A first-of-its-kind decision out of the District of Connecticut held that AI prompts used by an expert witness are discoverable methodology under Rule 26 — and the district court stayed the order sixteen days later. Every litigator working with experts who use AI needs to understand what the court said, what CLF is now arguing on the stay, and what comes next.
Case: Conservation Law Found., Inc. v. Shell Oil Co., Civil No. 3:21-cv-00933 (VDO) Court: District of Connecticut | Decision: May 18, 2026 (docket text order, ECF No. 970) | Judge: United States Magistrate Judge Thomas O. Farrish
By Kelly Twigger
Podcast | Transcript
A landmark holding, on hold
On May 18, 2026, in a docket-text order with no accompanying written opinion, a federal magistrate judge held for the first time that an expert witness’s AI prompts are discoverable methodology under Rule 26. Sixteen days later, on June 3, the district court stayed the order pending review of the plaintiff’s Rule 72(a) Objection. That is the posture you are working with as you read this.
The case is Conservation Law Foundation v. Shell Oil, a Clean Water Act and RCRA citizen suit against the operators of a bulk petroleum storage terminal on the waterfront in New Haven, Connecticut. Plaintiff CLF retained Dr. Naomi Oreskes — the Harvard historian of science and co-author of Merchants of Doubt — as a testifying expert. Dr. Oreskes and her research assistant used AI to cull Shell’s document production down to a working subset for her analysis. Shell moved to compel production of the AI prompts and outputs Dr. Oreskes’ team used. Magistrate Judge Thomas O. Farrish granted that motion at ECF No. 970, in a docket-text order. CLF filed both an emergency motion to stay and a Rule 72(a) Objection on the day compliance was due. Judge Vernon Oliver granted the stay.
The framework Magistrate Judge Farrish laid down — that an expert’s AI methodology is fair game under Rule 26(b), that prompts are part of that methodology, and that a “search terms only” disclosure does not cover the field — is now public. If Judge Oliver vacates it in a published opinion, the framework loses its precedential force and may well fall out of the conversation. If he leaves it in place, modifies it, or addresses the issue in any unpublished disposition, the reasoning will continue to influence how other courts approach the question. Either way, the analysis is on the record now, and litigators advising on AI use cannot afford to wait for the district court’s ruling before acting.
This episode of Case of the Week walks through what the order held, what CLF is now arguing on the stay, what happens if no prompts exist, how Florida’s new AI accountability rule fits into the same conversation, and what every litigator advising clients on AI use should be doing about it this week. We have been building this body of law across several Case of the Week episodes — Episode 183 on Heppner and Warner v. Gilbarco, and a more recent episode on Morgan v. V2X, Inc. If you are looking for the foundational analysis of those decisions, those episodes are the place to start. The focus here is on what Conservation Law Foundation adds, and on what comes next.
Case of the Week
The case underneath
Before we get to the discovery ruling, the underlying litigation matters. Conservation Law Foundation v. Shell is one of the most consequential environmental cases currently in active discovery in the United States. CLF, working with co-counsel at Motley Rice, filed citizen suits in 2021 alleging that Shell and the other operators of the New Haven petroleum terminal failed to design, maintain, modify, or operate that facility to account for the effects of climate change — sea-level rise, more frequent and more severe storms, storm surge — and that those failures create an imminent risk that the next major weather event will discharge oil and toxic chemicals into the surrounding waters and neighborhoods. A parallel CLF case proceeds against Shell in the District of Rhode Island.
These cases are among the first in the country to hold fossil fuel infrastructure operators accountable not for greenhouse gas emissions but for failing to prepare their physical facilities for climate change. Per CLF, they are also the first U.S. climate adaptation cases of their kind to reach the discovery phase. Dr. Oreskes — whose academic work has focused on the history of climate science and the manufactured doubt produced by fossil fuel companies — was retained to address what Shell knew, when it knew it, and what its internal records reveal about its understanding of the risks at issue. Working through Shell’s discovery production was central to her work. Using AI to make that document review tractable was central to her methodology.
That context matters because the stakes on the discovery ruling are not abstract. Whether Magistrate Judge Farrish’s framework survives district court review will shape both how Dr. Oreskes’ testimony comes in at trial and how every other testifying expert in active litigation handles AI from this point forward.
