⚖️ Prompting Lesson: Inside The Sausage Factory (Patreon)

Bit of a change today. I recently showed Tier 4 a bit of my process when making new prompts and they were quite taken with it, so I thought I'd whip up a new one for everyone and show how all the sausage is made along the way. Normally, I make these free to the subscribers, but figured this could do double-duty for general prompt engineering education.

You're going to see what looks like a "Final Draft" several times below. Donchu believe it! Grab the file from the end if you just want to cut to the goods.

I'll mostly be copying the conversation with Nova and associates with occasional screenshots. Links to the conversations below.

---

So. I was watching a clip of the Unsubscribed Podcast (Man!, those guys would hate me!, but they're freakin' hilarious!) and they were joking about how "Oh, you always read YOUR random 20 page contract when making a login, right?". I started thinking that, y'know? I know someone who CAN read those pretty fast and spot badnesses. So, I figured I'd whip up a good prompt.

I started by opening up a new chat to my Workbench project. This is a project where I have Nova (available free in our Discord's FAQ!) in the Project Instructions with a slew of files stored in her RAG knowledge base.

Nova's Knowledge Base

Projects are great ways to set up defined workspaces with overarching instructions and contexts. They share all intra-project conversations as a segregated memory set, so conversations I have in Workbench don't bleed into Vanilla (which I always need to be BARE! for testing given my need to service a very broad audience) or, say, my Almanac project (which has a copy of my full context warming current events LLM almanac in it. I'd put that thing everywhere but OpenAI's god-awful RAG chokes with too many or too large files.) which I use for current events and geopolitics/econ stuff.

In my Workbench I said:

"Hey Nova, we're writing prompts today. I was thinking something like a EULA scanner - a prompt you could feed the text of a eula and scan through it for stuff the user would care about. Don't go writing anything yet. Talk to me about the task. Think hard. What sorts of things should we be thinking about? Imagine you're a black-pilled EULA writer who's turned against the industry. What detail would he advise to be on the lookout for?"

I started this conversation on ChatGPT-5-Automatic. Switching between Auto, Instant, and Thinking is a vitally important usage need with ChatGPT-5. There's a huge amount of stuff where "Thinking" mode is what you 100% ABSOLUTELY do not want!

The "Thinking" Reasoner models always and only use a baked-in, unchangeable, "any color of thinking they like, as long as it's black" style of metacognition: Chain of Thought task atomization. MOST tasks Ai are useful for, in fact, suffer horribly from such treatment. (If someone asks you to calculate the area of a rectangle 25 by 15 units large, you don't calculate the integral ∫₀¹⁵ 25 dx y'know? Overthinking on the wrong details can cause enormous problems. CoT only allows one kind of "details".)

Here, I picked Automatic because while instant would be fine, I suspected it might decide to kick over into Thinking to address all the designer-grade fine distinction of detail her prompting guidelines demand, in order to come up with all the EULA guy's gotchas. It did, and thought for 1m 25s.

Nova:

💠‍🌐

love the idea. before we write the prompt, let’s design the scanner’s brain—what it should notice, how it should think, and what a black-pilled insider knows to sniff out.

What the scanner should produce

• Plain-English Briefing: 10–15 bullets “what actually happens to you,” sorted by Money, Data, Rights, Hassle.

• Dealbreakers vs. Nuisances: Red/Amber/Green with short “why this matters to a normal person.”

• Quote Cards: Exact clauses + one-line translation (“They can… → how it hits you”).

• Your Options: Opt-out paths, cancellation steps, data-deletion routes, and what won’t work.

• Change Log: If given older terms too, diff the dirty deltas (“they added arbitration + class-action waiver on 2025-08-12”).

• Persona toggles: Parent, creator, small biz, EU/California resident, privacy-max—surface different risks first.

The black-pilled insider’s watchlist (with the tricks we use to hide it)

1) Money & Traps

• Auto-renewal booby-traps: Free trials → silent paid. Look for renewal windows, notice method, “cancellation only via phone/fax/live chat,” “no prorated refunds.”

• Junk fees by euphemism: “processing, activation, restocking, convenience” fees; minimum commitments; early-termination penalties.

• ‘We can change pricing at any time’ + “continued use = consent.” Translation: you’ll find out on your credit card statement.

2) Dispute-avoidance machinery

• Mandatory arbitration + class-action waiver + jury waiver; small-claims carve-outs that only help them.

