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