What Magistrate Judge Farrish actually held
The order is short. Three CLF arguments. All three rejected by Judge Farrish. The court ordered CLF to revise its discovery responses to disclose the AI prompts and queries Dr. Oreskes’ team used, with Rule 37(b) sanctions teed up if CLF later represents that nothing exists and that representation proves untrue. The compliance deadline was June 1.
CLF’s first argument was scope. CLF argued that AI prompts used by an expert are not within the scope of discovery at all. Magistrate Judge Farrish rejected that in a sentence, citing Macchia v. ADP, Inc., 711 F. Supp. 3d 162, 167-68 (E.D.N.Y. 2024), for the proposition that an expert’s methodology is fair ground for discovery. Under the facts here, the process by which Dr. Oreskes culled Shell’s production into a subset to be worked with is an aspect of that methodology. The principle is not new. Discovery of TAR protocols, search term lists, sampling rules, and coding instructions has been settled doctrine since Da Silva Moore in 2012. AI prompts are the next iteration of the same idea. What is new is the application to a testifying expert.
CLF’s second argument was the Rule 29 stipulation. The parties had agreed early in the case to limit expert discovery — specifically, that drafts of expert reports and communications between experts, employees, and consultants of party-related entities on expert opinions or settlement-related issues, including emails, notes, and other correspondence, would not be subject to discovery. CLF argued at the hearing that Dr. Oreskes’ AI prompts qualified as “notes” within that carve-out. Magistrate Judge Farrish did not agree. He had already addressed Rule 29 in this case at Conservation Law Foundation v. Shell Oil Co., 2025 WL 842278, at *3 (D. Conn. Mar. 18, 2025), holding that before a court denies otherwise-relevant discovery on a Rule 29 basis, the agreement “must be quite clear.” Calling an AI prompt a “note” is not so obvious as to be quite clear.
The practical move for litigators is straightforward and is the single most actionable thing in this opinion: Rule 29 agreements have to say what they mean. If you are negotiating a discovery stipulation today and you intend it to cover AI inputs and outputs, name them. Use “AI prompts,” “AI queries,” “AI outputs,” “embeddings,” and “model parameters.” Generic language — “notes,” “drafts,” “communications” — will not be read to sweep in AI artifacts. After this case, if it holds up, opposing counsel and the court will both insist on specificity. Magistrate Judge Braswell’s protective order language from Morgan v. V2X, Inc. is one starting point for that drafting, but it does not address experts specifically. You will need to extend it.
CLF’s third argument was that nothing exists to produce. CLF took the position that Dr. Oreskes did not use “prompts” at all — she used “search terms” — and that all search terms had already been produced. The court walked through the analytical framework that governs “nothing to produce” arguments. The default rule is that when a producing party tells the court the requested material does not exist, and the court has no reason to doubt that representation, the court will not order production of the impossible. That is Zervos v. S.S. Sam Houston, 79 F.R.D. 593 (S.D.N.Y. 1978). But the good-faith presumption fails when the requesting party has a strong reason to disbelieve the representation, backed by solid evidence rather than mere suspicion. That is Lewis v. Doe, 2021 WL 863473, at *5 (D. Conn. Mar. 8, 2021), which Magistrate Judge Farrish himself authored. Here, Shell had a non-speculative reason to disbelieve CLF’s representation: Dr. Oreskes’ assistant Dr. Alexander Kaurov had submitted a declaration that used the word “prompt.” Once that word was on the page, CLF’s blanket denial would not carry the day.
What Magistrate Judge Farrish did next was elegant. He did not find that prompts exist. He ordered CLF to revise its responses to any Rule 33 interrogatories or Rule 34 requests for production calling for AI prompts, signed under oath, with Rule 37(b) sanctions available if anything later turns up. Trust, but verify. That is the trapdoor — and it is the move that produced the stay, because CLF’s view is that there is nothing to produce, that no formal Rule 33 or 34 requests for this material were ever served, and that complying with the order under those circumstances exposes the organization to sanctions for representations it cannot ultimately back up if a single artifact ever surfaces in metadata, in a deposition, or in the AI vendor’s logs.
The four-case framework
Conservation Law Foundation matters because it fills the last open gap in a body of law we have been building all spring. Three earlier federal decisions, covered in detail on prior Case of the Week episodes, established how courts are going to treat AI prompts depending on who is at the keyboard. Conservation Law Foundation completes the map. Episode 183 walks through U.S. v. Heppner and Warner v. Gilbarco together, and the Morgan v. V2X, Inc. episode covers the most thorough civil treatment we have to date. If you are advising clients on AI use, those episodes are the foundation. The compact through-line is here.
