Call Prep
AnthropicOfficialCreated by Common RoomDistributed by Anthropic
- Skill
- Content generator
- Business and productivity
- Apache-2.0
- No issues found
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
Call Prep synthesizes data from Common Room to create actionable meeting briefs. It identifies account status, attendee profiles, and recent signals to help users prepare for various meeting types, including discovery, demos, renewals, and QBRs. The skill integrates with calendar connectors to identify attendees and supplements Common Room data with targeted web searches to ensure recent company or contact news is included.
What it does
- Parses meeting context to identify accounts and attendees
- Aggregates account research including product signals and recent activity
- Conducts contact research to identify attendee roles and engagement history
- Synthesizes signal data into tailored talking points and potential objections
- Performs supplementary web searches for recent company or attendee news
- Formats output into a scannable brief based on available data density
When to use it
- Before a discovery or demo call with a new prospect
- When preparing for an expansion or renewal conversation with an existing customer
- Prior to a Quarterly Business Review to align on strategy
- When managing a risk or save call to address negative account signals
Advantages
- Grounds talking points in specific, real-world signal data
- Provides a consistent, scannable structure for meeting preparation
- Adapts content depth based on the amount of available data
- Helps identify potential objections before the meeting starts
Usage examples
- “prep me for my call with Acme Corp”
- The agent retrieves account signals and contact details from Common Room to generate a structured brief including talking points and potential objections.
- “what should I know before talking to Jane Doe at TechFlow?”
- The agent researches Jane Doe's role and recent activity, combined with TechFlow's account status, to provide a focused profile for the upcoming meeting.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/anthropics/call-prep/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip call-prep.zip -d .agents/skills
Documentation by Common Room
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 Standards
Ground every talking point in a real signal — no generic filler
Keep the brief tight — it should be readable in 5 minutes or less
Flag unknowns explicitly — if attendee research is thin, say so
Time-box the research — don't over-research at the expense of speed
Never invent deal context — no fabricated proposals, competitor comparisons, pricing, trial terms, or objections not returned by a tool call
Reference Files
references/call-types-guide.md— guidance for different call types (discovery, expansion, renewal, QBR) and how to tailor prep accordingly
Files
- ATTRIBUTION.mdAdded by AutoGineer1.1 KiB
- LICENSELicence file added by AutoGineer11.1 KiB
- SKILL.md5.8 KiB
- references/call-types-guide.md2.2 KiB
LICENSE
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Requirements and compatibility
Written for: Claude Code, Claude apps
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Security
No issues found by our static scan· No issues found
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Attribution and licence
- Created by
- Common Room
- Publisher
- Anthropic (Official)
- Distributed by
- Anthropic
- Source
- anthropics/knowledge-work-plugins · partner-built/common-room/skills/call-prep · commit e050c2e
- Licence
- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from partner-built/common-room/LICENSE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
AutoGineer is not affiliated with or endorsed by Common Room or Anthropic.
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