skills/legal/deposition-summary-index/SKILL.md
Creates topic-based deposition summaries with exhibit-to-transcript citation mapping for U.S. commercial litigation. Use when a user provides a deposition transcript and requests a witness summary, deposition digest, exhibit index, cross-examination prep, or discovery-analysis packet.
npx skillsauth add casemark/skills deposition-summary-indexInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Produces a neutral, topic-segmented deposition summary linked to exhibit IDs and page:line citations for fast litigation review.
Before starting, confirm you have:
Stop and escalate if privilege logs, sealing orders, or confidentiality controls are missing.
Exh. 1, DEF. EXH. 2, PX-3 → single format)| Topic | Transcript Span | Summary (2–3 sentences) | Linked Exhibits |
|---|---|---|---|
| Contract negotiation | 12:1–14:22 | ... | [Exh. 3], [12:18–12:30] |
| Billing disputes | 31:4–38:10 | ... | [Exh. 8], [33:14–33:40] |
Include every referenced exhibit with transcript cites, witness characterization, and risk flags.
| Exhibit ID | Transcript Mentions | Witness Characterization | Substance | Risk Notes |
|---|---|---|---|---|
| Exh. 3 | 12:10, 12:19–12:24 | "Contract signed by [witness] on [date]." | Formation details | [VERIFY] date ambiguity |
Flag: authentication gaps, hearsay concerns, missing metadata, contradictions.
[VERIFY]—never over-interpret.Key changes made:
tags field. Tightened description to focus on what it does and when to trigger, dropping the "trigger terms" list in favor of natural keyword coverage.development
name: automated-contract-summary language: en description: Generates structured executive summaries of contracts using ML — captures key terms, party obligations, risk allocations, and compliance requirements in a standardized format. Optimized for high-volume review where speed and consistency matter. tags: - summarization - agreement - corporate --- # Automated Contract Summarization Produces standardized executive summaries of contracts using machine learning, capturing essential term
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