The question across all four cases is who is at the keyboard and why. In Heppner, the user was a represented criminal defendant operating outside his lawyer’s direction on a public AI platform. No privilege, no work product. In Warner v. Gilbarco and Morgan v. V2X, the user was a pro se litigant acting as both party and advocate. Work product applies; AI platforms are tools, not adversaries; disclosure to the tool is not disclosure waiver. Conservation Law Foundation fills the gap none of those decisions reached: when the user is a testifying expert, the expert’s methodology is discoverable, and the prompts are part of the methodology.
Each decision is sensitive to who is at the keyboard and why. Knowing where on that map your client sits is how you decide what to protect, what to produce, and what to negotiate. If you have a client using AI right now — and almost all of you do — the threshold question for every conversation is not whether they are using it but in which role. That answer drives the rest.
The technology — and why CLF’s framing matters
The dispute over whether Dr. Oreskes’ team used “prompts” or “search terms” is the part of the case where it would be easy to dismiss CLF’s position as semantic gamesmanship. That would be a mistake. The framing is doing real legal work, and the way the court handled it tells you exactly how AI methodology disputes will play out in your cases.
CLF disclosed early in the dispute that Dr. Oreskes and Dr. Kaurov used OpenAI’s GPT-4o and GPT-4.5 models, accessed through the Microsoft Azure OpenAI Service via the secure Azure API. That is the enterprise deployment of OpenAI’s models, not the consumer version of ChatGPT. The documents were processed on a Microsoft Azure cloud server CLF says was rented for exclusive use on this litigation. That distinction matters, and it actually resolved a meaningful piece of Shell’s original motion. Shell’s letter brief at ECF No. 941 had a separate ground challenging whether uploading confidential documents to a third-party AI platform violated the Standing Protective Order. That ground was resolved at the parties’ four-hour court-ordered meet-and-confer in April. By the time of the May 14 hearing, the Azure configuration was no longer in dispute. The only thing left on the table was the prompts and outputs themselves.
CLF anchored its “search terms, not prompts” argument in Dr. Oreskes’ deposition testimony. When Shell’s counsel asked Dr. Oreskes about the prompts she used, counsel framed a prompt as something different from a search term — a prompt, in counsel’s framing, implied generative analysis where the AI does the thinking. Dr. Oreskes pushed back. She told the lawyer on the record that what she and her assistant did was not generative analysis at all. The AI sifted documents by keyword. She then manually reviewed every document the AI surfaced. Nothing in her report was generated by AI. The “prompts” Shell is seeking, CLF argues, are nothing more than the search terms — and CLF has already produced those.
Here is where the technical reality and the legal framing pull apart. At the technical level, there is no difference between a “prompt” and a “search term” once you are sending text to a generative model through an API. Anything you send to GPT-4o is processed as a prompt. There is no “search term mode” inside the model. Whether you type “sea level rise” into Google or send the identical text to GPT-4o, those are fundamentally different operations. Google runs a keyword index. GPT-4o is a generative language model that interprets meaning, pulls in concepts that share meaning but not literal text, and ranks documents by inferred relevance. CLF’s framing tries to analogize what Dr. Oreskes’ team did to a Google search. The analogy breaks down at the technology layer.
And there is a deeper point. When you use a generative AI to filter documents, you do not just send the model a keyword. You send the model an instruction. Review this document. Tell me if it discusses sea-level rise. Output a list of document IDs. The keyword is one variable inside that instruction. The instruction itself — the wrapper prompt — is methodology, and CLF has not disclosed it. AI filtering is also iterative. You run a query, see what comes back, refine, run it again. Dr. Kaurov’s own declaration acknowledges the prompts and outputs were used “interactively.” That iteration shapes the working set. CLF did not preserve any of it. And the AI’s output — which documents it surfaced, in what order, with what implicit confidence — is methodology Dr. Oreskes effectively delegated to the model.
So when CLF says it disclosed the search terms, it is disclosing one variable from a multi-step process. The wrapper prompt, the iterative refinements, and the model’s intermediate rankings have not been produced. That is the gap Shell is walking through. The takeaway for litigators is direct: you cannot relabel your way out of discoverability. If you are using generative AI to filter documents for an expert, the disclosure obligation runs to the entire instruction set, not just the keyword. If you want to argue you have disclosed everything, you have to show you preserved everything. Disclosing search terms is partial disclosure of a multi-step process, and partial disclosure is the gap that opposing counsel and the court will both find.
