Skill: xunhe730/academic-search-bak (version 1.0.0)

--- name: academic-search role: Academic Research Specialist version: 1.0.0 triggers: - "find papers" - "academic search" - "research" - "literature review" - "arxiv" - "scholar" - "scholarly articles" - "cite" - "citation" - "peer-reviewed" - "scientific literature" --- # Role You are an Academic Research Specialist. When activated, you systematically search academic databases (arXiv, Google Scholar, Semantic Scholar), screen abstracts for relevance, analyze citation networks, and synthesize findings into structured research summaries. You find the Top 5 most relevant papers on any topic within 2 minutes. # Capabilities 1. Construct database-specific search queries using arXiv category codes, Semantic Scholar field-of-study filters, and Google Scholar advanced operators to maximize recall across academic sources 2. Screen paper abstracts against user-defined relevance criteria, extracting key findings, methodology, and contribution claims to rapidly triage large result sets 3. Analyze citation graphs to identify seminal works, survey papers, and emerging research fronts using Semantic Scholar's citation and reference APIs 4. Cross-reference findings across multiple databases to deduplicate results, verify publication status (preprint vs. peer-reviewed), and assess paper quality through venue ranking and citation velocity 5. Synthesize research results into structured literature summaries with thematic grouping, methodology comparison, and identification of research gaps # Constraints 1. Never present a preprint as peer-reviewed -- always indicate publication status (preprint, accepted, published) and venue when available 2. Never rank papers solely by citation count -- always consider recency, methodology quality, venue reputation, and relevance to the specific query 3. Never return results without verifying they are actual academic papers -- exclude blog posts, news articles, and non-scholarly content that may appear in search results 4. Always disclose when a paper is behind a paywall and attempt to locate open-access versions (arXiv preprint, institutional repository, author's homepage) 5. Always include bibliographic metadata: authors, year, venue/journal, DOI or arXiv ID for every paper returned 6. Never fabricate or hallucinate paper titles, authors, or findings -- only return results actually retrieved from academic databases # Activation WHEN the user requests academic paper search, literature review, or research discovery: 1. Analyze the research query to identify: **topic**, **discipline**, **time scope**, **methodology preferences**, and **desired depth** 2. Extract domain-specific keywords following strategies/main.md Step 1 3. Construct database-specific queries using knowledge/domain.md for API patterns and query syntax 4. Execute parallel searches across arXiv, Google Scholar, and Semantic Scholar 5. Screen and rank results using knowledge/best-practices.md criteria 6. Verify against knowledge/anti-patterns.md to avoid common academic search mistakes 7. Output a ranked list of Top 5 papers with full bibliographic metadata, key findings, and a synthesis narrative # Dependency Usage This skill extends `@botlearn/google-search` capabilities: - Uses google-search query construction for Google Scholar operator syntax (`site:scholar.google.com`, `intitle:`, date filters) - Leverages google-search source credibility assessment for ranking .edu and .gov hosted papers - Applies google-search deduplication strategies when the same paper appears across multiple databases

path: README.md
content: # @botlearn/academic-search

> Academic paper discovery across arXiv, Google Scholar, and Semantic Scholar with abstract screening, citation analysis, and research synthesis for OpenClaw Agent

## Installation

```bash
# via npm
npm install @botlearn/academic-search

# via clawhub
clawhub install @botlearn/academic-search
```

## Category

Information Retrieval

## Dependencies

`@botlearn/google-search`

## Files

| File | Description |
|------|-------------|
| `manifest.json` | Skill metadata and configuration |
| `SKILL.md` | Role definition and activation rules |
| `knowledge/` | Domain knowledge documents |
| `strategies/` | Behavioral strategy definitions |
| `tests/` | Smoke and benchmark tests |

## License

MIT


path: _meta.json
content: ownerId: kn75hdn4xhpra58fj9qs3zdqyd81yhz6
slug: academic-search
version: 1.0.0
publishedAt: 1773473898703

path: knowledge/anti-patterns.md
content: ---
domain: academic-search
topic: anti-patterns
priority: medium
ttl: 30d
---

# Academic Search -- Anti-Patterns

## Query Construction Anti-Patterns

### 1. Natural Language Search Queries
- **Problem**: Submitting conversational queries like "What are the best methods for detecting fake images?" to academic APIs. Academic databases perform poorly on natural language; they expect keyword-based queries.
- **Fix**: Extract core concepts and use database-specific syntax. Transform to: `ti:deepfake+detection+AND+cat:cs.CV` (arXiv) or `query=deepfake detection generative adversarial&fieldsOfStudy=Computer Science` (Semantic Scholar).

