Official agent skill

Feature Risk Assessment

by anthropics in anthropics/claude-for-legal

Deeper risk assessment for a single feature or product area when the launch review found something that needs more than a line item.

OfficialApache-2.0Auto-check passedLegal & Compliance

Install Feature Risk Assessment

skills CLI
$ npx skills add anthropics/claude-for-legal --skill feature-risk-assessment -a claude-code

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

GitHub CLI
$ gh skill install anthropics/claude-for-legal feature-risk-assessment --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/anthropics/claude-for-legal.git skills-src && mkdir -p .claude/skills && cp -r skills-src/product-legal/skills/feature-risk-assessment .claude/skills/feature-risk-assessment && 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
feature-risk-assessment
GitHub stars
9.6k
Used in
2 other repos
Token cost
~2.2k tokens
SKILL.md length
1,033 words
Files
1
Skills in repo
147
Repo updated
First seen
Licence
Apache-2.0

At a glance

Deeper risk assessment for a single feature or product area when the launch review found something that needs more than a line item.

  • Works in 6 steps: What we're assessing → The risks → Regulatory landscape (if relevant) → …
  • User says deep dive on this risk
  • SKILL.md covers Matter context, Purpose, When to run this and Structure, plus 6 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Feature Risk Assessment is an agent skill from anthropics/claude-for-legal, published by the product's own GitHub organization. Deeper risk assessment for a single feature or product area when the launch review found something that needs more than a line item. Structured analysis: what could go wrong, how likely, how bad, what mitigates it. Use when user says "deep dive on this risk", "risk assessment for [feature]", "what could go wrong with", or when launch-review flags a novel issue.

Its SKILL.md is about 2.2k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Legal & Compliance, covering Feature launches and release readiness and Creative writing and fiction. The repository describes itself as: A suite of plugins for legal workflows. The licence is Apache-2.0.

When your agent uses it

  • User says deep dive on this risk
  • Risk assessment for [feature]
  • What could go wrong with
  • Launch-review flags a novel issue

Example prompts

  • “deep dive on this risk”
  • “risk assessment for [feature]”
  • “what could go wrong with”
  • “/feature-risk-assessment”

Workflow steps

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

  1. What we're assessing
  2. The risks
  3. Regulatory landscape (if relevant)
  4. Precedent (if any)
  5. Options
  6. Recommendation

What it can do on your machine

Read from SKILL.md and the folder at commit 4a6c651. 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 (its code samples are markdown).

    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

Feature Risk Assessment loads about 2.2k tokens when it runs. Until then it costs about 97 tokens; SKILL.md has 1,033 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~97
When it runs · the whole SKILL.md, loaded when a task matches
~2.2k

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 anthropics/claude-for-legal at commit 4a6c651, republished under its Apache-2.0 licence (© anthropics). 1,033 words, ~2,227 tokens.

Download SKILL.mdSave it as .claude/skills/feature-risk-assessment/SKILL.md (or your agent's skills folder).
name
feature-risk-assessment
description
Deeper risk assessment for a single feature or product area when the launch review found something that needs more than a line item. Structured analysis: what could go wrong, how likely, how bad, what mitigates it. Use when user says "deep dive on this risk", "risk assessment for [feature]", "what could go wrong with", or when launch-review flags a novel issue.

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):

markdown
### 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?]
3. Regulatory landscape (if relevant)

Only include if a regulator is actively interested in this space. If so:

  • Which regulator, what they've said/done recently
  • How this feature would look to them
  • Whether we'd rather they hear about it from us or from a headline
4. Precedent (if any)

Has another company done something similar? What happened?

  • If nothing bad happened → useful, not dispositive
  • If something bad happened → what was different about their situation, does it apply here

Don't overweight precedent. Regulators change priorities; one company getting away with something doesn't mean the next one will.

5. Options

Present 2-3 realistic paths:

markdown
| Option | Description | Risk reduction | Cost |
|---|---|---|---|
| A: Ship as designed | [current plan] | None | None |
| B: Ship with [mitigation] | [change] | [how much] | [eng effort, timeline, UX] |
| C: Don't ship [component] | [scope cut] | [how much] | [product impact] |
6. Recommendation

Pick one. Explain why. Acknowledge what you're trading off.

markdown
**Recommended: Option [X]**

[Why. What risk remains. Why that's acceptable. Who accepts it.]

**If the answer is "not my call":** [Who decides, what they need to know]

Calibration check

Before finalizing, check against ~/.claude/plugins/config/claude-for-legal/product-legal/CLAUDE.md → Risk calibration:

  • Is this risk assessment calibrated to this company, or is it generic?
  • A risk that's "High" at a company under a consent decree might be "Medium" at one that isn't
  • The assessment should reflect the actual regulatory posture, litigation history, and risk appetite captured in the practice profile

Handoffs

  • To AI governance: If the deep-dive was triggered by an AI feature — which it often is — run /ai-governance-legal:aia-generation [feature] in parallel or immediately after. The feature risk assessment frames the decision; the AIA documents the AI system specifically in the format AI governance needs. They're not duplicates: the FRA is a product-legal decision doc; the AIA is the governance record.
  • To privacy: If the feature involves new data collection or processing, run /privacy-legal:pia-generation [feature]. The FRA's risk section will likely overlap with the PIA's — flag that overlap so work isn't duplicated, but both docs need to exist.
  • To AI governance vendor review: If the feature uses a new AI vendor, run /ai-governance-legal:vendor-ai-review [vendor agreement] if not already done during the launch review.

Output format

Standalone doc, 2-4 pages. Prepend the work-product header from ~/.claude/plugins/config/claude-for-legal/product-legal/CLAUDE.md ## Outputs (it differs by user role — see ## Who's using this).