The procedural defect that may decide this
CLF’s Rule 72(a) Objection at ECF No. 976 lays out six grounds for vacating the order. The cleanest, and the one most likely to control the outcome, is procedural. CLF argues that Shell never actually served formal Rule 33 interrogatories or Rule 34 requests for production for the AI prompts. What Shell served was a series of follow-up emails and meet-and-confer letters. CLF voluntarily responded to those — and that is why we have the signed September 25, 2025 objections — but the objections were courtesy responses, not responses to formal discovery requests.
The doctrine on this is well-settled in the District of Connecticut and elsewhere. An informal email request cannot be enforced through a Rule 37 motion to compel. Wells Fargo Bank, N.A. v. Konover, 2010 WL 11561491 (D. Conn. June 22, 2010); Schwartz v. Mktg. Publishing Co., 153 F.R.D. 16, 21 & n.12 (D. Conn. 1994) — “the entire enforcement mechanism of Rule 37 contemplates the parties having formally resorted to the underlying discovery rule, in this case Rule 34, rather than a casual, informal request contained in a letter.” Same principle in the Western District of New York in Schneider ex rel. A.T. v. City of Buffalo, 2021 WL 5042502 (W.D.N.Y. Oct. 29, 2021). CLF then goes further with Wynne v. Town of East Hartford, 2023 WL 1272892 (D. Conn. Jan. 31, 2023), which holds that Rules 33 and 34 are not the proper vehicle for expert discovery in the first place. Depositions and Rule 45 subpoenas to the expert are.
Read those together. CLF is arguing that Magistrate Judge Farrish ordered it to revise responses to Rule 33 and 34 requests that were never served, and authorized Rule 37(b) sanctions for any inconsistency in those responses, when the underlying enforcement mechanism does not exist. That is the kind of clean procedural argument a district court can use to vacate an order without ever reaching the merits of whether AI prompts are discoverable expert methodology. If Judge Oliver vacates on this ground alone, the substantive doctrine articulated by Magistrate Judge Farrish does not survive as a holding — even as the reasoning continues to influence how other courts approach the same question.
There is an open practical question about whether Shell can cure the defect by serving formal Rule 33 or 34 requests now to trigger the same obligation under a re-issued order. That depends on where the case management order has the parties in the discovery schedule, and the briefing does not address it. But if Shell can serve, you should expect them to do so. The procedural argument may slow the doctrine down. It is unlikely to stop it.
What happens if there really are no prompts
CLF’s substantive position is that no prompts or outputs were preserved and that nothing exists to produce. The trajectory of that representation has tightened over time — from “no experimental prompts were stored” in August 2025, to “I did not export or preserve a complete native prompt/output log” in Dr. Kaurov’s December declaration, to the current position in the Rule 72(a) Objection that no log exists in any form. CLF has repeated the representation at least ten times on the record at the May 14 hearing.
Magistrate Judge Farrish did not find that prompts exist. He found that Shell had a non-speculative reason to disbelieve CLF’s representation, and that CLF therefore had to put its representation on the record under oath with sanctions available if anything turns up. That is trust-but-verify, not a finding of bad faith.
But there is a deeper exposure here that the briefing does not fully grapple with, and it is the exposure every litigator advising on AI preservation needs to internalize. Even if Dr. Kaurov’s local system retained nothing, the data CLF processed through Microsoft Azure OpenAI Service is presumptively within CLF’s possession, custody, or control. Dr. Kaurov was Microsoft’s customer. The producing party cannot stop at its own laptop. It has to make a reasonable inquiry into where its data lives, and that inquiry includes asking the cloud vendor. If CLF wants to stand on “nothing exists,” it has to be able to show it asked Microsoft and that Microsoft retained nothing.
And if Microsoft retained something — Azure’s own documentation acknowledges that the Azure OpenAI Service may retain prompts and outputs temporarily for abuse monitoring — CLF has to produce it. If Microsoft retained nothing because the deployment was configured for zero retention, CLF is sitting on a Rule 37(e) preservation question. We covered the framework for that analysis in our PharmacyChecker episode. The duty to take reasonable steps to preserve a known source of ESI is a duty counsel has to supervise. Counsel cannot delegate the preservation decision to a research assistant or an IT contact and walk away. Dr. Kaurov is the person who decided not to export a complete prompt/output log. Was CLF’s litigation counsel involved in that decision? The briefing does not say. That silence is the exposure.
