Research Paper Kb

Persistent cross-session knowledge base for research papers. Ingest arXiv/DOI → extract method, gap, threat level → append to PAPERS.md. Never lose paper con...

Security Scan

The skill's behavior (fetching paper metadata from arXiv/Semantic Scholar and appending structured entries to PAPERS.md and MEMORY.md) matches its description and does not request unexplained privileges or installs.

Purpose & Capability

The name/description (research paper KB) matches the instructions: fetch paper metadata, extract structured fields, generate BibTeX, and append to PAPERS.md / update MEMORY.md. No unrelated credentials, binaries, or installs are requested.

Instruction Scope

Instructions only reference arXiv and the Semantic Scholar API and explicitly read/update PAPERS.md and MEMORY.md in the workspace root (which is expected for a persistent KB). Minor note: the skill suggests extracting information from 'any available full text' but does not specify additional sources or crawling behavior beyond arXiv/Semantic Scholar.

Install Mechanism

No install spec or code files — instruction-only skill. This is low-risk because nothing is written to disk beyond the files the skill itself instructs the agent to create (PAPERS.md, MEMORY.md).

Credentials

The skill declares no required environment variables or credentials (proportionate). Practical caveat: Semantic Scholar API usage can be rate-limited or require an API key for higher-volume access; the skill does not declare or request an API key (this is an operational omission rather than a secrecy concern).

Persistence & Privilege

The skill writes/updates PAPERS.md and MEMORY.md in the workspace root (its stated purpose). It is not always: true and does not claim elevated system-wide privileges or modify other skills' configs.

Assessment

This skill will make HTTP requests to arXiv and Semantic Scholar and will create/append to PAPERS.md and update MEMORY.md in your workspace root. If you install it: (1) expect the agent to write persistent paper entries to those files, so don't run it in a workspace containing sensitive or unrelated files; (2) Semantic Scholar has rate limits and may require an API key for heavy use — the skill doesn't request one, so you may need to provide it separately if you hit limits; (3) verify autogenerated BibTeX keys and threat-level judgments before relying on them; (4) if you prefer not to have persistent files auto-updated, do not enable autonomous invocation or run the skill only on demand.

Persistent research paper knowledge base for AI agents.

Ingest any paper (arXiv URL, DOI, or title) and extract structured intelligence into a permanent PAPERS.md knowledge base that survives across sessions. Never lose context on a paper again.

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Trigger Conditions

Use this skill when the user:

• Pastes an arXiv URL (e.g. https://arxiv.org/abs/2310.12345)
• Pastes a DOI (e.g. 10.1038/s41586-024-07156-8)
• Says "add this paper to my KB" / "track this paper" / "save this paper"
• Says "what do we know about [paper title]"
• Says "update my paper KB" / "scan my PAPERS.md"
• Says "show me the papers I'm tracking" / "what papers have I saved"

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What This Skill Does

1. Fetches the paper abstract, metadata, and key sections
2. Extracts structured intelligence (method, gap, threat level, overlap)
3. Generates a clean BibTeX entry
4. Appends a structured entry to PAPERS.md in the workspace
5. Updates MEMORY.md with a pointer so future sessions know the KB exists
6. Works across sessions — the knowledge base is a file, not context

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Step-by-Step Instructions

Step 1: Identify the Paper

Accept any of:

• arXiv URL: https://arxiv.org/abs/XXXX.XXXXX
• arXiv ID: 2310.12345 or 2310.12345v2
• DOI: 10.XXXX/...
• Title string: look up via Semantic Scholar API

Normalize to arXiv ID or DOI before proceeding.

Step 2: Fetch Paper Metadata

For arXiv papers — fetch the abstract page:

https://arxiv.org/abs/<arxiv_id>

Extract: title, authors, date, abstract, subject categories.

Also fetch the Semantic Scholar API for structured metadata:

https://api.semanticscholar.org/graph/v1/paper/arXiv:<arxiv_id>?fields=title,authors,year,abstract,tldr,citationCount,influentialCitationCount,fieldsOfStudy

For DOI papers — use Semantic Scholar:

https://api.semanticscholar.org/graph/v1/paper/<DOI>?fields=title,authors,year,abstract,tldr,citationCount,influentialCitationCount,externalIds

For title lookup:

https://api.semanticscholar.org/graph/v1/paper/search?query=<url_encoded_title>&fields=title,authors,year,abstract,externalIds&limit=1

Step 3: Extract Structured Intelligence

From the abstract and any available full text, extract:

Field	What to Extract
Method	Core technical approach or contribution (1-2 sentences)
Gap they claim	What problem/limitation they say they're solving
Key results	Main quantitative or qualitative outcome
Overlap with user's work	Ask the user if context is unclear; or infer from prior PAPERS.md entries and MEMORY.md
Threat level	1-5 scale: how much does this threaten the user's research? (1=unrelated, 5=directly competing)
Citation count	From Semantic Scholar
Related papers	Up to 3 highly-cited related papers from the same fetch

Threat level guide:

• 1 — Unrelated field, no overlap
• 2 — Adjacent method, different application
• 3 — Similar approach, different dataset/domain
• 4 — Direct competition, overlapping claims
• 5 — Near-identical work, same target problem

Step 4: Generate BibTeX

Generate a clean BibTeX entry. Format:

