Contact Research

From the author

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.

— the description by Common Room

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill enables users to perform deep research on specific contacts by pulling data from Common Room. It aggregates activity history, engagement scores, website visits, and persona-based Spark data to provide a holistic view of a lead or customer. The skill is designed to help users identify conversation hooks and assess account-level context, ensuring that outreach is informed by real behavioral signals rather than assumptions.

What it does

Performs contact lookups via email, social handles, or name and company combinations.
Retrieves enriched contact data including activity history, engagement scores, and website visits.
Provides persona classification using Spark data to identify roles like Champion or Economic Buyer.
Summarizes account-level context, including open opportunities and colleague engagement.
Generates actionable conversation starters based on specific recent activities or signals.
Handles sparse data scenarios by clearly identifying missing information and suggesting further research.

When to use it

When preparing for a sales call or outreach to a specific prospect.
When you need to understand a contact's recent engagement history with your organization.
When you want to identify the best angle to re-engage a dormant contact.
When you need to determine a contact's role and influence within their organization.

Advantages

Reduces manual research time by centralizing data from multiple channels.
Prevents fabrication of data by strictly separating rich profiles from sparse ones.
Ensures outreach is relevant by surfacing specific, signal-backed conversation hooks.
Provides consistent, objective reporting of engagement scores and persona signals.

Usage examples

“who is Sarah Jenkins at Acme Corp?”: The agent retrieves Sarah's profile, including her Spark persona, recent community activity, and a summary of her company's current account status.
“is John Doe a warm lead?”: The agent analyzes John's recent activity, website visits, and engagement scores to provide an assessment of his current interest level and potential conversation starters.

Install

Download the skill as a ZIP:
https://autogineer.com/agent-skills/anthropics/contact-research/download

Unzip it into the cross-client skills folder of your project:

mkdir -p .agents/skills && unzip contact-research.zip -d .agents/skills

Documentation by Common Room

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:

## [Contact Name] — Profile

**Overview**
[2 sentences: who they are, their role, and relationship status]

**Details**
- Title: [title]
- Company: [company]
- Email: [email]
- LinkedIn: [URL]
- Other profiles: [Twitter/X, GitHub, CRM link if available]

**Scores** [If scores returned]
[All scores as raw values or percentiles]

**Recent Activity** (last 60 days) [If activity returned]
[3–5 bullets with dates]

**Website Visits** (last 12 weeks) [If visit data exists]
[Total visit count + list of pages visited]

**Spark Profile** [If Spark data is non-null]
[Persona type, background summary, influence signals]

**Segments** [If segments returned]
[List of segment names this contact belongs to]

**Account Context**
[1–2 sentences on their company's status]

**Conversation Starters**
[2–3 specific, signal-backed openers]

When data is sparse (e.g., only name, title, email, tags returned; sparkSummary is null):

## [Contact Name] — Profile (Limited Data)

**Data available:** [List exactly what Common Room returned]

[Present only the returned fields]

**Web Search**
[Any findings from searching their name + company]

**Note:** Common Room has limited data on this contact. No activity history, scores, or Spark profile available. I can run deeper web searches or look up their company for additional context.

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

Files

ATTRIBUTION.mdAdded by AutoGineer1.1 KiB
LICENSELicence file added by AutoGineer11.1 KiB
SKILL.md5.3 KiB
references/contact-signals-guide.md2.3 KiB

SKILL.md