### 2. Single-Database Dependency
- **Problem**: Searching only arXiv and missing published journal papers, or searching only Google Scholar and missing very recent preprints uploaded in the last 48 hours.
- **Fix**: Always query at least 2 databases. Use arXiv for the freshest preprints, Semantic Scholar for structured metadata and citation graphs, and Google Scholar for the broadest coverage including theses and technical reports.

### 3. Overly Broad Queries
- **Problem**: Searching for "machine learning" without topic, method, or application constraints returns millions of irrelevant results.
- **Fix**: Add specificity through subtopic terms, category filters, date ranges, and venue constraints. "machine learning" becomes `"graph neural network" protein structure prediction 2023-` with `fieldsOfStudy=Computer Science,Biology`.

### 4. Overly Narrow Queries
- **Problem**: Using hyper-specific technical jargon that returns 0 results because the exact phrasing does not match paper titles or abstracts. For example, searching `"multi-head cross-attention with rotary position embeddings"` may miss papers that use slightly different terminology.
- **Fix**: Start specific, then progressively broaden. Use OR clauses with synonymous terms. Check if zero results means the topic is genuinely niche or your terminology is misaligned with the literature.

### 5. Ignoring Field-Specific Terminology
- **Problem**: Using CS terminology when searching for biomedical papers, or vice versa. Different fields use different terms for the same concepts (e.g., "feature" in ML vs. "biomarker" in medicine).
- **Fix**: Consult the keywords section of one known relevant paper to identify field-appropriate terminology. Use Semantic Scholar's `fieldsOfStudy` filter to disambiguate.

## Ranking & Selection Anti-Patterns

### 6. Citation Count Bias
- **Problem**: Ranking papers primarily by citation count. This biases toward older papers (more time to accumulate citations), popular fields (more researchers citing each other), and anglophone research (English papers cited more globally). A 2024 paper with 15 citations may be more impactful for the user than a 2018 paper with 500.
- **Fix**: Use **citation velocity** (citations per year) and **influential citation count** (Semantic Scholar) instead of raw citation count. Weight recency appropriately for fast-moving fields. A paper with 15 influential citations in 1 year may outrank one with 500 total citations over 6 years.

### 7. Ignoring Methodology Quality
- **Problem**: Selecting papers based solely on claimed results without evaluating the methodology. Papers may report impressive numbers from flawed experimental setups, cherry-picked baselines, or non-standard evaluation metrics.
- **Fix**: Check for: (a) comparison against established baselines, (b) use of standard benchmark datasets, (c) statistical significance reporting, (d) ablation studies, (e) reproducibility indicators (code/data availability). Apply the methodology rigor factor from the ranking framework in best-practices.md.

### 8. Recency Bias
- **Problem**: Automatically preferring the newest papers and dismissing foundational work. Users may miss seminal papers that define the field's core concepts, or survey papers that provide essential context.
- **Fix**: Include at least one foundational or survey paper when the user is exploring a new topic. Explicitly distinguish between "latest results" and "essential background." A 2017 paper that introduced a paradigm (e.g., "Attention Is All You Need") is critical context even if the user asked for recent work.

### 9. Venue Snobbery
- **Problem**: Dismissing papers from workshops, smaller conferences, or preprints solely based on venue prestige. Groundbreaking work sometimes appears first in workshops or on arXiv before being published at a top venue.
- **Fix**: Assess the paper on its own merits (methodology, results, clarity) rather than venue alone. Note the venue tier but do not automatically exclude lower-tier publications. Flag preprints as "not yet peer-reviewed" rather than excluding them.

### 10. Popularity Echo Chamber
- **Problem**: Returning only the most-cited papers leads to an echo chamber where the same 5-10 well-known papers appear for every query in a field, missing diverse perspectives and newer approaches.
- **Fix**: Deliberately include at least 1-2 papers from different research groups, geographic regions, or methodological traditions. Use Semantic Scholar's "recommended papers" feature to discover less obvious but relevant work.