For arXiv:

@article{<AuthorYEARkeyword>,
  title     = {<Full Title>},
  author    = {<Author1> and <Author2> and ...},
  journal   = {arXiv preprint arXiv:<id>},
  year      = {<year>},
  url       = {https://arxiv.org/abs/<id>},
  note      = {arXiv:<id>}
}

For published paper:

@article{<AuthorYEARkeyword>,
  title     = {<Full Title>},
  author    = {<Author1> and <Author2> and ...},
  journal   = {<venue>},
  year      = {<year>},
  doi       = {<doi>},
  url       = {https://doi.org/<doi>}
}

BibTeX key convention: FirstAuthorLastNameYearFirstContentWord (e.g., Smith2024diffusion)

Step 5: Write to PAPERS.md

Check if PAPERS.md exists in the workspace root. If not, create it with the header:

# PAPERS.md — Research Paper Knowledge Base
> Auto-maintained by the `research-paper-kb` skill. Add papers with: "add this paper to my KB"
> Last updated: <date>

---

Append (never overwrite) the following entry template:

## [<Short Title>](<arxiv_or_doi_url>)
**Added:** <YYYY-MM-DD>  
**Authors:** <Author1>, <Author2>, ...  
**Venue:** <arXiv / Conference / Journal>  
**Citations:** <N> (Semantic Scholar)  
**Threat Level:** <1-5> — <one-line reason>

### Method
<1-2 sentence description of the core technical contribution>

### Gap They Claim
<What problem/limitation they say they're solving>

### Key Results
<Main outcomes, benchmarks, or claims>

### Overlap With My Work
<How this relates to the user's research — ask if unclear>

### Notes
<Any additional context the user provides, or leave blank>

### BibTeX
```bibtex
<bibtex entry>

---

### Step 6: Update MEMORY.md

After writing to PAPERS.md, append or update the PAPERS.md pointer in `MEMORY.md`:

Find or create a section `## Research Paper KB`:
```markdown
## Research Paper KB
- PAPERS.md exists in workspace root — <N> papers tracked as of <date>
- Latest addition: <Short title> (<threat level>/5)
- Run `research-paper-kb` to add more papers

If the section already exists, update the count and latest addition line.

Step 7: Confirm to User

Reply with a summary:

✅ Added to PAPERS.md

**[Paper Title]** (<year>)
- Authors: ...
- Threat level: X/5 — <reason>
- BibTeX key: `AuthorYearWord`

PAPERS.md now has N papers. Run `show me my papers` to review.

---

Query Mode: "Show Me My Papers"

When the user asks to review their paper KB:

1. Read PAPERS.md
2. Summarize: total count, highest threat-level papers, recently added
3. Optionally filter by threat level, topic, or year
4. Offer to export BibTeX for all papers: collect all @article{...} blocks and present as a code block

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Query Mode: "What Do We Know About X?"

When the user asks about a specific paper or topic:

1. Search PAPERS.md for matching title/author/keywords
2. Return the structured entry
3. If not found, offer to add it: "This paper isn't in your KB yet. Want me to add it?"

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Edge Cases

Situation	Handling
arXiv paper not found	Try Semantic Scholar title search; if still not found, ask user to confirm title
DOI behind paywall	Fetch abstract from DOI.org metadata (https://doi.org/<doi> with Accept: application/json); note "full text unavailable"
Paper already in PAPERS.md	Detect by title/arXiv ID match; offer to update notes or threat level instead
User doesn't know their research area	Ask: "What's your research focus? I'll use this to assess overlap." Store in MEMORY.md
Semantic Scholar rate limit	Fall back to arXiv API: http://export.arxiv.org/api/query?id_list=<id>

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Integration With Other Skills

This skill works best alongside:

• citation-management — for full BibTeX workflow and PubMed/Google Scholar search
• biorxiv-database — for biology/life-science preprints (use to find papers to add)
• cs-research-methodology — for identifying gaps and research proposals from your KB
• proactive-research (ClaWHub) — can feed new papers into this KB automatically

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Files Modified

File	Action
PAPERS.md	Append new entry (create if missing)
MEMORY.md	Update ## Research Paper KB section

Never modifies: SOUL.md, USER.md, AGENTS.md, TOOLS.md, or any project files.

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Example Interaction

User: "Add this to my KB: https://arxiv.org/abs/2310.06825"

Agent:

1. Fetches arXiv 2310.06825 → "Mistral 7B" by Jiang et al.
2. Fetches Semantic Scholar metadata (12k citations)
3. Extracts: method = grouped-query attention + sliding window; gap = efficient 7B model
4. Assesses threat level vs user's work (reads MEMORY.md for context)
5. Generates BibTeX key Jiang2023mistral
6. Appends structured entry to PAPERS.md
7. Updates MEMORY.md
8. Replies: "✅ Added Mistral 7B (threat: 2/5 — efficient inference, different from your focus on X)"

---

Metadata

name: research-paper-kb
version: 1.0.0
author: <your-github-handle>
category: Academic & Research
tags: [research, papers, arxiv, bibtex, knowledge-base, literature, academic, persistent-memory]
summary: Persistent cross-session knowledge base for research papers. Ingest arXiv/DOI → extract method/gap/threat level → append to PAPERS.md. Never lose paper context again.
requires: []