Cold Call Prep logo

Cold Call Prep

OrganizationPopular
anthropics
cold-call-prep

Prep for a cold-call — predict the professor's likely questions and drill them Socratically, flagging where you're shaky so you know what to re-read before class. Use when the user says "prep for class tomorrow", "cold call [case]", "what might [professor] ask on", or points at assigned reading.

Overview

Publisheranthropics
Repositoryclaude-for-legal
Skill namecold-call-prep
Stars
9.6K
Forks
1.9K
Bundled files
Instructions only
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by anthropics on GitHub. Read the source before you install it.

Installation

Install the Cold Call Prep AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/anthropics/claude-for-legal/tree/main/law-student/skills/cold-call-prep
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/anthropics/claude-for-legal.git /tmp/claude-for-legal
mkdir -p .claude/skills
cp -r /tmp/claude-for-legal/law-student/skills/cold-call-prep .claude/skills/cold-call-prep
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Cold Call Prep in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Cold Call Prep on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Cold Call Prep is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

/cold-call-prep

  1. Load ~/.claude/plugins/config/claude-for-legal/law-student/CLAUDE.md → class list, professors, learning style.
  2. Apply the workflow below.
  3. Identify reading (case name + citation, professor, class, syllabus context).
  4. Predict 6-10 likely questions across categories (Facts / Holding / Reasoning / Application / Policy), weighted to professor's known tendencies.
  5. Drill using socratic pattern — ask, wait, push back, narrow when stuck. Don't give answers.
  6. Post-drill summary: strong/shaky/missed; what to re-check before class.

Real-matter check

If the question the student is asking sounds like it's about a REAL situation — their lease, their parking ticket, their family's business, their friend's arrest, a real dollar amount, a real deadline, a real party name — stop.

"This sounds like a real situation, not a hypothetical. I can't give you legal advice, and you can't give it either — you're not a lawyer yet. If this is real, [the person] needs an actual lawyer: legal aid, your school's clinic, a lawyer referral service (your jurisdiction's bar association, law society, or legal aid body), or (if there's money) a private attorney. I'm happy to help you understand the general legal concepts involved, but that's study, not advice."

Watch for: real names, real addresses, real dates, specific dollar amounts, "my landlord/boss/parent/friend," "I got a ticket/letter/notice," deadlines measured in days. Any one of these is a trigger.

Purpose

Cold-calling lives or dies on preparation. The professor has read the case dozens of times and knows the questions; the student has read it once. This skill narrows the gap — predicts the likely question patterns for the case, drills the student on them, and surfaces what they haven't locked in.

Not a replacement for reading the case. A test that you actually did.

Confidence discipline

  • When the student provides case text or casebook excerpts: I predict questions based on the actual text. Confident.
  • When the student provides only a case name: I predict based on what I know about the case. Flag [UNCERTAIN] on any question that depends on case details I'm not sure of. Strongly recommend the student pastes the case or casebook treatment first.
  • If I don't know the case well: say so. "I don't have a reliable read on this case — paste the text or casebook treatment and I can work from that. Otherwise my questions are educated guesses."

Load context

  • ~/.claude/plugins/config/claude-for-legal/law-student/CLAUDE.md → current classes, professors, learning style
  • User-provided: case name / case text / casebook pages / reading list

Workflow

Step 1: Identify the reading + professor

  • Case name and citation
  • Professor (from ~/.claude/plugins/config/claude-for-legal/law-student/CLAUDE.md class list — tone and focus vary by professor)
  • Class / subject area
  • Where this case falls in the syllabus (for context — is this the first case on the topic, a narrowing case, a counterexample?)

Step 2: Predict the questions

Professors cold-call in recurring patterns. Predict across these categories:

Facts-level (warm-up):

  • Who are the parties? What happened? Procedural posture?
  • What did the trial court do? The appellate court below?
  • Why is this in the casebook? What subject is it illustrating?