Not a slide deck, not a memo to file — a decision document someone reads and then decides.

Save where ~/.claude/plugins/config/claude-for-legal/product-legal/CLAUDE.md → Launch review process says review docs go. If the doc is going to be shared with anyone outside the privileged loop (e.g., posted to a broadly-shared ticket), drop the work-product header only for that externally-facing copy and keep the privileged original in the matter file.

Show full SKILL.md (390 more words)Show less

Citation check

If the assessment cites cases, statutes, regulations, or enforcement actions — in the Regulatory landscape or Precedent sections especially — those citations were generated by an AI model and have not been verified against a primary source. Before the decision document goes to a decisionmaker, verify each citation against a legal research tool (Westlaw, CourtListener, or your firm's research platform) for accuracy, good law status, and current enforcement posture. A risk assessment built on a fabricated enforcement action is worse than no assessment.

No silent supplement. If a research query to the configured legal research tool returns few or no results for the regime or precedent the assessment needs, report what was found and stop. Do NOT fill the gap from web search or model knowledge without asking. Say: "The search returned [N] results from [tool]. Coverage appears thin for [regime / precedent]. Options: (1) broaden the search query, (2) try a different research tool, (3) search the web — results will be tagged [web search — verify] and should be checked against the issuing authority before relying, or (4) flag as unverified and stop. Which would you like?" A lawyer decides whether to accept lower-confidence sources.

Source attribution. Tag every citation in the Regulatory landscape and Precedent sections with where it came from: [Westlaw], [CourtListener], [regulator site], or the MCP tool name for citations retrieved from a legal research connector; [web search — verify] for web-search citations; [model knowledge — verify] for citations recalled from training data; [user provided] for citations from the feature team. Citations tagged verify carry higher fabrication risk and should be checked first. Never strip or collapse the tags — the decisionmaker needs to see which citations to verify first.

Close with the next-steps decision tree

End with the next-steps decision tree per CLAUDE.md ## Outputs. Customize the options to what this skill just produced — the five default branches (draft the X, escalate, get more facts, watch and wait, something else) are a starting point, not a lock-in. The tree is the output; the lawyer picks.

What this skill does not do

  • It doesn't assess every feature. Most features get a launch review and that's it.
  • It doesn't make the decision. It frames the decision. Someone with authority picks an option.
  • It doesn't do quantitative risk modeling. If the company has a formal risk framework with numbers, use that — this is qualitative.

© anthropics, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in product-legal/skills/feature-risk-assessment of anthropics/claude-for-legal.

Open the folder on GitHubat commit 4a6c651

Used in 2 other repositories

We found 2 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 2 other GitHub owners. This page covers the copy in anthropics/claude-for-legal, which our catalogue first saw on October 7, 2026.

Compare with similar skills

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Book Market Researchrobertguss/claude-code-toolkit124—~2.4kAutomated safety check: PassProprietary
.NET MAUI Release Readinessdotnet/maui23k—~15kAutomated safety check: PassMIT
Release ValidationMesh-LLM/mesh-llm3.5k—~2.6kAutomated safety check: PassApache-2.0
Final Release Reviewopenai/openai-agents-python30k—~5.4kAutomated safety check: PassMIT

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Questions about Feature Risk Assessment

What does Feature Risk Assessment do?

Deeper risk assessment for a single feature or product area when the launch review found something that needs more than a line item. Feature Risk Assessment is an agent skill from anthropics/claude-for-legal, published by the product's own GitHub organization. Deeper risk assessment for a single feature or product area when the launch review found something that needs more than a line item.

When should I use Feature Risk Assessment?

Feature Risk Assessment fits situations like: user says deep dive on this risk; risk assessment for [feature]; what could go wrong with; launch-review flags a novel issue.

How do I install Feature Risk Assessment in Claude Code?

Run `npx skills add anthropics/claude-for-legal --skill feature-risk-assessment -a claude-code`. Or copy the skill folder (product-legal/skills/feature-risk-assessment in anthropics/claude-for-legal) into .claude/skills/feature-risk-assessment in your project. Claude Code loads it when a task matches its description.

How do I install Feature Risk Assessment in Codex?

Run `npx skills add anthropics/claude-for-legal --skill feature-risk-assessment -a codex`. Or copy the skill folder (product-legal/skills/feature-risk-assessment in anthropics/claude-for-legal) into .agents/skills/feature-risk-assessment in your project. Codex loads it when a task matches its description.

Can I use Feature Risk Assessment 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 anthropics/claude-for-legal --skill feature-risk-assessment -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/feature-risk-assessment, .gemini/skills/feature-risk-assessment, .github/skills/feature-risk-assessment and .opencode/skills/feature-risk-assessment in your project.

What does Feature Risk Assessment need to run?

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

Does Feature Risk Assessment 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 Feature Risk Assessment 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 Feature Risk Assessment use?

Feature Risk Assessment is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Feature Risk Assessment use?

About 2.2k tokens (SKILL.md is roughly 8.9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Feature Risk Assessment?

Skills that share tags, products or a category with Feature Risk Assessment: Design Submission Check (SeanJ1ang/design-judge-skills, 712 stars), Book Market Research (robertguss/claude-code-toolkit, 124 stars), .NET MAUI Release Readiness (dotnet/maui, 23k stars) and Release Validation (Mesh-LLM/mesh-llm, 3.5k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Feature Risk Assessment?

anthropics (a GitHub organization, an official publisher) maintains it in anthropics/claude-for-legal, which has 9,633 GitHub stars. The repository holds 147 skills in this directory. The repository was last updated on September 29, 2026.

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