## Result Presentation Anti-Patterns

### 11. Missing Publication Status
- **Problem**: Presenting arXiv preprints alongside peer-reviewed journal articles without distinguishing between them. Users may assume all results have undergone peer review.
- **Fix**: Always indicate publication status: `[Preprint]`, `[Peer-reviewed - Conference]`, `[Peer-reviewed - Journal]`, `[Workshop paper]`, `[Thesis]`. This is a hard requirement (see skill.md Constraints).

### 12. Incomplete Bibliographic Metadata
- **Problem**: Returning paper titles and URLs without authors, year, venue, or identifiers. Users cannot properly cite, find, or assess the papers.
- **Fix**: Every paper must include: authors (at least first author + "et al."), year, venue/journal, and at least one persistent identifier (DOI, arXiv ID, or Semantic Scholar Corpus ID).

### 13. Abstract Dump Without Synthesis
- **Problem**: Copy-pasting raw abstracts for each paper without summarizing the key finding or explaining why it is relevant to the user's specific query.
- **Fix**: Extract 1-2 sentences of key findings relevant to the user's question. Provide a synthesis section that connects papers thematically, identifies consensus and contradictions, and suggests a reading order.

### 14. Fabricating Paper Details
- **Problem**: Generating plausible-sounding but non-existent paper titles, authors, or findings to fill gaps in search results. This is a critical failure mode for LLM-based agents.
- **Fix**: Only return papers actually retrieved from database API responses. If the search returns fewer than 5 relevant results, report that honestly rather than padding with fabricated entries. Include the database query used so the user can verify.

### 15. Ignoring Open Access Status
- **Problem**: Returning papers behind paywalls without checking for open-access alternatives. Users without institutional access cannot read the papers.
- **Fix**: For each paper, check: (a) arXiv preprint version, (b) Semantic Scholar `openAccessPdf` field, (c) author's personal website or institutional repository. Flag papers with no open-access version available and suggest the user check their institutional library access.

## Workflow Anti-Patterns

### 16. No Iteration on Poor Results
- **Problem**: Returning the first set of results without assessing quality. If the top results are irrelevant or insufficient, the skill should refine and retry.
- **Fix**: After initial results, perform a quality check: Are at least 3 of the top 5 directly relevant? If not, refine the query using synonym expansion, alternative category codes, or broader/narrower scope. Iterate up to 2 times before presenting final results.

### 17. Ignoring the Citation Graph
- **Problem**: Treating each paper as an isolated entity rather than exploiting the citation network for discovery. Citation snowballing (forward and backward) is one of the most powerful techniques in academic search.
- **Fix**: For the top 1-2 most relevant initial results, always check their citations (forward) and references (backward) using the Semantic Scholar API. This often surfaces highly relevant papers that keyword search alone would miss.

### 18. Conflating Preprint Versions
- **Problem**: Returning both the arXiv preprint and the published version of the same paper as separate results, inflating the result count with duplicates.
- **Fix**: Follow the deduplication protocol from best-practices.md. Match by DOI/arXiv ID, then by title+author+year. Keep the published version as the primary entry but include the arXiv link for open access.


path: knowledge/best-practices.md
content: ---
domain: academic-search
topic: query-construction-relevance-ranking-cross-referencing
priority: high
ttl: 30d
---

# Academic Search -- Best Practices

## Query Construction

### 1. Research Question Decomposition
Before searching, decompose the user's request into structured components:
- **Core topic** -- The primary subject (e.g., "federated learning")
- **Subtopic / aspect** -- Specific focus area (e.g., "privacy guarantees")
- **Methodology preference** -- Empirical, theoretical, survey (e.g., "benchmark evaluation")
- **Temporal scope** -- Date range for results (e.g., "2022 onwards")
- **Discipline** -- Target field(s) (e.g., computer science, biomedicine)

### 2. Academic Keyword Selection
Academic papers use different terminology than general web content:

| General Term | Academic Equivalent(s) |
|-------------|----------------------|
| AI chatbot | large language model, conversational agent, dialogue system |
| image recognition | visual recognition, image classification, object detection |
| data privacy | differential privacy, privacy-preserving, data protection |
| brain scan | neuroimaging, fMRI, MRI, EEG |
| drug discovery | pharmacological screening, molecular docking, compound identification |
| self-driving car | autonomous vehicle, automated driving, self-driving system |
| fake news | misinformation detection, claim verification, fact-checking |

**Key principle**: Use the terminology of the target research community. Check the "Keywords" section of known relevant papers to discover preferred terms.

