Knowledge is not gathered — it is systematically excavated. The difference between reading papers and acquiring knowledge is the same as between tourism and cartography.
Systematic Research Knowledge Acquisition Engine for Academic Research
Knowledge Acquisition is not a literature search tool. It is a complete research intelligence pipeline. You provide a research intent — it surveys literature, mines patents, audits benchmarks, synthesizes cross-study evidence, and establishes SOTA baselines. Five autonomous campaigns, each a self-contained research activity domain with quantitative budget enforcement.
- 📚 Literature Survey — autonomous literature survey across 5 paradigms (scoping, systematic, deep, narrative, snowball). Searches, screens, reads, categorizes, identifies gaps
- ⚖️ Patent Mining — patent landscape analysis with prior art search, claim decomposition, competitive intelligence, white-space mapping, and trend tracking
- 🏋️ Benchmark Archaeology — systematic excavation of AI/ML evaluation methodology. Audits benchmark quality, detects saturation, probes construct validity, maps coverage gaps
- 📊 Meta-Analysis — cross-study statistical synthesis with effect size extraction, heterogeneity investigation, publication bias detection, and GRADE evidence assessment
- 🎯 Baseline Establishment — SOTA performance baseline collection. Method inventory, performance extraction, condition standardization, discrepancy analysis, progress quantification
Each campaign is a self-contained research activity domain. The entry point routes to the correct campaign based on user intent, then the campaign autonomously selects strategies and executes with quantitative budget enforcement.
ENTRY.md (root)
→ Campaign (5): self-contained research activity domain
→ Strategy: selected by analysis purpose/intent
→ Tactic: multi-step orchestration pattern (reusable across strategies)
→ SOP: single operation (import or subagent)Every strategy embeds quantitative floors using domain-natural units (papers read, patents analyzed, benchmarks audited, effect sizes extracted). State Ledgers track progress. Budget gates prevent premature exit.
┌───────────────────────────────────────────────────────────────┐
│ ENTRY POINT (ENTRY.md) │
│ Routes to campaign based on research intent │
├───────────────────────────────────────────────────────────────┤
│ CAMPAIGN (5) │
│ literature-survey, patent-mining, benchmark-archaeology, │
│ meta-analysis, baseline-establishment │
├───────────────────────────────────────────────────────────────┤
│ STRATEGY (25) │
│ 5 per campaign, selected by analysis purpose │
├───────────────────────────────────────────────────────────────┤
│ TACTIC (15) │
│ 3 per campaign, multi-step orchestration patterns │
├───────────────────────────────────────────────────────────────┤
│ SOP (55) │
│ Import (5 shared) + Subagent (~50) │
├───────────────────────────────────────────────────────────────┤
│ EXTERNAL SKILLS + MCP TOOLS │
│ web-browsing, literature-engine, subagent-spawning, │
│ context-management, brave-search, apify, alphaxiv, │
│ semantic-scholar │
└───────────────────────────────────────────────────────────────┘| Campaign | Strategies | Tactics | SOPs | Focus |
|---|---|---|---|---|
| literature-survey | 5 | 3 | 11 | Academic paper survey (5 paradigms) |
| patent-mining | 5 | 3 | 10 | Patent landscape analysis |
| benchmark-archaeology | 5 | 3 | 9 | Benchmark quality & validity audit |
| meta-analysis | 5 | 3 | 10 | Cross-study statistical synthesis |
| baseline-establishment | 5 | 3 | 10 | SOTA performance baseline |
| Total | 25 | 15 | 50 |
knowledge-acquisition/
├── ENTRY.md # Root entry point — campaign routing
├── skills/
│ ├── literature-survey/ # Campaign
│ ├── patent-mining/ # Campaign
│ ├── benchmark-archaeology/ # Campaign
│ ├── meta-analysis/ # Campaign
│ ├── baseline-establishment/ # Campaign
│ ├── scoping-survey/ # Strategy (literature-survey)
│ ├── systematic-survey/ # Strategy (literature-survey)
│ ├── ... # 95 more skill directories
│ └── web-search/ # Import SOP (shared)
├── tests/
│ └── integration-prompt.md
└── README.mdAll skills live flat in skills/. Organization is via frontmatter used-by field, not directory nesting.
| Dependency | Repository | What It Provides |
|---|---|---|
| web-browsing | yogsoth-ai/web-browsing | web-search + web-research |
| literature-engine | yogsoth-ai/literature-engine | paper-overview + paper-search + paper-research |
| subagent-spawning | yogsoth-ai/subagent-spawning | Subagent dispatch conventions |
| context-management | yogsoth-ai/context-management | Checkpoint protocol |
| 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/knowledge-acquisition.git-
Ensure sibling skill repos are available:
web-browsing,literature-engine,subagent-spawning,context-management -
Configure MCP servers (brave-search, apify, alphaxiv, semantic-scholar) in your Claude Code session.
-
Invoke:
/knowledge-acquisition I need to understand the patent landscape for protein language modelsThe engine routes to the correct campaign (patent-mining in this case) and executes autonomously.
Part of the Yogsoth AI ecosystem. Built by Pthahnix.