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search-knowledge

Search the brand's stored knowledge and memory, ranked with provenance. "what are our brand voice guidelines"

plugins/indranilbanerjee/digital-marketing-pro/skills/search-knowledge/SKILL.md

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# /digital-marketing-pro:search-knowledge

> **Script location.** If your host does not set `${CLAUDE_PLUGIN_ROOT}`, the scripts are in this plugin's `scripts/` folder, next to `skills/`. Run them with Python 3.10 or newer: `python`, or `python3` where `python` is not found (macOS, many Linux systems).

## Purpose

Semantic search across all stored brand knowledge in the vector database and knowledge graph. Answers questions like "What worked for email in Q4?", "What are our brand voice guidelines?", "Show me learnings about audience X", or "What did we learn about competitor Y's pricing?" Returns relevant entries ranked by similarity with full provenance context, so agents and users can make decisions informed by everything the brand has ever learned — not just what they remember from the current session. Searches all connected memory layers simultaneously: vector DB for semantic similarity, knowledge graph for entity relationships, and local index for un-synced recent entries.

## Input Required

The user must provide (or will be prompted for):

- **Search query**: Natural language question or topic — e.g., "What email subject line patterns drove the highest open rates?", "What are our compliance restrictions for the EU market?", "Show me everything we know about competitor X", or "What campaign strategies worked for audience millennials in Q4?" The query is embedded and matched semantically, so exact wording does not need to match stored content
- **Content type filter (optional)**: Narrow results to a specific type — `guideline`, `campaign-learning`, `competitive-intel`, `performance-insight`, or `brand-asset`. Omit to search all types. Multiple types can be specified as a comma-separated list