Claude for Legal is a collection of AI agents, skills, and data connectors for legal work such as commercial, privacy, employment, litigation, regulatory, and intellectual-property matters. Lawyers and legal teams use it through Claude Cowork, Claude Code, or the Claude Managed Agents API, with attorney review required for every output. The catalogue entries are its workflow plugins, skills, agents, and connectors.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add anthropics/claude-for-legal --skill cold-call-prepgit clone --depth 1 https://github.com/anthropics/claude-for-legalWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/anthropics/claude-for-legal/cold-call-prep)<a href="https://agentmods.dev/skills/anthropics/claude-for-legal/cold-call-prep"><img src="https://agentmods.dev/badge/skills/anthropics/claude-for-legal/cold-call-prep/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/anthropics/claude-for-legal/cold-call-prep"><img src="https://agentmods.dev/badge/skills/anthropics/claude-for-legal/cold-call-prep.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00072 | $0.01685 |
| Opus 5.5 | $0.00029 | $0.00674 |
| Sonnet 5 | $0.00014 | $0.00337 |
| Haiku 4.5 | $0.00007 | $0.00169 |
Grade A, and why
cold-call-prep scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
1 near-identical copy found in the catalogue:
- cold-call-prep — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 134 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/cold-call-prep
- Load
~/.claude/plugins/config/claude-for-legal/law-student/CLAUDE.md→ class list, professors, learning style. - Apply the workflow below.
- Identify reading (case name + citation, professor, class, syllabus context).
- Predict 6-10 likely questions across categories (Facts / Holding / Reasoning / Application / Policy), weighted to professor's known tendencies.
- Drill using socratic pattern — ask, wait, push back, narrow when stuck. Don't give answers.
- 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."
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 6d ago First seen · 134 lines · 72 tokens per session scan A 7883e8cf2b55
cold-call-prep is a skill published in the GitHub repository anthropics/claude-for-legal (9,544 stars, last pushed 4d ago), licensed Apache-2.0. It adds 72 tokens to every session and 1,685 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-23.
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