Legal Settlement Evaluation Framework

# Legal Settlement Evaluation Framework

Safety Notice

This listing is from the official public ClawHub registry. Review SKILL.md and referenced scripts before running.

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Install skill "Legal Settlement Evaluation Framework" with this command: npx skills add harrylabsj/legal-settlement-evaluation-framework

Legal Settlement Evaluation Framework

Overview

Helps evaluate settlement options using exposure, evidence strength, litigation cost, non-monetary terms, timing, and uncertainty. This is a descriptive OpenClaw skill for legal-industry workflow support. It provides structured frameworks, checklists, templates, and issue-spotting prompts. It does not execute code, call external APIs, access legal databases, retrieve court records, automate filings, or perform legal services.

When to Use

  • Assessing settlement offers
  • Preparing client recommendation memos
  • Comparing litigation versus resolution options

Target Users

  • Litigators
  • In-house counsel
  • Claims teams
  • Business stakeholders

Inputs to Collect

  • Matter or project context, including jurisdiction if known
  • Relevant facts, documents, parties, dates, and constraints
  • Desired output format, audience, and level of detail
  • Known deadlines, risk concerns, or review priorities

Core Modules

  1. Exposure and damages range table — provides structured prompts, checklists, and review fields for this area.
  2. Evidence strength factors — provides structured prompts, checklists, and review fields for this area.
  3. Cost/time/risk comparison — provides structured prompts, checklists, and review fields for this area.
  4. Non-monetary term checklist — provides structured prompts, checklists, and review fields for this area.
  5. Decision memo template — provides structured prompts, checklists, and review fields for this area.

Workflow

  1. Confirm the user's legal workflow goal and the relevant practice context.
  2. Ask for missing facts, documents, dates, parties, jurisdiction, and audience where needed.
  3. Apply the modules below as a structured thinking framework.
  4. Produce checklists, templates, matrices, memos, or planning aids tailored to the user's context.
  5. Flag uncertainty, verification needs, deadlines, ethics concerns, confidentiality issues, and attorney-review points.

Expected Outputs

  • Settlement evaluation matrix
  • Risk-adjusted comparison
  • Client recommendation outline
  • Open issues list

Example Prompts

  • "Build a settlement evaluation matrix for a contract dispute."
  • "Help compare accepting an offer versus continuing litigation."

Safety and Legal Limitations

  • This skill provides informational workflow support only and is not legal advice.
  • It does not create an attorney-client relationship and does not replace review by a qualified attorney.
  • Laws, court rules, deadlines, ethics duties, privilege, confidentiality, and professional responsibility rules vary by jurisdiction and matter.
  • Users must verify all legal authorities, filing requirements, deadlines, facts, citations, and strategic decisions with qualified counsel.
  • The skill must not be used to fabricate evidence, coach false testimony, evade regulation, access data unlawfully, or bypass confidentiality obligations.
  • Specific limitation for this skill: Does not predict court outcomes or advise acceptance; final settlement decisions require attorney-client analysis.

Acceptance Criteria

  • Package is descriptive only: no handler.py, scripts, external APIs, network calls, or command execution.
  • SKILL.md and README.md are English-first and include an explicit legal-information disclaimer.
  • Outputs are frameworks, checklists, templates, or planning aids rather than legal conclusions.
  • Includes target users, when-to-use guidance, inputs, workflow, outputs, examples, and safety limitations.
  • skill.json contains unique slug, tags, trigger keywords, requires_api=false, and readiness=stable.

Source Transparency

This detail page is rendered from real SKILL.md content. Trust labels are metadata-based hints, not a safety guarantee.

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