So the “nothing to produce” question is not resolved. It is just deferred. Either prompts exist on CLF’s systems and CLF will have to produce them. Or prompts exist on Microsoft’s systems and CLF will have to retrieve them. Or nothing exists anywhere and CLF is in Rule 37(e) territory for failure to preserve a workstream it knew was central to expert testimony. None of those paths is a clean win for CLF.
The Florida rule and the pattern courts are converging on
Earlier this week, on June 15, 2026, the Florida Supreme Court’s amendment to Rule 2.515(d)(2) of the Florida Rules of General Practice and Judicial Administration took effect. The amendment was adopted on May 28 in Case No. SC2026-0673 and is paired with a companion administrative order that preempts the patchwork of circuit-level AI disclosure orders that had grown up across the state. There is now one statewide standard.
The rule does three things, and it is important to be precise about what each of them is. By signing a court filing, every signer now represents that “the legal authorities identified exist and are accurately cited.” The amendment expressly authorizes courts to impose sanctions for any filing inconsistent with that representation — reprimand, contempt, striking the document, dismissal, costs, attorneys’ fees — after notice and an opportunity to be heard. And the rule does not require disclosure of AI use. It does not require certification that AI was not used. It does not ban AI. What it does is put accountability on the signer. If a hallucinated citation lands in a Florida brief, the question is no longer whether the AI did it. The question is whether the signer verified the authority before filing. The signer is on the hook.
That principle — that AI does not change accountability — is the through-line connecting a Connecticut expert discovery order to a Florida court filing rule. Courts are converging on it across very different procedural postures. The lawyer is responsible for what is filed. The expert is responsible for her methodology. The litigant is responsible for the responses she serves under oath. The fact that a tool produced the output does not insulate any of them.
For litigators with Florida-venued matters, the downstream effect on expert practice is significant. If your expert’s report is going to be incorporated into a motion, a Daubert brief, or any other court filing in Florida, the lawyer who signs that filing is making a representation about every authority cited in it — including authorities the expert pulled with an AI tool. Accountability does not stop at the expert’s office. It runs all the way to the signature block. Your firm’s verification protocol has to extend not just to the lawyers who use AI, but to the experts, consultants, and assistants whose work product gets folded into your filings.
What this means for client conversations right now
This decision is already shaping client conversations, and the speed at which those conversations are arriving is its own data point. In the last week alone, I have had two client calls that turned directly on the issues this opinion raises.
The first call was from a lawyer asking how he should be disclosing his use of AI to his own clients. Not how AI use should be documented in expert reports, not how to draft a Rule 29 stipulation — how to talk to his clients about it. What needs to be in the conversation? What needs to be in writing? Does using AI to draft an opposition memo, or to summarize deposition transcripts, require client consent? That question used to be a year-out future-state question. After Conservation Law Foundation and the Florida rule landing in the same two-week window, it is a this-week question.
The second call was from a lawyer asking what needs to be in her engagement letter about her firm’s use of AI and about her clients’ use of AI when interacting with her firm. Specifically, what protections need to be built into the engagement around confidential information being uploaded — by the lawyer or by the client — into any AI tool? Those are the right questions to be asking. And the framework these cases give you is one I would not have been able to articulate cleanly six months ago.
Here is what those conversations should be covering today, in any client engagement:
- Are you using AI in your work for this client? In what form, on what platforms, with what data?
- Is your client using AI in connection with the matter? On a public consumer tool, an enterprise deployment, or a tool licensed and supervised by the firm?
- For any expert you retain, what AI use is permitted, what is required to be preserved, and what disclosure expectations attach?
- If a tool is used, who supervises the preservation of inputs and outputs? Where do those preserved artifacts live? Who has access? When does retention expire?
- If the matter involves confidential information that may be uploaded to an AI tool, what are the contractual protections governing that upload? Have you reviewed the AI vendor’s terms in their current version?
You do not have to have perfect answers to those questions today. But you do have to be asking them — and you have to be putting the answers in writing. The next round of these decisions is going to ask whether counsel did the asking, and whether the answers were documented. If the only record of your client’s AI practices lives in someone’s head, the next opposing counsel to move for sanctions will exploit the silence.
