From search-research
ALWAYS use this skill for explore queries - unified search across your local data (chat history, knowledge base, code, docs) AND the web with intelligent source selection.
How this skill is triggered — by the user, by Claude, or both
Slash command
/search-research:allThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Single entry point to search **EVERYTHING** - your local data (chat history, knowledge base, code, docs) AND the web (current documentation, best practices, Stack Overflow). Results are merged and ranked by relevance, then intelligently filtered via three-layer architecture.
README.md__init__.pyadaptive_limits.pyagent_filter.pycomplete_three_layer_implementation.pyexecute_three_layer_search.pyexplore.pyfiltering.pylayer2_filter.pyorchestration.pyproduction_three_layer.pyquery_complexity.pyreferences/implementation-details.mdreferences/inline-execution-code.mdreferences/migration-notes.mdreferences/output-format.mdreferences/performance-tuning.mdreferences/troubleshooting.mdreferences/usage-examples.mdsearch_executor.py/explore)Single entry point to search EVERYTHING - your local data (chat history, knowledge base, code, docs) AND the web (current documentation, best practices, Stack Overflow). Results are merged and ranked by relevance, then intelligently filtered via three-layer architecture.
| Command | Searches | Speed | When To Use |
|---|---|---|---|
/search | Local data only | <1s | "What did we discuss?" |
/research | Web only | 5-10s | "What's the current best practice?" |
/explore | Both + merged results | 1-10s | "I want to see everything" |
| Layer | Responsibility | When Applied |
|---|---|---|
| Layer 1A | Volume control, deduplication, quality floor | Always |
| Layer 1B | Semantic clustering (Jaccard similarity) | Always |
| Layer 1C | Query complexity scoring | Always |
| Layer 1D | Adaptive result limits (token-aware) | Always |
| Layer 2 | Semantic relevance via Agent tool (LLM-based) | Conditional (auto-triggers) |
| Layer 3 | Presentation formatting | Always |
Layer 2 Auto-triggers when:
--context-threshold N)"we discussed", "for the X feature")User overrides:
--no-context-filter: Skip Layer 2--force-context-filter: Force Layer 2 even for small result sets--context-threshold N: Adjust trigger threshold (default: 20)This skill executes inline Python code (no subprocess). The Agent tool is ONLY available in skill execution context.
Full inline execution code: See
references/inline-execution-code.mdfor the complete Python source that handles three-layer filtering orchestration.
/explore "python async patterns" # Auto-filtering (default)
/explore "what did we decide about auth" # Context-aware (auto-triggers Layer 2)
/explore "best practices" --mode unified # Force local + web
/explore "what did we discuss" --mode local-only # Fast, no web APIs
/explore "query" --no-context-filter # Layer 1 only
/explore "microservices patterns" --force-context-filter # Always apply Layer 2
More examples and advanced options: See
references/usage-examples.md
Results display as [score] SOURCE: title with preview text. When Layer 2 activates, results are grouped by theme with key insights extracted per group.
Full output format examples: See
references/output-format.md
| Mode | Local | Web | Total | Use Case |
|---|---|---|---|---|
auto | <1s | 0-10s | 0-11s | Default, adaptive |
unified | <1s | 5-10s | 5-11s | Comprehensive |
local-only | <1s | 0s | <1s | Fast, private |
web-fallback | <1s | 0-10s | 0-11s | Quality-focused |
Layer 1 total: <1 second. Layer 2: <5 seconds when triggered.
| File | Contents |
|---|---|
references/inline-execution-code.md | Full Python execution source code |
references/implementation-details.md | Layer behavior, trigger conditions, error handling |
references/usage-examples.md | Usage examples and advanced CLI options |
references/output-format.md | Output format examples (Layer 1 vs Layer 2) |
references/migration-notes.md | Migration from /search + /research workflow |
references/troubleshooting.md | Common issues and solutions |
references/performance-tuning.md | Speed vs coverage tuning guide |
npx claudepluginhub enduser123/search-researchGuides users through a choose-your-own-adventure writing process that turns raw markdown material into a structured article by grounding concepts before they are used.