Call Prep

# Call Prep

Produce a complete, scannable call prep brief by combining account research, contact research, and signal synthesis from Common Room.

## Prep Process

### Step 1: Identify the Account and Attendees

Parse what the user has provided: - **Company name** — required; look up the account in Common Room - **Attendee names** — optional; if provided, research each one

**Calendar lookup:** If a `~~calendar` connector is available, search for upcoming meetings with the named company to automatically surface attendee names, meeting time, and any meeting notes or agenda. Use this to fill gaps the user didn't provide.

If neither attendees nor a calendar match can be found, ask: "Who will be on the call from [Company]? I can research each attendee to make your prep more useful."

### Step 2: Run Account Research

Use the account-research skill process to build a full account snapshot. For call prep, prioritize: - Recent product signals (what are they doing in the product right now?) - Open opportunities or renewal timeline - Any risk signals (declining usage, support tickets, churned seats) - Key recent events (funding, executive change, new hire)

When reviewing activity history, prioritize Gong and call recording activities — these provide direct context about previous conversations. Do not filter out call recordings by activity origin.

### Step 3: Run Contact Research for Each Attendee

For each external attendee, use the contact-research skill process. For call prep, focus on: - Role and influence in the buying process - Their personal activity and engagement history - Any recent signals that suggest their current mood/priorities - Spark persona classification if available

### Step 4: Synthesize Talking Points and Objectives

Based on the combined account and contact research: - Identify the **call objective** (e.g., discovery, demo, expansion conversation, renewal, QBR) - Generate **3–5 tailored talking points** grounded in specific signal data - Anticipate **2–3 likely objections or topics** the customer may raise - Suggest a **recommended outcome** for the call

When the user's company context is available (see `references/my-company-context.md`), tailor talking points to the user's product and value proposition.

### Step 5: Recency Check (Web Search)

After gathering all Common Room data, run a quick recency check to catch anything that happened since the last CR data sync. This is supplementary — CR data drives the prep; web search only adds recency.

**Company news:** Search `"[company name]" news` filtered to the last 14 days. Look for funding announcements, product launches, leadership changes, layoffs, partnerships, or press coverage.

**Attendee presence:** For each external attendee, search `"[full name]" "[company name]"` — look for recent articles, LinkedIn posts, conference talks, podcasts, or published opinions.

If a company news item is significant (e.g., just raised a round, announced a major hire), flag it in Signal Highlights. Otherwise, include findings briefly — don't let web search results overshadow CR signals.

## Output Format

The output adapts to how much data Common Room returned. Only include sections where you have real data. Never fill a section with invented details.

### When data is rich (multiple field groups returned, activity history, scores, signals):

``` ## Call Prep: [Company] — [Date/Time if known]

**Meeting Context** [Attendees, meeting type, and any known agenda]

---

### Company Snapshot [4–6 bullets: key account status, signals, and recent activity]

---

### Attendee Profiles

**[Attendee Name] — [Title]** [3–4 bullets: role, recent activity, Spark persona if available, personal hook]

[Repeat for each attendee]

---

### Signal Highlights [Top 3 signals most relevant to this specific call]

---

### Talking Points 1. [Point tied to a specific signal] 2. [Point tied to a specific signal] 3. [Point tied to a specific signal]

### Likely Topics / Objections to Prepare For - [Topic or objection + suggested response] - [Topic or objection + suggested response]

### Recommended Call Outcome [1–2 sentences: what success looks like for this meeting] ```

### When data is sparse (few fields returned, no activity, null sparkSummary):

``` ## Call Prep: [Company] — [Date/Time if known]

**Data available:** [List exactly what Common Room returned — e.g., "Name, title, email, two tags. No activity history, no scores, no Spark data."]

### What I Found [Only the fields actually returned, presented as-is]

### Web Search Results [Findings from web search on the company and attendees — or "No significant results"]

### Suggested Next Steps - I can pull [specific field groups] from Common Room if available - I can run deeper web searches on [specific topics] - You may want to check Common Room directly for [what's missing] ```

Do not generate a full call prep brief from sparse data. A short honest output is always better than a long fabricated one.

## Quality St