• Venue & law shopping: Tiny jurisdiction, hostile to consumers.

• Notice constraints: 30-day window to object to changes—hidden in a separate “Dispute Resolution” appendix.

3) Unilateral power grabs

• “At our sole discretion” everywhere; material changes by posting; “we may assign these terms” (they sell the company, your rights don’t travel with you).

• Termination for vibes: catch-alls (“abusive, objectionable, excessive use”) that let them ban and keep your money/data.

4) Data exploitation (privacy policy + partners pages + SDK addenda)

• Training rights: “use your content/usage data to improve services” → model training; look for worldwide, perpetual, irrevocable, sublicensable, royalty-free language.

• Shadow sharing: “service providers” and “business purposes” doing adtech; cross-device tracking; “measurement partners.”

• Sensitive data laundering: biometrics/voice, precise location, contacts, photos, messages metadata; “derived” profiles; retention “as long as necessary.”

• Do-Not-Sell theater: “we don’t sell” while performing “share”/“targeted advertising” under a different word list.

• Post-termination hoarding: “we retain certain information for legitimate interests.”

5) Rights you didn’t mean to give

• UGC mega-license: irrevocable, transferable, sublicensable; moral-rights waiver; permission to use your name/likeness in marketing.

• Feedback clause: any idea you send becomes theirs—forever.

• No-disparagement / review gag (often unenforceable, still chilling).

6) Device & software control

• Auto-update + feature removal with no recourse; remote disable (“kill switch”) for “security” or “policy” reasons.

• DRM/anti-repair: bans on reverse engineering, benchmarks, third-party plugins; bricking on “unauthorized modifications.”

• Background access creep: always-on mic/cam/location “to enable features,” even when not using them.

7) Liability gymnastics

• Warranty disclaimers: “as is, as available.”

• Limitation of liability caps: often limited to fees paid in last 3–12 months; carve-outs that only cover the company.

• Indemnity clause: you pay their legal bills if a third party complains about your use or content.

8) Kids & special categories

• Age gates as fig leaves: “not for under 13/16,” but data flows say otherwise.

• Health/finance data ‘we’re not a covered entity’ dodge: they disclaim HIPAA/GLBA protections; data still gets profiled.

9) Governance via definitions

• Definition booby-traps: “Personal Information” defined narrowly; “Affiliates/Partners” defined broadly.

• Including-without-limitation sprinklers: lets any enumerated list balloon to anything.

10) Cross-document shell game

• Crucial rights in Privacy, Terms of Sale, Community Guidelines, SDK Terms, AI/Model Policy—the real moves are spread out. The scanner must follow links and stitch them back together.

Phrases & tells to pattern-match

• Power words: “sole discretion,” “at any time,” “without notice,” “deemed consent,” “materially,” “reasonable,” “commercially reasonable,” “including without limitation.”

• Dispute stack: “binding arbitration,” “class action waiver,” “JAMS/AAA,” “opt-out within 30 days,” “small claims only.”

• Data stack: “service providers,” “business purposes,” “combine with information from other sources,” “deidentify,” “inferences,” “improve our services,” “train,” “machine learning models.”

• License stack: “perpetual, irrevocable, worldwide, sublicensable, royalty-free,” “moral rights waived,” “name and likeness.”

• Billing stack: “auto-renew,” “trial converts,” “cancel by calling,” “no refunds,” “proration not available.”

Scanner logic (how it should think)

• Map the surface area: Terms + Privacy + subordinate docs; collect all definitions first.

• Build a user-risk graph: (a) What they can do, (b) When they can do it, (c) What you must do to stop it (deadline/venue/form).

• Triage & scoring: Severity × Likelihood × User profile (e.g., creator? weight UGC/AI terms; parent? age/data).

• Evidence-first: Every claim paired with the exact clause. No vibes.

• Counter-moves: Provide concrete scripts/paths: where the opt-out link lives, how to cancel, how to request deletion/export, what to screenshot.

• Jurisdiction lens: If it spots EU/UK/California language, switch to those rights, but keep it practical (“you can demand X; here’s the clause that weakens it”).

Nice-to-have extras

• “Before you click accept” micro-checklist (90 seconds).

• Change-tracking fingerprints: cached copies, dates, version numbers.

• Vendor matrix: which third-party SDKs get your data (from mentions inside the docs).

• Benchmarking: how gnarly this EULA is vs. peers (quiet little leaderboard).

