deep-research

Deep Research

Coordinate deep technical research with intelligent caching for cross-project reuse and team knowledge sharing.

Quick Start

When research is needed:

1. Scripts path - ${CLAUDE_SKILL_DIR}/scripts/
2. Single fetch call - Run python3 ${CLAUDE_SKILL_DIR}/scripts/cache_manager.py fetch "{topic}" (combines check+get)
3. If exists=true - Present the content field directly (no agent needed). Suggest promote if valid, refresh if expired.
4. If exists=false - Invoke deep-researcher agent for EXA research, which caches via cache_manager.py put
5. Report findings - Include cache status and promote suggestion

Cache Architecture

Tier	Location	Purpose	Shared
1	~/.claude/plugins/research/	Fast, cross-project	User only
2	docs/research/	Curated, version controlled	Team

Operations

Operation	Trigger	Fast Path?	Action
Research	/research <topic> or natural language	Yes (cache hit)	Check cache → return if valid, else research → cache
Promote	/research promote <slug>	Yes	Run promote.py {slug} directly
Refresh	/research refresh <slug>	No	Spawn agent → fresh research → cache → update promoted
List	/research list	Yes	Run cache_manager.py list (project-scoped by default, --all for everything)

Project Scoping

Research entries are automatically associated with the current git repository when cached. The list operation filters by current project by default, so each project sees only its relevant research. Use --all to see everything.

• Auto-detection: Project name derived from git rev-parse --show-toplevel basename
• Multi-project: Entries can belong to multiple projects (associations merge, never replace)
• Backward compatible: Existing entries without project associations appear in --all but not in project-scoped views

Scripts

All cache operations use Python scripts in ${CLAUDE_SKILL_DIR}/scripts/:

Script	Purpose
research_utils.py	Shared utilities (imported by all scripts)
cache_manager.py	Cache CRUD: fetch, get, put, check, list, delete
promote.py	Tier 1 → Tier 2 promotion with team notes
index_generator.py	README index generation for both tiers

Slug Normalization

Convert topics to cache keys:

• "Domain-Driven Design" → domain-driven-design
• "DDD" → domain-driven-design (via alias)
• "React Hooks" → react-hooks

Output Format

After research, report:

## Research: {Topic}

**Cache:** {Hit | Miss | Expired}
**Source:** {Cached | Fresh research}
**Path:** ~/.claude/plugins/research/entries/{slug}/

[Brief summary of findings]

Run `/research promote {slug}` to add to project docs.

Agent Delegation

For actual research execution (cache miss or refresh only), delegate to deep-researcher agent:

• Has MCP tool access (EXA web search, code context)
• Uses cache_manager.py put for cache write operations
• Structures research output consistently

Additional Resources

• WORKFLOW.md - Detailed process flows
• EXAMPLES.md - Usage examples
• TROUBLESHOOTING.md - Common issues and solutions