. Substitute that literal path; it is not an > environment variable. Bundled scripts self-locate, so only the path needs to > resolve.

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 from its methods into working code. That implementation work is out of scope for this skill.

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.

1. If the request is paper-level and not already normalized, run

scripts/normalize_paper.py first.

1. Determine the mode from explicit user intent or the defaulting rules below.
2. If the chosen mode is profile-sensitive, load the supplied

researcher-profile or collect only the missing fields.

1. Produce the requested mode output.
2. 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 "/scripts/normalize_paper.py" \
  --source "" \
  --lang "" \
  --fulltext ""

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 "/scripts/workbench_io.py" init-profile \
  --path "" \
  --research-field "" \
  --core-question "" \
  --thesis "" \
  --target-tier "" \
  --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 "/scripts/workbench_io.py" save-artifact \
  --workspace "" \
  --artifact-type "" \
  --title "" \
  --payload-file "" \
  --profile-path "" \
  --source-record "" \
  --sidecar-file ""

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/ANALYSIS_FRAMEWORK.md — x-ray five-dimension critique framework
• references/template-paper.org — Org sidecar template for deep-read / interpret output
• references/template-xray.org — Org sidecar template for x-ray critique output
• 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

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: bahayonghang
• Source: bahayonghang/my-ai-cli-toolkit
• License: MIT

Install and usage instructions live in the source repository linked above.