Agent skill

Call Prep

by w95 in w95/awesome-claude-corporate-skills

Prepare for a customer or prospect call using Common Room signals.

MITAuto-check passedSales & Support

Install Call Prep

skills CLI
$ npx skills add w95/awesome-claude-corporate-skills --skill call-prep -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install w95/awesome-claude-corporate-skills call-prep --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/w95/awesome-claude-corporate-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/05-sales/call-prep-common-room .claude/skills/call-prep && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
call-prep
GitHub stars
235
Token cost
~1.5k tokens
SKILL.md length
601 words
Files
2 (incl. references)
Skills in repo
43
Repo updated
First seen
Licence
MIT

At a glance

Prepare for a customer or prospect call using Common Room signals.

  • Works in 5 steps: Identify the Account and Attendees → Run Account Research → Run Contact Research for Each Attendee → …
  • Prep me for my call with [company]
  • SKILL.md covers Prep Process, Output Format, Quality Standards and Reference Files
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Call Prep is an agent skill from w95/awesome-claude-corporate-skills. Prepare for a customer or prospect call using Common Room signals. Triggers on 'prep me for my call with [company]', 'prepare for a meeting with [company]', 'what should I know before talking to [company]', or any call preparation request.

Its SKILL.md is about 1.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including reference files (for example `references/call-types-guide.md`).

It sits in Sales & Support, covering Sales call preparation. The repository describes itself as: 166 production-ready Claude AI skills organized by corporate role — executive leadership, finance, HR, marketing, sales, legal, operations, engineering, product, data, customer…. The licence is MIT.

When your agent uses it

  • Prep me for my call with [company]
  • Prepare for a meeting with [company]
  • What should I know before talking to [company]
  • Any call preparation request

Example prompts

  • “prep me for my call with [company]”
  • “prepare for a meeting with [company]”
  • “what should I know before talking to [company]”
  • “/call-prep”

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Identify the Account and Attendees
  2. Run Account Research
  3. Run Contact Research for Each Attendee
  4. Synthesize Talking Points and Objectives
  5. Recency Check (Web Search)

What it can do on your machine

Read from SKILL.md and the folder at commit 78dbc7c. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Call Prep loads about 1.5k tokens when it runs, and up to ~2k if it reads all its reference files. Until then it costs about 62 tokens; SKILL.md has 601 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~62
When it runs · the whole SKILL.md, loaded when a task matches
~1.5k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from w95/awesome-claude-corporate-skills at commit 78dbc7c, republished under its MIT licence (© w95). 601 words, ~1,469 tokens.

Download SKILL.mdSave it as .claude/skills/call-prep/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
call-prep
description
Prepare for a customer or prospect call using Common Room signals. Triggers on 'prep me for my call with [company]', 'prepare for a meeting with [company]', 'what should I know before talking to [company]', or any call preparation request.

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.

Show full SKILL.md (279 more words)Show less

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

© w95, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file (references) in 05-sales/call-prep-common-room of w95/awesome-claude-corporate-skills.

  • SKILL.md
  • references/call-types-guide.md

Open the folder on GitHubat commit 78dbc7c

Compare with similar skills

Call Prep next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Call Prep compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Call Prep this skillw95/awesome-claude-corporate-skills235—~1.5kAutomated safety check: PassMIT
Meeting Prep BriefBrianRWagner/ai-marketing-claude-code-skills4401 repos~922Automated safety check: PassNone
Luopan Company Researchzhangxiaoqiang1991/luopan388—~998Automated safety check: PassMIT
Company Researchstophobia/deerflow2.0-enhanced820—~845Automated safety check: PassMIT
SdtStopDisTrain/sdt-skills309—~535Automated safety check: PassMIT
Account Researchextruct-ai/gtm-skills109—~1.6kAutomated safety check: PassNone

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Categories

Questions about Call Prep

What does Call Prep do?

Prepare for a customer or prospect call using Common Room signals. Call Prep is an agent skill from w95/awesome-claude-corporate-skills. Prepare for a customer or prospect call using Common Room signals.

When should I use Call Prep?

Call Prep fits situations like: prep me for my call with [company]; prepare for a meeting with [company]; what should I know before talking to [company]; any call preparation request.

How do I install Call Prep in Claude Code?

Run `npx skills add w95/awesome-claude-corporate-skills --skill call-prep -a claude-code`. Or copy the skill folder (05-sales/call-prep-common-room in w95/awesome-claude-corporate-skills) into .claude/skills/call-prep in your project. Claude Code loads it when a task matches its description.

How do I install Call Prep in Codex?

Run `npx skills add w95/awesome-claude-corporate-skills --skill call-prep -a codex`. Or copy the skill folder (05-sales/call-prep-common-room in w95/awesome-claude-corporate-skills) into .agents/skills/call-prep in your project. Codex loads it when a task matches its description.

Can I use Call Prep in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add w95/awesome-claude-corporate-skills --skill call-prep -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/call-prep, .gemini/skills/call-prep, .github/skills/call-prep and .opencode/skills/call-prep in your project.

What does Call Prep need to run?

SKILL.md names no scripts, command-line tools or credentials: Call Prep is instructions for the agent only.

Does Call Prep access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Call Prep safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Call Prep use?

Call Prep is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Call Prep use?

About 1.5k tokens (SKILL.md is roughly 5.9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 556 tokens, read only when the agent opens those files.

What are the alternatives to Call Prep?

Skills that share tags, products or a category with Call Prep: Meeting Prep Brief (BrianRWagner/ai-marketing-claude-code-skills, 440 stars), Luopan Company Research (zhangxiaoqiang1991/luopan, 388 stars), Company Research (stophobia/deerflow2.0-enhanced, 820 stars) and Sdt (StopDisTrain/sdt-skills, 309 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Call Prep?

w95 (a GitHub user) maintains it in w95/awesome-claude-corporate-skills, which has 235 GitHub stars. The repository holds 43 skills in this directory. The repository was last updated on February 26, 2026.

Source: w95/awesome-claude-corporate-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.