FETCH

/FETCH — Knowledge & Strategy Domain

Build Phase 2/7: RESEARCH — Gather intel before the pack moves

Lifecycle Context

Follows SCOUT (plan generated) -> feeds into SNIFF (code analysis with research context).

The primary research and strategic analysis entry point for C0RS0. Loads research scoping, documentation sourcing, decision analysis, and trade-off evaluation context into C0RS0, which then executes directly using knowledge MCP tools.

Claude -> loads FETCH context -> C0RS0 executes with fetch tool

---

Protocol

Step 1: Gather Requirements (if spec is vague)

1. Gather research-specific context:
   • Question: What question needs answering?
   • Scope: Which project, technology, or domain?
   • Decision: Is this informational research or driving a decision?
   • Depth: Quick overview or comprehensive deep dive?
   • Criteria: What factors matter most? (performance, cost, security, maintainability)
   • Constraints: Timeline, team expertise, existing stack?
2. Synthesize into a clear specification
3. Present spec for confirmation

Step 2: Execute with MCP Tools

Use mcp__C0RS0__corsoTools with action: "fetch" for research, knowledge retrieval, and knowledge graph queries, applying all research intelligence context below.

---

Quality Gates

Pre-Execution

• Research scope classified
• Documentation sources identified for target language/framework
• Search strategy selected based on query intent
• Decision factors defined (if decision-driving research)

Post-Execution

• All findings sourced with citations
• Recommendations include rationale
• Trade-off analysis presented (if applicable)
• All acceptance criteria met

---

Research Scope Classification (from knowledge.rs:infer_research_scope)

Classify the research query to determine scope and depth:

Query Type	Keywords	Scope
API documentation	api, endpoint, interface, method	Targeted API specs and examples
Tutorial/guide	how to, tutorial, guide, getting started	Step-by-step guides, official tutorials
Code examples	example, sample, snippet, demo	Repository search, docs code blocks
Best practices	best practice, pattern, idiom, convention	Community patterns, official guidelines
Security	CVE, vulnerability, security, audit	CVE databases, security advisories
Architecture	design, architecture, structure, pattern	Design docs, architecture decision records

---

Documentation Sources (from knowledge.rs:select_documentation_sources)

Rust:

• doc.rust-lang.org (std library, book, reference)
• crates.io (package registry, README)
• docs.rs (auto-generated API docs)
• Rust by Example, Async Book

Python:

• docs.python.org (official docs, PEPs)
• PyPI (package metadata)
• Real Python (tutorials)

JavaScript/TypeScript:

• MDN Web Docs (language reference)
• npm (package registry)
• TypeScript Handbook

Go:

• pkg.go.dev (official package docs)
• Go Blog (design decisions)
• Effective Go

---

Search Strategy (from knowledge.rs:determine_search_strategy)

Query Intent	Strategy
Latest/current	Focus on recent 6 months, official sources first
Specific API	Targeted function/method search, API docs
Comparison	Multi-source research, pros/cons analysis
Comprehensive	Deep dive, multiple angles, cross-reference
Quick answer	Overview, top results, summary
Code examples	Repository search, docs code blocks, playground

---

Decision Factors (from wisdom.rs:identify_decision_factors)

When analyzing architecture or technology decisions, apply these factors:

Factor	Trigger Keywords	Weight
Performance	optimize, speed, latency, throughput	High for systems/backend
Cost	budget, pricing, resources, ROI	High for cloud/infra
Security	security, threat, vulnerability, auth	Always high
Scalability	scale, growth, load, concurrent	High for distributed
Maintainability	maintain, technical debt, readability	Always consider
Team expertise	team, learning, familiar, ramp-up	Pragmatic factor
Timeline	deadline, sprint, MVP, urgent	Schedule pressure

Language-Specific Decision Factors (from wisdom.rs):

• Rust: Performance, memory safety, compile-time guarantees, zero-cost abstractions
• Python: Development speed, ecosystem breadth, prototyping velocity
• JavaScript/TypeScript: Browser compatibility, ecosystem size, full-stack capability
• Go: Simplicity, concurrency model, deployment ease, compile speed

---

Trade-Off Analysis (from wisdom.rs:analyze_trade_offs)

Universal Trade-Offs:

• Performance vs Simplicity
• Security vs Convenience
• Flexibility vs Constraints
• Speed vs Quality
• Features vs Stability

Language-Specific Trade-Offs:

• Rust: Safety & Performance vs Learning Curve
• Python: Development Speed vs Runtime Performance, Dynamic Typing vs Type Safety
• JavaScript/TypeScript: Runtime Flexibility vs Type Safety, Bundle Size vs Feature Richness
• Go: Simplicity vs Expressiveness, Goroutine Ease vs Resource Management

Framework Trade-Offs:

• Microservices: Distributed complexity vs deployment flexibility
• Async: High throughput vs code complexity

---

Wisdom Domain Classification (from wisdom.rs:determine_wisdom_domain)

Domain	Trigger Keywords
Architectural	architecture, design, structure
Algorithmic	algorithm, data structure, complexity
Organizational	team, organization, process
Technology Selection	technology, stack, tool, library
Scalability	scale, growth, millions, distributed
Technical Debt	refactor, technical debt, legacy
Security & Compliance	security, compliance, audit, GDPR
Performance	performance, optimize, benchmark

---

Cross-Domain Context

When	Skill Context	MCP Tools
Research reveals security concerns	GUARD	corsoTools action: guard
Research evaluates codebase patterns	SNIFF	corsoTools action: code_review
Research drives performance comparison	CHASE	corsoTools action: chase

---

MCP Tools Available

corsoTools Action	Purpose
fetch	Research & knowledge retrieval + knowledge graph queries