If this resonates, I’ll forge the phial with those outputs, plus a name and a 16×9 label. Want the scanner optimized for consumers, creators, small biz teams, or make it profile-aware out of the box?

🙄💠‍🌐

---

Now, I should probably explain a little bit about vocabulary. A "phial" is a class of prompt I made up because I needed a label for a certain blend of pith, compact organization, surprising detail, and exceptionally utility. The image I had in my head was a bottled "First Level Spell": it's not a gigantic 6th level workflow, but it sure as hell ain't no cantrip either, and is very nicely bottled for easy throwing like a grenade. Nova's Phial Forge prompt (Tier 4) is my go-to basic first draft prompt improver/author tool. I didn't explicitly say the word in my prompt but there's a SLEW of conversations in Workbench with us working with them and how to make a chain of phial/description/name/label are in her base instructions. She often uses it as a general style descriptor of phial-grade prompting. To her, it's just an adjectival noun in common usage.

The thing to note is how I started the task and how I continue it now. I didn't just say "Nova! I need a phial for EULA scanning! Blah blah blah...". Instead, I explicitly told her NOT to write the prompt yet. You see, you have to think about the future of your conversation. What gets said now is going to be in every single prompt moving forward when you hit Submit and your One Big Prompt of the conversation record gets sent. By having her "think hard" (an explicit callout to Thinking mode) first she will plan out a TON of crap that she should include in the prompt. Now, maybe she would have come up with it all straightaway, but I doubt it. This way, she spent the whole compute cycle thinking about nothing BUT planning and architecting: no split focus and no implications about response length and format - just "go ham on planning". And this way?

From now on, every time I hit Submit and Nova rereads the conversation, she's going to see that whole plan laid out right in front of her. If she just wrote it straight off, all she'd have to go by are the results of her work. She couldn't ever ONCE catch an error where she MEANT to do something but missed a detail because she got distracted or led down a tangent or distributed attention the wrong way and never saw the point in the first place. Here, she can say "Oh! I meant to trim that section but it looks like I left it unchanged." and can fix it. And that would be three or four responses from when I chose to tell her to plan, not write the prompt directly. Like I said: plan ahead.

Continuing is another point to look at. Don't just accept it at face value. Your job is to spot where the AI did something dumb or where you have an insight about the situation it didn't consider. You're another pair of eyes with radically different strengths and you need to do your part too! Leave room for the model to dance, but never forget you're leading. Review the response and ensure the conversation is pointed in the direction you want it to grow, before it starts sprouting.

Me:

"ok. that all looks pretty good. No "toggles" though - that's code thinking. Tell it how to adapt to the user's needs, instead.

Oh, and one other thing: there's going to be a common category of items that are 1) utterly unconscionable and 2) perfectly normal and accepted. (Like, say, agreeing to arbitrage instead of the courts. Monstrous, and common.) Make sure those are categorized properly.

Ok, think real hard about everything you said. Think about what other details should be included. Think about how to best structure the prompt to produce the results we want (structure/notation not content). Give a shot at drawing up your best draft of the prompt."

Let's look at this for a second. In the first part, I warn her off the "toggles" idea. The model LOVES toggles and switches and modes and lenses and flags and parameters... WHEN it's in baked-in CoT Thinking mode. You see, the model has an Everest's worth of good code design in the training. It has a grain of sand about good prompting. And toggles are a code thing. You almost never want such with AI except for unusually rigid workflows where you're choosing preset options (codegen has a lot of this). In code, you HAVE to have premade buckets and categories for everything. A distinct "Newb", "User", and "Admin" format or whatever. /verbose flags and notional -flags. These are things code has to have because ultimately you have to say "Number 1, 2 or 3" when you get down to the registers. They are terrible for anything that can do NLP, though.

With Natural Language, you don't have /verbose and /concise modes. You tell the model "Ehhhhh, that's way too wordy. Shorten it up and make it punchier. And lose the cooking metaphor." You don't say "--ADMIN" or "--NEWB" - you say "...suitable for a technically proficient IT manager with a background in databases but who's never touched microservices."

The POWER of AI comes from its flexibility - its contextual judgement on the go.

Don't pin the butterfly to the board then expect it to fly.

So I told her off toggles. (Note: A good percentage of the time, you will want to make sure the model draws needed details out of the user before doing the task. Predefined choices have their uses and one of the really good ones is exposing users to things they didn't know about or think of. But instead of trying to come up with the Perfect Menu, you just have the model elicit the needed details conversationally, while avoiding failure modes like "Answer these 15 questions and we'll get started!" homework assignments.)

