contact-research

Q: How do I install contact-research?

Run npx skills add https://github.com/anthropics/knowledge-work-plugins --skill contact-research in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/contact-research/ (personal) or .claude/skills/contact-research/ (project).

by anthropics OfficialProductivity & Automation

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.

Installs: 2.9K
Last 24h: —
Trending rank: —
Security: Not audited

Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/anthropics/knowledge-work-plugins --skill contact-research

01
Install
Run the command in your project. The CLI asks which agents to install for.
02
Describe the task
Ask your agent for something this skill covers. It loads contact-research automatically when relevant.
03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

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:

## [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

FAQ

What does the contact-research skill do?

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.

How do I install contact-research?

Run npx skills add https://github.com/anthropics/knowledge-work-plugins --skill contact-research in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/contact-research/ (personal) or .claude/skills/contact-research/ (project).

Which AI agents can use contact-research?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is contact-research safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains contact-research?

It is published by anthropics in anthropics/knowledge-work-plugins, and it is an official skill from the maker of the technology it covers. It has 2,863 recorded installs.