A literature survey is not reading — it is systematic cartography. You are mapping terrain that no one has drawn before, and the quality of your map determines whether the expedition succeeds or fails.
Autonomous Literature Survey Engine for Academic Research
Literature Survey is not a search assistant that finds papers for you. It is the surveyor. You provide a research question — it searches, screens, reads, categorizes, identifies gaps, and produces a structured survey output. Autonomously. Five research paradigms, each a self-contained playbook with quantitative budget enforcement.
- 🗺️ Scoping survey — broad landscape mapping of a new field. High volume (100 papers), shallow depth. Produces a hierarchical field taxonomy with key authors, trends, and open questions
- 📋 Systematic survey — exhaustive PRISMA-style coverage. Multi-stage screening, citation chaining, quality assessment. Produces comparison tables, PRISMA flow, and gap analysis
- 🔬 Deep survey — precise investigation of a specific sub-problem. Few papers, all read in full (50% deep-read rate). Produces detailed technical analysis with equations and hyperparameters
- 📝 Narrative review — theory-driven argument building. Strategic evidence selection, thematic coding. Produces a structured narrative with thesis and counter-evidence
- 🔗 Snowball — citation-chain-driven exploration from seed papers. Forward and backward tracing until saturation (67% deep-read rate). Produces a research lineage map
This is not a chatbot. After brief intake (if needed), CC selects a strategy and executes autonomously. Each strategy is a self-contained playbook with:
- A quantitative budget (hard floors, ±10% flexibility)
- Available tactics and SOPs (CC decides sequencing)
- Quality gates (inherited from imported base skills)
- A defined output format
The human provides only the research question. Everything after that — what to search, which papers to read deeply, when to stop, how to organize findings — is decided by the engine.
Strategy (5 available)
→ Budget + available tactics + execution guidance
→ CC decides: which tactics, what order, how many iterations
Tactic (3 available)
→ Multi-step orchestration pattern
→ Composes multiple SOPs into coherent workflows
SOP (17 available: 5 import + 11 subagent + 1 dialogue)
→ Single focused operation
→ Import: delegates to external skill
→ Subagent: isolated context execution
→ Dialogue: talks to user (intake only)AI agents naturally take the path of least resistance — searching a handful of papers and declaring victory. Every strategy embeds hard enforcement:
- Quantitative floors: Each SOP has a target count per strategy. CC cannot exit until ≥90% of each target is met.
- State Ledger: A progress table printed before each iteration decision — the AI cannot lose track of where it stands.
- Deviation documentation: Any departure from budget requires explicit reasoning.
┌───────────────────────────────────────────────────────────────┐
│ ENTRY POINT (/literature-survey) │
│ Routes to strategy based on user intent │
├───────────────────────────────────────────────────────────────┤
│ STRATEGY (5) │
│ scoping-survey, systematic-survey, deep-survey, │
│ narrative-review, snowball │
├───────────────────────────────────────────────────────────────┤
│ TACTIC (3) │
│ prisma-screening, citation-chaining, narrative-framing │
├───────────────────────────────────────────────────────────────┤
│ SOP (17) │
│ import (5) | subagent (11) | dialogue (1) │
├───────────────────────────────────────────────────────────────┤
│ EXTERNAL SKILLS + MCP TOOLS │
│ web-browsing, literature-engine, subagent-spawning, │
│ brave-search, apify, alphaxiv, semantic-scholar │
└───────────────────────────────────────────────────────────────┘| Category | Count | Skills |
|---|---|---|
| Strategies | 5 | scoping-survey, systematic-survey, deep-survey, narrative-review, snowball |
| Tactics | 3 | prisma-screening, citation-chaining, narrative-framing |
| Import SOPs | 5 | web-search, web-research, paper-overview, paper-search, paper-research |
| Subagent SOPs | 11 | survey-synthesis, define-search-protocol, categorize-papers, extract-data, quality-assessment, seed-selection, saturation-detection, taxonomy-mapping, prisma-flowchart, thematic-coding, gap-identification |
| Dialogue SOPs | 1 | intake-dialogue |
| Strategy | web-search | web-research | paper-overview | paper-search | paper-research |
|---|---|---|---|---|---|
| scoping-survey | 100 | 10 | 100 | 20 | 0 |
| systematic-survey | 50 | 5 | 60 | 40 | 30 |
| deep-survey | 30 | 5 | 40 | 40 | 20 |
| narrative-review | 80 | 15 | 50 | 40 | 20 |
| snowball | 20 | 3 | 30 | 30 | 20 |
All values are defaults with ±10% flexibility. CC documents deviations with reasoning.
literature-survey/
├── skills/
│ ├── literature-survey/ # Entry point — /literature-survey
│ ├── scoping-survey/ # Strategy
│ ├── systematic-survey/ # Strategy
│ ├── deep-survey/ # Strategy
│ ├── narrative-review/ # Strategy
│ ├── snowball/ # Strategy
│ ├── prisma-screening/ # Tactic
│ ├── citation-chaining/ # Tactic
│ ├── narrative-framing/ # Tactic
│ ├── web-search/ # Import SOP
│ ├── web-research/ # Import SOP
│ ├── paper-overview/ # Import SOP
│ ├── paper-search/ # Import SOP
│ ├── paper-research/ # Import SOP
│ ├── survey-synthesis/ # Subagent SOP
│ ├── define-search-protocol/ # Subagent SOP
│ ├── categorize-papers/ # Subagent SOP
│ ├── extract-data/ # Subagent SOP
│ ├── quality-assessment/ # Subagent SOP
│ ├── seed-selection/ # Subagent SOP
│ ├── saturation-detection/ # Subagent SOP
│ ├── taxonomy-mapping/ # Subagent SOP
│ ├── prisma-flowchart/ # Subagent SOP
│ ├── thematic-coding/ # Subagent SOP
│ ├── gap-identification/ # Subagent SOP
│ └── intake-dialogue/ # Dialogue SOP
├── tests/
│ └── integration-prompt.md
└── README.md| Dependency | Repository | What It Provides |
|---|---|---|
| web-browsing | yogsoth-ai/web-browsing | web-search + web-research |
| literature-engine | yogsoth-ai/literature-engine | literature-overview + literature-search + literature-research |
| subagent-spawning | yogsoth-ai/subagent-spawning | Subagent dispatch conventions |
| Server | Purpose |
|---|---|
| brave-search | Web search API |
| apify | Google Scholar scraping + full page reading |
| alphaxiv | Paper search, content extraction, PDF queries |
| semantic-scholar | Paper lookup, citations, references, recommendations |
- Clone:
git clone https://github.com/yogsoth-ai/literature-survey.git-
Ensure sibling skill repos are available:
web-browsing,literature-engine,subagent-spawning -
Configure MCP servers (brave-search, apify, alphaxiv, semantic-scholar) in your Claude Code session.
-
Invoke:
/literature-survey I need to understand all methods for protein structure prediction published since AlphaFold2Part of the Yogsoth AI ecosystem. Built by Pthahnix.