/dm:recall

Purpose

Retrieve relevant learnings from the brand's compound intelligence graph. Given a context — channel, audience, objective, or situation — return the most relevant validated insights ranked by confidence and recency. Turns accumulated marketing knowledge into an actionable playbook for any scenario, so past learnings directly inform current decisions without relying on memory or searching through old reports.

Input Required

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

• Query context: The situation to recall learnings for — specified as one or more of the following dimensions: channel (email, social, paid search, SEO, content, SMS, etc.), audience segment (developers, marketers, executives, SMB owners, enterprise buyers, etc.), objective (awareness, conversion, retention, upsell, win-back, etc.), campaign type (product launch, seasonal, evergreen, nurture, event, etc.), or a freeform situation description that captures the scenario in natural language (e.g., "planning a Black Friday email campaign targeting lapsed customers" or "launching a new product to a developer audience via content marketing")
• Confidence threshold (optional): Minimum confidence score to include — defaults to 0.3 (includes hypotheses and above). Set to 0.7+ for only validated insights, or 0.0 to see everything including early-stage observations
• Time range (optional): Filter learnings by when they were recorded — "last 30 days", "this quarter", "all time" (default). Recent learnings may be more relevant for fast-changing channels like paid social, while evergreen learnings about audience psychology may be valuable regardless of age
• Max results (optional): Number of learnings to return — defaults to 10. Increase for comprehensive research or decrease for quick decision support

Process

1. Load brand context: Read ~/.claude-marketing/brands/_active-brand.json for the active slug, then load ~/.claude-marketing/brands/{slug}/profile.json. Apply brand industry, audience segments, and active channels to contextualize the query and boost relevance of matching learnings. Check for agency SOPs at ~/.claude-marketing/sops/. If no brand exists, ask: "Set up a brand first (/dm:brand-setup)?" — or proceed with defaults.
2. Query the intelligence graph: Execute intelligence-graph.py query-relevant with the provided context dimensions. The query matches against all indexed conditions — channel, audience, objective, campaign type — and also performs semantic matching for freeform situation descriptions. Apply confidence threshold and time range filters.