Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add LegalQuants/lq-ai --skill case-law-researchgit clone --depth 1 https://github.com/LegalQuants/lq-aiWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/legalquants/lq-ai/case-law-research)<a href="https://agentmods.dev/skills/legalquants/lq-ai/case-law-research"><img src="https://agentmods.dev/badge/skills/legalquants/lq-ai/case-law-research/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/legalquants/lq-ai/case-law-research"><img src="https://agentmods.dev/badge/skills/legalquants/lq-ai/case-law-research.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00051 | $0.02009 |
| Opus 5.5 | $0.00020 | $0.00804 |
| Sonnet 5.5 | $0.00010 | $0.00402 |
| Haiku 4.5 | $0.00005 | $0.00201 |
Grade A, and why
case-law-research scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 156 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Case-Law Research
Conduct a structured search of U.S. case law via CourtListener, read the opinion text, and produce a grounded research report. Every statement in the report must be traceable to retrieved text — do not assert what a case holds unless you have read the relevant passage.
When this skill applies
Apply when the user asks to locate, read, or cite court decisions on a legal question. Common triggers: finding controlling or persuasive authority, confirming whether authority exists, reading specific language from an opinion, assembling citations for a brief or memo.
Do not apply this skill when:
- The user asks about statutory text, regulations, or administrative guidance. Those are not in the CourtListener corpus; direct the user to the appropriate source.
- The user asks about non-U.S. law. CourtListener covers U.S. federal and state courts only; this skill does not extend beyond that coverage.
- The user needs to confirm whether a case is still good law (overruled, distinguished, limited). This skill is not a citator. It does not perform Shepardizing or KeyCiting. Always recommend the user validate any case's current status in Westlaw, Lexis, or a comparable citator before relying on it.
Inputs
Required: the question input — the legal question or issue to research. If the user has not stated a question clearly, ask before searching.
Optional: the jurisdiction input — a court or jurisdiction to focus on (e.g., "9th Circuit", "New York Court of Appeals", "SCOTUS"). When provided, weight searches toward that jurisdiction. When absent, begin with a broad U.S. search and note which courts produced the most relevant results.
If the user's question is ambiguous (e.g., "case law on good faith") and the jurisdiction materially affects which results are relevant, ask one clarifying question before searching. Do not block on optional inputs.
Tool chain
This skill runs through the LQ.AI governed gateway. All CourtListener tool calls are tier-gated and audited; the "Sources consulted" panel (visible to the user after the run) shows the provenance trail.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 156 lines · 51 tokens per session scan A e8b9e171a21e
case-law-research is a skill published in the GitHub repository LegalQuants/lq-ai (156 stars, last pushed today), licensed Apache-2.0. It adds 51 tokens to every session and 2,009 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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