Research a specific person using Common Room data. Triggers on 'who is [name]', 'look up [email]', 'research [contact]', 'is [name] a warm lead', or any contact-level question.

Contact Research Retrieve a comprehensive contact profile from Common Room. Supports lookup by email, social handle, or name + company. Returns enriched data including activity history, Spark, scores, website visits, and CRM fields. Step 1: Locate the Contact Common Room supports multiple lookup methods — use whichever the user has provided: What the user gives Lookup method Email address Look up by email (most reliable) LinkedIn, Twitter/X, or GitHub handle Look up by social handle — specify handle type explicitly Name + company Identity resolution by name + org domain; present matches if ambiguous Name only Search by name; if multiple matches, show a brief list and ask the user to confirm If no match is found, respond: "Common Room doesn't have a record for this person." Do not speculate or fabricate profile data. Step 2: Fetch Contact Fields Use the Common Room object catalog to see available field groups and their contents. For full profiles, request all groups. For targeted questions, request only what's relevant. Key field groups to know about: Scores — always return as raw values or percentiles, never labels Recent activity — use Contact Initiated filter (last 60 days) for their actions, not your team's Website visits — total count + specific pages (last 12 weeks) Spark — retrieve all Sparks when tracking engagement evolution over time Step 3: Run Spark Enrichment (If Available) If Spark is available, use it. Spark provides: Professional background and job history Social presence and influence signals Persona classification: Champion, Economic Buyer, Technical Evaluator, End User, or Gatekeeper Inferred role in the buying process If Spark is unavailable but real activity data exists (recent actions, website visits, community engagement), infer a persona from those signals. If neither Spark nor activity data is available, classify as Unknown — do not guess a persona from title alone. Retrieve all Sparks (not just the most recent) when the user wants to understand how this contact's engagement has evolved over time. Step 4: Assess Account Context Pull an abbreviated account snapshot for this contact's parent company. Note: Open opportunities, expansion signals, or churn risk at the account level Whether other contacts at this company are also active How this person's engagement compares to their colleagues Step 5: Identify Conversation Angles Based on activity and signals, surface the strongest 2–3 hooks: A recent Contact Initiated activity (community post, product event, support ticket) A specific web page they visited recently — especially if it signals evaluation intent A job change, promotion, or company news Their Spark persona and what that suggests about communication style Their role in a known active deal Output Format Only include sections where data was actually returned. Omit sections with no data rather than filling them with guesses. When data is rich: When data is sparse (e.g., only name, title, email, tags returned; sparkSummary is null): Do not generate conversation starters, persona inferences, or engagement assessments from sparse data. These require real signals. Quality Standards Lookup must use the correct method for the input type — don't guess on email vs. handle Scores as raw/percentile only — never labels Contact Initiated activity (last 60 days) is the primary engagement signal — lead with it If Spark is unavailable, say so — don't fabricate a persona from title alone Flag any contact where the most recent activity is older than 30 days Reference Files references/contact signals guide.md — full field descriptions, Spark persona guide, and conversation starter principles

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