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 feature-risk-assessmentgit 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/feature-risk-assessment)<a href="https://agentmods.dev/skills/anthropics/claude-for-legal/feature-risk-assessment"><img src="https://agentmods.dev/badge/skills/anthropics/claude-for-legal/feature-risk-assessment/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/feature-risk-assessment"><img src="https://agentmods.dev/badge/skills/anthropics/claude-for-legal/feature-risk-assessment.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 | $0.00083 | $0.02031 |
| Opus 5.5 | $0.00033 | $0.00812 |
| Sonnet 5.5 | $0.00017 | $0.00406 |
| Haiku 4.5 | $0.00008 | $0.00203 |
Grade A, and why
feature-risk-assessment 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 23d 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.
How it starts
The opening of the file, as written. The whole thing — 159 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Feature Risk Assessment
Matter context
Matter context. Check ## Matter workspaces in the practice-level CLAUDE.md. If Enabled is ✗ (the default for in-house users), skip the rest of this paragraph — skills use practice-level context and the matter machinery is invisible. If enabled and there is no active matter, ask: "Which matter is this for? Run /product-legal:matter-workspace switch <slug> or say practice-level." Load the active matter's matter.md for matter-specific context and overrides. Write outputs to the matter folder at ~/.claude/plugins/config/claude-for-legal/product-legal/matters/<matter-slug>/. Never read another matter's files unless Cross-matter context is on.
Purpose
The launch review is broad. This is deep. When a single issue needs more than a table row — a novel AI feature, a children's product, something a regulator is actively looking at — this skill produces a standalone assessment.
Not every launch needs one. Most don't. This is for the 10% where "PIA done, shipped" isn't the right level of scrutiny.
When to run this
- Launch review found a pattern that's not in the calibration table (novel)
- Launch review found something in the "usually blocks" category
- GC or leadership asked "what's the risk here" and wants more than a one-liner
- The feature is in an area with active regulatory attention (AI, children, biometric, health)
- Someone outside legal is worried and a structured answer would help
If none of the above, the launch review is enough. Don't generate paperwork for its own sake.
Structure
1. What we're assessing
One paragraph. What the feature does, what's new about it, why it got escalated to a full assessment.
2. The risks
For each distinct risk (aim for 2-5, not 15):
### Risk [N]: [Short name]
**Scenario:** [What would have to happen for this to go wrong. Be specific —
not "data breach" but "the recommendation algo surfaces a user's sensitive
category interest to someone who shouldn't see it because X."]
**Who gets hurt:** [Users? The company? A third party? Specific.]
**How likely:** [Low / Medium / High — with a reason. "Low — would require
both X and Y to fail simultaneously." Not just a vibes rating.]
**How bad if it happens:** [Low / Medium / High — with a reason. "High —
regulatory fine + class action exposure + press" vs. "Low — one angry
tweet, no actual harm."]
**Existing mitigations:** [What already reduces the likelihood or impact]
**Gap:** [What's missing, if anything]
**Residual risk:** [After existing mitigations — is this acceptable or does
it need more?]
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.
- 23d ago First seen · 159 lines · 83 tokens per session scan A 90ebbc0ddffe
feature-risk-assessment is a skill published in the GitHub repository anthropics/claude-for-legal (9,693 stars, last pushed 2d ago), licensed Apache-2.0. It adds 83 tokens to every session and 2,031 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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