Research Scout Skill
Purpose
This skill discovers and analyzes academic literature relevant to the Master Thesis on multi-agent orchestration. It searches across academic databases, creates structured summaries, and proposes papers for human review. It is the thesis agent's eyes into the research landscape.
CRITICAL: This skill NEVER adds citations to the thesis. It NEVER modifies references.bib. It NEVER writes \cite{} commands in any .tex file. It only discovers and proposes. The citation-gatekeeper skill handles all approval and citation management.
Search Sources
The research scout searches across the following academic databases and repositories:
| Source | Best For | URL Pattern |
|---|---|---|
| Google Scholar | Broad academic search, citation counts | scholar.google.com |
| Semantic Scholar | AI/ML papers, citation graphs, API access | semanticscholar.org |
| arXiv | Preprints, cutting-edge AI/LLM research | arxiv.org |
| ACM Digital Library | Software engineering, HCI, systems | dl.acm.org |
| IEEE Xplore | Systems, distributed computing | ieeexplore.ieee.org |
| DBLP | Computer science bibliography, author search | dblp.org |
Search Strategies
By Topic
Search for papers matching a thesis-relevant topic:
- •"multi-agent orchestration LLM" for core topic
- •"tool use large language models" for tool calling foundations
- •"Model Context Protocol" for MCP-specific literature
- •"agentic AI systems" for agent architecture patterns
By Author
Follow prolific authors in relevant fields:
- •Authors of foundational MAS papers (Wooldridge, Jennings)
- •Authors of LLM agent papers (Yao, Schick, Qin)
- •Authors of multi-agent framework papers (Wu, Hong)
By Citation Chain
Given a known relevant paper, find:
- •Forward citations: "Papers that cite X" -- find newer work building on it
- •Backward citations: Papers in X's reference list -- find foundational work
- •Sibling citations: Papers that share many citations with X -- find related work
By Keyword Combination
Combine keywords for precision:
- •"multi-agent" AND "LLM" AND "orchestration"
- •"tool calling" AND "function calling" AND "language model"
- •"MCP" AND "protocol" AND "agent"
- •"autonomous agent" AND "coordination" AND "architecture"
Paper Summary Schema
Every discovered paper is summarized in a structured format and saved to thesis/latex/bibliography/pending-papers.json.
{
"id": "sem-scholar-abc123",
"title": "Communicative Agents for Software Development",
"authors": ["Chen Qian", "Xin Cong", "Wei Liu"],
"year": 2024,
"venue": "ACL 2024",
"url": "https://arxiv.org/abs/2307.07924",
"doi": "10.18653/v1/2024.acl-long.1",
"summary": "Proposes ChatDev, a multi-agent framework where LLM-powered agents collaborate through natural language communication to develop software. Agents take roles (CEO, CTO, programmer, tester) and coordinate through structured chat chains.",
"key_findings": [
"Role-based agent specialization improves task completion",
"Chat-chain communication reduces hallucination in code generation",
"Multi-agent debate improves code quality over single-agent approaches"
],
"methodology": "Experimental evaluation on software development benchmarks, comparing single-agent vs. multi-agent approaches",
"relevance_score": 4,
"relevance_justification": "Directly relevant as a multi-agent software framework. Different approach (role-playing chat) than juliaz_agents (tool-calling orchestration). Good contrast for verwandte-arbeiten.",
"suggested_chapter": "03-verwandte-arbeiten",
"suggested_section": "Bestehende Multi-Agenten-Frameworks",
"discovered_date": "2026-02-22",
"search_query": "multi-agent LLM software development",
"status": "pending"
}
Status Values
| Status | Meaning |
|---|---|
pending | Discovered, awaiting human review via citation-gatekeeper |
approved | Human approved; moved to approved-papers.json by citation-gatekeeper |
rejected | Human rejected; stays in pending with rejection_reason field |
duplicate | Already exists in pending or approved list |
Relevance Scoring
Each paper receives a relevance score from 1 to 5:
| Score | Meaning | Action |
|---|---|---|
| 5 | Directly addresses a thesis research question | Recommend immediate review |
| 4 | Highly relevant to a specific chapter | Recommend for review |
| 3 | Relevant background or related approach | Include in batch review |
| 2 | Tangentially related, might be useful | Include only if topic area is thin |
| 1 | Loosely connected, likely not needed | Skip unless specifically requested |
Only papers scoring 3 or higher are added to pending-papers.json by default. Papers scoring 1-2 are mentioned in the search report but not persisted unless the human requests it.