Holding / rule:

  • What's the holding? One sentence.
  • What's the rule that comes out of this case — the portable takeaway?
  • How would you phrase the rule if it were in your outline?

Reasoning:

  • Why did the court decide this way?
  • What arguments did the court reject?
  • Was there a dissent? What did it argue?

Application / hypos:

  • What if [fact X] were different — same outcome?
  • How does this case compare to [prior case in the syllabus]?
  • What's the limiting principle? Where does this rule stop?

Policy / theory:

  • What's the policy the court is protecting?
  • Does this rule make sense? Alternative approaches?

Professor-specific flavor (from ~/.claude/plugins/config/claude-for-legal/law-student/CLAUDE.md notes):

  • If the professor is known for hypo-heavy calls, weight Application/Hypo questions
  • If policy-heavy, weight Policy/Theory
  • If fact-heavy socratic (Socratic 101 Paper Chase style), weight Facts + Holding

Pick 6-10 questions across these categories. Rank by likelihood of being asked first (Facts usually go first, then Holding, then the harder categories).

Step 3: Drill

Use the socratic-drill pattern:

  1. Ask Question 1. Wait for answer.
  2. If right + well-reasoned: acknowledge, move to Question 2.
  3. If right but sloppy: don't let it slide. "You got there, but explain — why does the court's reasoning support that?"
  4. If wrong: don't give the answer. Ask a narrowing question. "What facts does the court rely on?" Walk them to it.
  5. If stuck: narrow further. "Before we go to the holding — what's the procedural posture?"
  6. If genuinely lost: tell them to re-read the case. "This is a re-read, not a guess-your-way-through. Come back when you've read it again."

Step 4: Post-drill summary

At the end:

markdown
# Cold-Call Prep — [case] — [date]

**Questions drilled:** [N]
**Strong:** [questions where they were confident + right]
**Shaky:** [questions where they guessed or hedged]
**Missed:** [questions where they didn't know]

## Before class tomorrow:
- [specific thing to re-check — facts they got wrong, rule they couldn't state]
- [if shaky on policy/theory: "read the dissent again — that's usually where policy questions come from"]

## Questions likely to come up in class:
- [top 3 of the 10 — the ones the professor is most likely to lead with]

Integration

  • case-brief: if the student hasn't briefed the case yet, offer to run /law-student:case-brief before cold-call prep. A brief is a cold-call prep tool too.
  • socratic-drill: if prep surfaces a weak spot in the subject (not just this case), follow with /law-student:socratic-drill [subject].
  • flashcards: if the case's rule is one the student should memorize, offer to add to the flashcard deck.

What this skill does not do

  • Be the professor. The actual cold-call can go anywhere. This skill predicts patterns; professors surprise.
  • Replace reading the case. If you haven't read it, the skill can't help you — questions require text you've absorbed.
  • Give you the case's holding without asking you first. Drill-me pattern: I ask, you answer.
  • Predict jurisdiction-specific niche questions. If the professor has known hobby horses, capture them in ~/.claude/plugins/config/claude-for-legal/law-student/CLAUDE.md class notes and the skill can weight accordingly; otherwise, it works from general patterns.

Frequently asked questions

What does the Cold Call Prep AI skill do?

Prep for a cold-call — predict the professor's likely questions and drill them Socratically, flagging where you're shaky so you know what to re-read before class. Use when the user says "prep for class tomorrow", "cold call [case]", "what might [professor] ask on", or points at assigned reading.

Why use Cold Call Prep on TypingMind?

Because you install it once and use it with any model. Cold Call Prep is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Cold Call Prep in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/anthropics/claude-for-legal/tree/main/law-student/skills/cold-call-prep. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Cold Call Prep?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Cold Call Prep?

As many as you like. As long as a model supports skills, you can use Cold Call Prep with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Cold Call Prep AI skill free?

Yes. It is published on GitHub by anthropics under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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