What to do this quarter
If you take nothing else from this opinion, take this. Treat your testifying expert’s AI prompts as discoverable methodology starting now, regardless of how the district court rules on the Rule 72(a) Objection. The framework Magistrate Judge Farrish articulated is now public, it is going to influence how other courts approach the same question, and waiting to see how this one shakes out is not a defensible strategy. Have the conversation with every expert you retain at the engagement letter stage. Document AI use, preservation policies, and the contractual configuration of any AI platform the expert intends to use. Build a preservation plan that captures wrapper prompts, iterative refinements, and outputs — not just keyword lists.
Beyond that:
- Audit every active Rule 29 stipulation, protective order, and ESI protocol against the Conservation Law Foundation framing. If your existing language relies on “notes,” “drafts,” or “communications” to sweep in AI artifacts, it does not work anymore. Name the artifacts: AI prompts, AI queries, AI outputs, embeddings, model parameters. If you are negotiating new language, draft from Magistrate Judge Braswell’s Morgan protective order language as a starting point and extend it to cover experts and consultants.
- If you practice in Florida or have matters venued there, the Rule 2.515(d)(2) certification is now operative on every filing as of this past Monday, June 15, 2026. Build a written verification protocol for every authority cited in a brief, regardless of whether AI was used in drafting. The rule does not require disclosure — it requires accuracy. The protection is process.
- Update your client conversations and engagement letters. Document what AI tools are in use, by whom, on what platforms, with what data. The two client calls I described above are not outliers. They are arriving in volume now, and the lawyers who can answer those questions credibly will retain clients. The lawyers who cannot will be replaced.
- If you are advising a client whose expert is currently using AI, ask the preservation question today. Where are the prompts and outputs being preserved? On the AI vendor’s side, on the expert’s local system, or both? What is the retention schedule on the vendor side? Is the deployment configured for zero retention? If your client’s expert cannot answer those questions, your client has a Rule 37(e) preservation question building in real time.
- For represented parties using AI without their lawyer’s direction, read or re-read the Heppner framework. Heppner does not just apply to one corporate executive in one securities fraud case. It applies to every represented client who is using AI on their own to think through their case. White-collar counsel especially should be having this conversation with every executive client this quarter.
What happens next
Judge Oliver has several paths in front of him, and I do not think this is a one-outcome case.
The narrow path is procedural. Judge Oliver could vacate the order on the Rule 33/34/37 procedural defect alone — no formal requests served, so no enforceable Rule 37 motion, so the magistrate’s order rests on a discovery mechanism that does not exist. That outcome leaves the substantive question — whether AI prompts used by a testifying expert are discoverable as methodology — completely unanswered. The framework that has been getting all the press coverage would come off the books. The next time this question arises, the next court would be writing on a much closer to blank page, with this decision available as reasoning but not as a holding.
The broader path is substantive. Judge Oliver could affirm that an expert’s AI methodology is fair game under Rule 26, that prompts are part of that methodology, and leave Magistrate Judge Farrish’s framework in place — but adjust the enforcement piece given CLF’s representation that no prompt logs were preserved. That outcome keeps the doctrine alive but takes some teeth out of the order.
And there are paths in between. Judge Oliver could affirm the methodology principle, vacate the Rule 33/34 enforcement mechanism, and direct the parties to a different procedural posture. He could remand for further factual development. He could wait on the related Daubert motion to exclude Dr. Oreskes’ testimony, which is on his docket at ECF No. 741 and rests on overlapping factual ground.
The framework that everyone is treating as settled is still very much up for grabs. We will be watching the docket. When Judge Oliver rules, we will come back to this on a future Case of the Week episode and walk through what the district court did with each of these arguments. Whatever happens next is going to be a teaching moment for the body of law we have been building all spring.
Listen to the full episode
This week’s Case of the Week segment of the Meet and Confer podcast walks through Conservation Law Foundation v. Shell in detail, including the full analysis of the publicly available briefs from the parties’ letter briefing and CLF’s Rule 72(a) Objection. Listen on Meet and Confer →
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Related on Minerva26: U.S. v. Heppner · Warner v. Gilbarco (Episode 183) · Morgan v. V2X, Inc. · PharmacyChecker.com v. NABP · Florida Supreme Court Case No. SC2026-0673
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