本文へ移動
cccskills
無料GitHub で公開

related-work-survey

Conducts a systematic related-work / literature-survey / state-of-the-art review for a research question by defining survey dimensions, searching each axis, building a taxonomy of prior work, identifying the gap, and producing a positioning narrative for a paper's Related Work section. Goes beyond a flat paper list to deliver structured analysis. Use when the user is starting a new research project and needs to map the landscape, asks "what's been done on X?" or "how does my idea compare to existing work?", needs to write or revise a Related Work / Background / Prior Art section, wants to identify a research gap or position their contribution, or asks to build a taxonomy of approaches in a research area.

インストール方法を見る

含まれるファイル(1)

  • SKILL.md4.0 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

Related Work Survey

Systematic literature survey for positioning a research contribution.

When to Use

  • User starts a new research project and needs landscape understanding
  • User asks "what's been done on X?"
  • User needs to write a related work section
  • User wants to identify the gap their work fills

Workflow

Step 1: Define the Research Question

Work with the user to pin down:

  • Core question: What specific problem are we solving?
  • Key concepts: What are the 3-5 key terms/concepts?
  • Scope boundaries: What's in scope vs. adjacent but out of scope?

Step 2: Identify Survey Dimensions

Every research topic sits at an intersection of multiple dimensions. Identify 2-4 axes:

Example for "Neural-Symbolic Decomposition in LLM Agents":

  • Axis 1: Neuro-symbolic integration approaches
  • Axis 2: LLM agent architectures (harness, scaffolding)
  • Axis 3: Adaptive/metacognitive planning
  • Axis 4: Text analysis at scale (specific application)

Step 3: Search Each Dimension

For each axis, use literature-search skill with targeted queries:

Axis 1 → "neuro-symbolic integration LLM reasoning 2024 2025"
Axis 2 → "LLM agent harness scaffolding architecture survey"
Axis 3 → "metacognitive planning adaptive tool use LLM"
Axis 4 → "large scale text analysis LLM code generation quality"

Collect 10-20 papers per axis, then deduplicate across axes.

Step 4: Build the Taxonomy

Organize papers into a structured taxonomy:

## Related Work Taxonomy

### 1. Neuro-Symbolic Integration
  1.1 Neural reasoning with symbolic verification
  1.2 Symbolic planning with neural execution
  1.3 Adaptive decomposition (our focus)

### 2. LLM Agent Architecture
  2.1 Harness engineering
  2.2 Self-evolving agents
  2.3 Meta-Harness optimization

### 3. ...

Step 5: Identify the Gap

The gap is where your work lives — the intersection that no existing paper covers:

Paper A does X but not Y.
Paper B does Y but not X.
We do both X and Y, connected by Z.

Step 6: Write the Narrative

Structure the related work section as a story:

  1. Context: What's the broader field?
  2. Prior art per axis: What has been done?
  3. Gap statement: What's missing?
  4. Our position: How do we fit in?

Output Format

Deliver a structured document:

# Related Work Survey: [Topic]

## Research Question
[1-2 sentences]

## Taxonomy
[Structured tree]

## Key Papers
| Paper | Axis | Key Contribution | Gap Relative to Us |
|-------|------|------------------|--------------------|
| ...   | ...  | ...              | ...                |

## Identified Gap
[Clear statement of what no existing work covers]

## Recommended Positioning
[How to frame our contribution relative to existing work]

Principles

  • Be honest about overlap: If someone has done something very similar, acknowledge it
  • Recent > old: Prioritize 2024-2026 papers for positioning; cite older seminal works for foundations
  • Quality > quantity: 20 well-chosen papers > 50 tangentially related ones
  • Track the conversation: Note who cites whom — citation chains reveal intellectual lineage
  • Check workshops/findings: Important early-stage work often appears in workshop papers

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

同じリポジトリのスキル

概要と使いどころ

Generates publication-quality academic figures (framework diagrams, pipeline illustrations, system architectures, method overviews) from a paper's method text and a target caption, using a local PaperBanana multi-agent pipeline (Retriever → Planner → Stylist → Visualizer → Critic).