Deduplication
Before adding a paper to pending-papers.json:
- •Check against existing entries in
pending-papers.jsonby title similarity (fuzzy match, >90%) - •Check against existing entries in
approved-papers.jsonby title or DOI - •Check against existing entries in
references.bibby BibTeX key or DOI - •If a match is found, mark the new discovery as
duplicateand note the existing entry ID - •If the new discovery has a higher relevance score or additional information, update the existing entry instead
Topic Areas for THIS Thesis
The following topic areas are the primary search targets, mapped to thesis chapters:
For 02-grundlagen
- •Multi-agent systems: foundational definitions, BDI architecture, coordination mechanisms
- •Large Language Models: transformer architecture, emergent capabilities, limitations
- •Tool use / function calling: Toolformer, Gorilla, ToolLLM, function calling in GPT/Claude
- •Model Context Protocol: Anthropic's MCP specification, ecosystem
- •Agent architectures: ReAct, Chain-of-Thought, Reflexion, LATS
For 03-verwandte-arbeiten
- •Multi-agent LLM frameworks: AutoGPT, CrewAI, LangGraph, AutoGen, MetaGPT, ChatDev, CAMEL
- •Agent benchmarks: AgentBench, ToolBench, MINT
- •Orchestration patterns: hierarchical, flat, market-based, blackboard
- •Real-world agent deployments: case studies, production systems
For 04-konzept and 05-implementierung
- •Architecture patterns for LLM applications
- •Message passing in agent systems
- •Prompt engineering for tool use
- •Memory and context management in agents
For 06-evaluation
- •Evaluation methodologies for agent systems
- •Qualitative case study methodology in software engineering
- •Metrics for multi-agent system performance
Output Formats
Search Report
After a search session, produce a summary:
## Search Report: [Topic] **Date**: 2026-02-22 **Query**: "multi-agent orchestration LLM" **Sources checked**: Google Scholar, Semantic Scholar, arXiv ### Papers Found: 7 - **Score 5**: 1 paper (immediate review recommended) - **Score 4**: 2 papers - **Score 3**: 3 papers - **Score 1-2**: 1 paper (not added to pending) ### Added to pending-papers.json: 1. [Title 1] (Score 5) -- 03-verwandte-arbeiten 2. [Title 2] (Score 4) -- 02-grundlagen ... ### Duplicates skipped: 1 ### Next suggested search: "tool calling evaluation benchmark"
Chapter Coverage Report
Assess how well each chapter is covered by discovered literature:
## Literature Coverage - 02-grundlagen: 12 papers (good coverage) - 03-verwandte-arbeiten: 8 papers (needs more on MCP ecosystem) - 04-konzept: 3 papers (thin -- search for architecture pattern papers) - 05-implementierung: 2 papers (acceptable for implementation chapter) - 06-evaluation: 1 paper (critical gap -- need evaluation methodology papers)
Rules
- •NEVER add to references.bib -- only citation-gatekeeper does this
- •NEVER write \cite{} in any .tex file -- only propose papers
- •NEVER fabricate papers -- if unsure whether a paper exists, say so explicitly
- •Always provide URLs -- every paper entry must have a verifiable URL
- •Deduplicate before adding -- check all existing lists before inserting
- •Score honestly -- do not inflate relevance to make a search look productive
- •Respect rate limits -- do not hammer academic APIs; space out searches
- •Date awareness -- note when papers are preprints vs. peer-reviewed