OpenClaw Legal Research Automation

Research request intake — The attorney fills out a structured form specifying jurisdiction (state/federal/both), practice area, core legal question, key facts, relevant statutes, date range, and urgency level. The form maps to OpenClaw's input schema.

Skill file initialization — The request triggers the OpenClaw skill file, which loads jurisdiction-specific research parameters. Federal circuit courts have different precedent hierarchies than state courts, and the skill file adapts its search strategy accordingly.

Statutory foundation — The AI first establishes the statutory framework by identifying all relevant statutes, regulations, and administrative rules. This grounds the subsequent case law search in the correct legal context.

Primary case law research — OpenClaw executes structured queries against case law databases, using the statutory framework to build targeted search terms. Results are scored on a 0-100 relevance scale combining: factual similarity (40%), legal issue alignment (30%), jurisdictional authority weight (20%), and recency (10%).

Citation chain analysis — For each highly-relevant case (score 70+), the system traces the citation chain — what cases it cites, what cases cite it, and how subsequent courts treated it. This uncovers precedents that keyword searches miss entirely.

Citation validation — Every case in the results set is checked for negative subsequent treatment. Cases that have been overruled are removed. Cases that have been distinguished or questioned are flagged with warnings. Cases that have been affirmed or followed get a positive signal boost.

Opposition research — The skill file flips the research question and searches for cases supporting the opposing position. This gives attorneys a preview of what they'll face, with pre-drafted distinguishing arguments for each adverse precedent.

Relevance ranking — All results are re-ranked using a composite score that combines the initial relevance score with citation validation results, authority weight (Supreme Court > Circuit > District), and how recently the case was decided.

Memo generation — The AI compiles findings into a structured research memo following the firm's template: executive summary, statutory framework, supporting precedents (ranked), adverse precedents with distinguishing arguments, recommended citations for the brief, and a bibliography with full citation formatting.

Quality validation — A final pass checks the memo for internal consistency: are all citations properly formatted, do page references match, are there any circular references, and does every legal conclusion cite supporting authority.

Delivery — The completed memo is saved to the firm's document management system, linked to the matter number, and the requesting attorney receives an email notification with a summary and direct link.

Feedback loop — Attorneys can rate the research quality and flag any missed precedents. These ratings feed back into the skill file's relevance scoring parameters, improving accuracy over time.