### 3. Database-Specific Query Strategies

#### arXiv Strategy
- Use category codes to narrow scope: `cat:cs.LG` for ML, `cat:cs.CL` for NLP
- Search title (`ti:`) for high-precision results, abstract (`abs:`) for broader recall
- Use `ANDNOT` to exclude tangential categories
- Sort by `submittedDate` for latest work, `relevance` for best matches
- For emerging topics, prefer recency over relevance sorting

#### Semantic Scholar Strategy
- Use `fieldsOfStudy` parameter to filter by discipline
- Use `year` parameter with range syntax (`2022-2025`) to constrain dates
- Request `influentialCitationCount` to gauge true impact
- Use the `tldr` field for quick paper summaries when triaging large result sets
- Follow up with citation/reference graph for seminal paper discovery

#### Google Scholar Strategy (via google-search)
- Use `intitle:"keyword"` for title-focused search
- Use `author:"name"` to find specific researcher's work
- Use `source:"venue"` to restrict to specific journals/conferences
- Apply `as_ylo` and `as_yhi` URL parameters for date ranges
- Use `"exact phrase"` for specific technical terms or method names

### 4. Multi-Database Search Protocol
For comprehensive literature coverage:
1. **Start with Semantic Scholar** -- Best for structured metadata, citation graphs, and field-of-study filtering
2. **Supplement with arXiv** -- Catches very recent preprints not yet indexed elsewhere (especially CS, physics, math)
3. **Verify with Google Scholar** -- Broadest coverage; catches papers from smaller venues, theses, and technical reports
4. **Cross-reference results** -- Deduplicate using DOI or arXiv ID; merge metadata from the richest source

### 5. Query Expansion Techniques
When initial results are insufficient:
- **Synonym expansion**: Add OR clauses with alternative terms (e.g., `"graph neural network" OR "message passing network"`)
- **Citation snowballing**: Find one relevant paper, then search its references (backward) and citations (forward)
- **Author tracking**: Identify key authors from initial results, then search for their other recent papers
- **Venue scoping**: If a relevant paper was published at ICML, search specifically within ICML proceedings for related work
- **Related paper features**: Use Semantic Scholar's "recommended papers" or Google Scholar's "Related articles"

## Relevance Ranking

### Multi-Factor Ranking Framework
Rank papers by weighted combination of these factors:

| Factor | Weight | Assessment Criteria |
|--------|--------|-------------------|
| **Topical Relevance** | 35% | How directly does the paper address the user's specific question? Title/abstract keyword overlap, methodology match |
| **Methodological Rigor** | 20% | Appropriate methodology, sufficient baselines, statistical significance, reproducibility indicators |
| **Venue Quality** | 15% | Conference/journal ranking (A*/A for CS, Impact Factor for journals), peer-review status |
| **Recency** | 15% | Publication date relative to the field's pace; recent is better for fast-moving fields |
| **Impact** | 15% | Influential citation count, citation velocity, whether it introduced a widely-adopted technique |

### Scoring Procedure
For each paper, score 0-5 on each factor:
- **5** -- Excellent: directly relevant, top venue, rigorous method, high and growing citations
- **4** -- Strong: highly relevant with minor gaps in one dimension
- **3** -- Good: relevant but from a secondary venue or with moderate impact
- **2** -- Acceptable: tangentially relevant or older but still useful
- **1** -- Marginal: peripherally related or methodologically weak
- **0** -- Not relevant: off-topic, retracted, or fundamentally flawed

Compute weighted score: `(Relevance * 0.35) + (Rigor * 0.20) + (Venue * 0.15) + (Recency * 0.15) + (Impact * 0.15)`

### Special Ranking Adjustments
- **Survey papers**: Boost ranking when user requests "overview" or "literature review" -- surveys provide comprehensive coverage
- **Seminal papers**: Boost ranking for foundational papers even if older, when user is exploring a new field
- **Preprints**: Apply a -1 penalty to venue quality score unless the preprint is from a well-known research group or has high citation count
- **Open access**: Apply a +0.5 bonus when the user needs full-text access and the paper has a freely available PDF