日本語の概要は準備中です。原文の説明を表示しています。

jxtse/scientific-research-skills712026年5月19日 更新

Searches and discovers academic papers across multiple sources (Semantic Scholar, arXiv, Tavily, Exa, Gemini deep research, AMiner, Google Scholar) with adaptive engine selection based on query type. Returns ranked, deduplicated results with metadata (authors, venue, year, citations, abstract, PDF link). Use when the user asks to find papers / literature / publications / preprints / references on a topic, search for related work, look up a specific paper by title or DOI or arXiv ID, find papers by an author, find recent SOTA / state-of-the-art work, survey a research area, or run a deep / comprehensive literature search with synthesis.

日本語の概要は準備中です。原文の説明を表示しています。

jxtse/scientific-research-skills712026年5月19日 更新

Batch download academic paper full-text (PDF/XML) from a list of DOIs. Handles 25 DOI prefixes across 19 publisher families via three layered routes: (1) publisher TDM APIs requiring institutional subscription (Elsevier ScienceDirect, Wiley Online, Springer Nature), (2) Open Access sources (Crossref, Unpaywall, OpenAlex), and (3) a browser-based fallback for paywalled publishers without TDM access (ACS, RSC, IEEE, AIP, IOP, APS, Annual Reviews, T&F, Chinese journals). Browser fallback offers two routes — Route A drives the user's logged-in Chrome via the OpenClaw `browser` tool with `profile="user"` (best for interactive sessions), Route B uses the standalone `auto-paper-harvester` CLI with its built-in Playwright (best for unattended bulk runs). Use when the user wants to harvest, scrape, fetch, or bulk-download papers from a DOI list, savedrecs export, or Excel; or wants to fill missing full-text PDFs for an existing literature collection. Triggers on phrases like "批量下载文献", "下载全文", "harvest papers", "scrape full text", "TDM API", "下载 Elsevier 全文", "Wiley 批量下载", "下载 PDF".

日本語の概要は準備中です。原文の説明を表示しています。

jxtse/scientific-research-skills712026年5月19日 更新

Reads and analyzes academic papers (arXiv preprints, conference / journal PDFs, Zotero items) at three configurable depths: quick skim (2 min), standard read (10 min), or deep analysis (30 min). Produces structured digests covering problem, method, key innovation, results, limitations, reproducibility, hidden assumptions, and connections to the user's other work. Use when the user shares an arXiv link, PDF, or paper title and asks to read / summarize / digest / TL;DR / analyze / review / critique / explain / break down a paper, asks about a paper's contributions / methods / results / equations / figures, wants to compare two papers side by side, or needs a reading note for their records.

日本語の概要は準備中です。原文の説明を表示しています。

jxtse/scientific-research-skills712026年5月19日 更新

Extracts paper recommendations from social-media posts and online articles (小红书 / Xiaohongshu / RedNote, 微信公众号 / WeChat Official Accounts, Twitter / X threads, Reddit posts, Bilibili videos, blog posts, newsletters, Jina Reader URLs), identifies the underlying academic papers, locates the authoritative original sources (arXiv, conference proceedings, DOI), and triages relevance to the user's research before any library action. Use when the user forwards a social-media link, screenshot, or article that mentions a paper / method / model, asks to "find the original paper" from a blog or thread, shares a 调研贴 / 论文推荐 / paper recommendation post, or wants to evaluate whether a buzz-worthy paper is worth reading before adding it to Zotero.

日本語の概要は準備中です。原文の説明を表示しています。

jxtse/scientific-research-skills712026年5月19日 更新

Manages a Zotero academic reference library through both the local API (localhost:23119, read-only) and the Web API (api.zotero.org, read-write), using a structured collection hierarchy (Inbox / Active Projects / Background / Reading Queue / Archive / Meta) plus project, status, priority, and type tags. Handles adding papers with full metadata, deduplication, attaching provenance notes, moving items between collections, updating tags after reading, listing the prioritized reading queue, and setting up the literature scaffold for a new project. Use when the user asks to add / save / file / organize a paper in Zotero, check / list / clean up the reading queue, move papers between collections, tag papers for a project, query their library ("what do I have on X?"), or set up Zotero for a new research project.

日本語の概要は準備中です。原文の説明を表示しています。

jxtse/scientific-research-skills712026年5月19日 更新

jxtse のスキルをすべて見る

このスキルの問題を報告する