skills/legal/case-summary/SKILL.md
Produces an attorney-ready memo from a corpus of legal documents supplied by the user. Use when a user shows up with a folder, zip, or vault of case documents and asks for a case summary, case evaluation, litigation package, intake memo, matter overview, or "can you summarize this case for me." The skill ingests the corpus into a searchable index, OCRs anything non-searchable, inventories and diagnoses the practice area, loads the appropriate practice-area playbook module(s) (PI/tort, commercial litigation, IP infringement, or user-authored extensions), iteratively searches the corpus across eight core dimensions plus any module-specific dimensions, defers specialized document clusters (depositions, medical records, discovery, liens) to dedicated sibling skills, and synthesizes a cited memo.
npx skillsauth add casemark/skills case-summaryInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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A user arrives with a pile of legal documents — often hundreds or thousands — and asks for a summary. This skill is the playbook for doing that well, across any practice area.
The work is map-reduce: map the corpus across the dimensions that matter, reduce the hits into a memo. The skill does not prescribe a specific search tool — it describes the work. references/BACKENDS.md lists concrete implementations the agent can use today.
The core describes what every case has (parties, timeline, evidence, claims, exposure, defenses, procedural status, red flags). Practice-area playbooks (references/PLAYBOOK-*.md) specialize the core for specific domains. Specialized document types (depositions, medicals, discovery, liens) route out to dedicated sibling skills per references/ROUTING.md.
case-chronology — when the deliverable is a dated timeline rather than a full memocase-intake-initial-fact-memo — for commercial-litigation intake memoscase-viability-report — for go/no-go screening before accepting a matterpi-demand-summary — when the objective is a PI settlement package, not a case evaluationdemand-letter — when the objective is a pre-suit demand letterdeposition-summary — for deposition transcripts; route per ROUTING.mddiscovery-summary — for interrogatory/RFP/RFA responses; route per ROUTING.mdmedical-record-chronology — for dense medical-records sets; route per ROUTING.mdmedical-treatment-summary — for causation-focused treatment narrativesevidence-liability-summary — for plaintiff-side liability element breakdownslien-resolution-summary — for post-settlement lien trackinglegal-strategy-summary — for motion-and-discovery roadmap deliverablessettlement-summarization — for post-memo settlement activityAsk every time unless the user says "use defaults" or "just go." Gather:
ROUTING.md.Defaults (if the user doesn't answer): PI/tort playbook; evaluation memo; attorney work-product; U.S. state-court posture. Label every default explicitly in the memo header.
1. Ingest. Put the corpus into something searchable. See references/WORKFLOW.md for the phase description and references/BACKENDS.md for implementations.
2. OCR. Any PDF that isn't already searchable will not be indexed usefully. Handle OCR before searching on files that need it. Asynchronous — keep moving.
3. Inventory. Pull the object list. Skim filenames to build a mental model: what practice area, what kinds of documents, how many of each. This is the only time the agent cares about every file.
4. Diagnose practice area. From the inventory plus a single global-overview query, pick one or more playbook modules from references/PLAYBOOK-*.md. Each has triggers in its frontmatter that match filenames and content signals. Record the choice in the memo header. When no module fits, proceed core-only and note it.
5. Map — iterative search per dimension. For each of the eight core dimensions in references/CORE-DIMENSIONS.md:
references/SEARCH-PLAYBOOK.md.references/ROUTING.md and consume the sibling skill's output rather than summarizing inline.Don't brute-force read every file. Drive the search with focused queries, pull the 3–10 most relevant chunks per query, follow up when hits are load-bearing, route when a cluster warrants a sibling skill.
6. Reduce — synthesize. Assemble the memo using references/OUTPUT-TEMPLATE.md's core skeleton, plus the output sections contributed by each loaded playbook. Every claim cites an object + page (or chunk ID). No citation = cut or label as assumption.
7. Review. Run the quality checklist below (core items + playbook-specific items). Flag any unsatisfied items.
After delivering the draft, ask:
[VERIFY]?If the user doesn't answer, default to: list the loaded playbooks and invoked siblings; flag any dimension that returned "no evidence found"; require attorney review.
references/BACKENDS.md small-corpus recipe.Every matter:
[VERIFY]Plus every playbook-specific item from each loaded PLAYBOOK-*.md's Quality checklist section.
SEARCH-PLAYBOOK.md base queries). Note the ambiguity in VIII Strengths / Weaknesses / Red Flags. Offer the user a recommendation to narrow.Sibling skills invoked), record the cluster the agent wanted to route, and summarize the cluster inline with reduced depth. Attorney review should consider whether a focused sibling-skill run is warranted before the memo is used downstream.development
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