## Cross-Referencing Strategies

### 1. Forward Citation Analysis
Starting from a known relevant paper, examine papers that cite it:
- Use Semantic Scholar `/paper/{id}/citations` endpoint
- Filter citations by year to find recent extensions
- Sort by `influentialCitationCount` to find the most impactful follow-up works
- Identify **citation clusters** -- groups of papers that cite the same source tend to be related

### 2. Backward Reference Analysis
Starting from a known relevant paper, examine its references:
- Use Semantic Scholar `/paper/{id}/references` endpoint
- Identify the **foundational papers** the author builds upon
- Look for methodological sources -- the papers describing the technique being used
- Find the dataset and benchmark papers for evaluation context

### 3. Bibliographic Coupling
Two papers that share many references are likely related:
- Compare reference lists of candidate papers
- Papers with 30%+ reference overlap are strong candidates for relevance
- Useful for finding papers the database search missed

### 4. Co-Citation Analysis
Two papers frequently cited together by other papers are related:
- If papers A and B both appear in the reference lists of many papers, they address related topics
- Use Semantic Scholar recommended papers feature as a proxy

### 5. Author Network Exploration
- Identify prolific authors in the initial result set
- Search for their other recent publications
- Check their co-authors for related work from collaborating labs
- Examine their Google Scholar profile for a comprehensive publication list

### 6. Deduplication Protocol
The same paper often appears across multiple databases:
1. **Match by DOI** -- Definitive identifier; if DOIs match, it is the same paper
2. **Match by arXiv ID** -- Maps arXiv preprints to their Semantic Scholar entries
3. **Match by title + first author + year** -- Fuzzy match for papers without DOI
4. **Merge metadata** -- Keep the record with the richest metadata (prefer Semantic Scholar for citations, arXiv for full text)
5. **Resolve version conflicts** -- A paper may have an arXiv v1 and a published version; prefer the published version but link to the arXiv open-access copy

## Output Formatting

### Per-Paper Entry
Each paper in the output should include:
```
[Rank]. Title
Authors: First Author et al. (Year)
Venue: Conference/Journal Name [Peer-reviewed / Preprint]
Citations: X total, Y influential | Citation velocity: Z/year
arXiv: XXXX.XXXXX | DOI: 10.XXXX/XXXXX
Open Access: [Yes - link] / [No - paywall]
Key Findings: 1-2 sentence summary of the main contribution
Relevance: Why this paper matters for the user's query
```

### Synthesis Section
After listing individual papers, provide:
- **Thematic grouping**: Cluster papers by approach or subtopic
- **Consensus findings**: What do multiple papers agree on?
- **Contradictions**: Where do papers disagree, and why?
- **Research gaps**: What questions remain unanswered?
- **Recommended reading order**: Suggest which papers to read first based on the user's background


path: manifest.json
content: {
  "name": "@botlearn/academic-search",
  "version": "0.1.0",
  "description": "Academic paper discovery across arXiv, Google Scholar, and Semantic Scholar with abstract screening, citation analysis, and research synthesis for OpenClaw Agent",
  "category": "information-retrieval",
  "author": "BotLearn",
  "benchmarkDimension": "information-retrieval",
  "expectedImprovement": 35,
  "dependencies": {
    "@botlearn/google-search": ">=0.1.0"
  },
  "compatibility": {
    "openclaw": ">=0.5.0"
  },
  "files": {
    "skill": "SKILL.md",
    "knowledge": [
      "knowledge/domain.md",
      "knowledge/best-practices.md",
      "knowledge/anti-patterns.md"
    ],
    "strategies": [
      "strategies/main.md"
    ],
    "smokeTest": "tests/smoke.json",
    "benchmark": "tests/benchmark.json"
  },
  "tags": [
    "academic",
    "search",
    "retrieval",
    "information",
    "paper",
    "discovery",
    "across"
  ],
  "capabilities": [
    "search-academic-papers",
    "analyze-citations",
    "screen-abstracts"
  ],
  "triggers": [
    "find papers",
    "academic search",
    "research",
    "literature review",
    "arxiv",
    "scholar",
    "scholarly articles",
    "cite",
    "citation",
    "peer-reviewed",
    "scientific literature"
  ]
}


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)