majiayu000 Context Synthesis
Efficient multi-source context gathering that minimizes token usage while maximizing relevant information.
When to Use
- Starting stakeholder discovery/interviews
- Researching new features or domains
- Building context for analysis tasks
- Synthesizing information from multiple sources
Core Principle
Gather silently, synthesize briefly, share relevantly.
Token efficiency comes from:
- Parallel MCP tool calls (not sequential)
- Filtering irrelevant results before presenting
- Structured summaries over raw dumps
Context Gathering Pattern
Step 1: Parallel Information Retrieval
Execute these in parallel (single tool call block):
# All four in parallel - not sequential
mcp__plugin_claude-mem_mem-search__search(query="{keyword}")
mcp__serena__list_memories()
Glob(pattern="**/features/*_FEATURE.md")
WebSearch(query="{domain} best practices 2025")
Step 2: Selective Deep Reads
Based on Step 1 results, read only high-relevance items:
# Only if memory mentions relevant topic
mcp__serena__read_memory(memory_file_name="relevant_memory")
# Only if glob found matching specs
Read(file_path="/path/to/relevant/*_FEATURE.md")
# Only if search returned actionable results
WebFetch(url="most_relevant_url", prompt="extract specific info")
Step 3: Structured Synthesis
Present findings in structured format:
**Context Summary** ({feature/topic})
| Source | Key Finding | Relevance |
|--------|-------------|-----------|
| Memory | Past decision X | Direct |
| Spec FEATURE_A | Similar pattern Y | Reference |
| Web | Industry trend Z | Background |
**Implications for Current Task:**
- [Key implication 1]
- [Key implication 2]
Source Priority Order
| Priority | Source | When to Use | Token Cost |
|---|---|---|---|
| 1 | claude-mem | Always first | Low |
| 2 | serena memories | Project context | Low |
| 3 | Existing specs | Pattern reference | Medium |
| 4 | WebSearch | Industry context | Medium |
| 5 | WebFetch | Deep dive needed | High |
Anti-Patterns
| Anti-Pattern | Problem | Better Approach |
|---|---|---|
| Sequential tool calls | Slow, inefficient | Parallel execution |
| Reading all files | Token waste | Selective deep reads |
| Dumping raw results | Cognitive overload | Structured synthesis |
| Skipping memory check | Miss past decisions | Always check first |
| WebFetch everything | High token cost | Only for high-value URLs |
Integration with Other Skills
With requirements-discovery
1. context-synthesis gathers background
2. requirements-discovery conducts interview
3. Context informs question prioritization
With architecture
1. context-synthesis gathers existing patterns
2. architecture analyzes against patterns
3. Context validates decisions
Quick Reference
# Minimal context check (fast)
mcp__plugin_claude-mem_mem-search__search(query="{topic}")
mcp__serena__list_memories()
# Standard context gathering (balanced)
# Add: Glob for existing specs, WebSearch for trends
# Deep context research (comprehensive)
# Add: WebFetch for detailed sources, multiple memory reads