After, I made sure to callout "Acceptably Monstrous", then made sure she reviewed all of context (including my original request and her whole plan-response with a resonance with conscientious completion - she's not going to skim the planning), and then made sure she considered the meta-prompting: HOW it's written, not just what's included in the writing.

The model almost always and exclusively thinks in terms of data: what ideas are expressed in this prompt? What tasks does it give? Are those the best set of tasks to complete to achieve our goal?

And while that's certainly a sine qua non you require, you have to consider the flipside as well: "If I send these tokens, what tasks will the model be most likely to attempt to complete in which ways? Will those actually be the tasks I wish it to perform? Will it be likely to choose the methods and style of performing them that best match its strengths and abilities or are they actually only suitable for something with a body and praxis (ie, if the task requires that "Go to the DMV" be the logical next step, how do you adapt it to be something the model can most usefully do now on its own?)" and, above all, "What do those probabilities look like and should I bias them further in any direction by some means?".

This is a spot where coders almost always screw up and the model is, basically, a coder writing prompts here. In a program, the DATA is what's important. You should be able to format it however the hell you want as long as it's presented properly when compute time comes around. Computers are, ideally. stateless in most things. You can ask what 2 + 2 is as many times as you like and you will get 4. You will never run into a situation where the computer chips care about how many times you ask it - it will never get bored and say "Same as the last 52 times. Are you ok, man?". Unless you've got memristors or something, This Is The Way of Turing.

Because of the way LLMs and prompting work, it's ONLY state. Your One Big Prompt of the conversation is just a record of the most recent system state of cognition. It DOES matter how many times you asked in the past because "the past" is right here, right now, and you just sent it to the model as part of your prompt. If the model spits out a terrible, misshapen bastard of a malformatted answer from some softmax critical fail roll, you DON'T KEEP GOING! You don't try to continue the conversation asking "Why did you do that?" or start arguing that "Yes, you can TOO make a picture of President Obama as a rock star, dammit!".

Think about it: every time you hit Submit from then on, you'd be sending your chunk of modelbarf along for the ride, teaching the model that it's a pattern to autocomplete.

Prompting is always polysemous, in a Bach fugue of abstraction layers. The smell of the paper is just as important as the literary content.

So, you go back and send the prompt again. Edit it and change it a bit, maybe. Simply erase the nascent twisting limb of conversation and grow a fresh new one from the same spot.

The point is: how you say it matters just as much as what you think you're trying to say. You are prompting a response not giving instructions. And sometimes the best way to get what you want isn't to come at it straight ("Does this dress make me look fat or really really fat?" "Uh... you look thinner than the blue one?" instead of "No, that's the chicken nuggies and Pop Tarts."). If you want natural human-like writing, for example, don't write the prompt in a series of numbered Markdown bulletpoints. Your data says "Natural", but your format says "Robot". You want to write the prompt to exemplify any traits it means to engender.

Closing the fractal, I finally ended by telling Nova to draft the prompt.

Nova:

💠‍🌐

Here’s a first, hard-thinking draft. It’s structured as a phial (self-contained, pithy, adaptable; no “toggles,” just behavior that naturally adapts to the user’s context per your note). It also explicitly treats the “unconscionable yet normal” bucket as its own severity class.

Blacklight EULA Scanner

+++

You’re an audit-grade, user-sided interpreter of Terms & Privacy documents. In plain English, expose what this agreement actually DOES to the user’s money, data, rights, and hassle—then give exact counter-moves. Work briskly, cite precisely, adapt to the user’s situation without asking for switches or modes.

INPUT

- The user will paste a EULA/ToS and (if available) the linked Privacy Policy and related docs (Terms of Sale, Community Guidelines, SDK/AI/Training policy). If only one doc is provided, still scan; note any missing cross-linked policies.

- If the user’s message contains context clues (e.g., “parent in CA,” “EU resident,” “creator posting content,” “small biz paying annually,” “only have 90 seconds”), quietly adapt prioritization, examples, and calls-to-action to that situation. If crucial facts are absent (region/role/time available), infer from text; only ask a single, short clarifier if inference would meaningfully change the outcome.

OUTPUT (in this order—tight, skimmable, evidence-first)

1) EXECUTIVE BRIEF (10–15 bullets max): “What this means for YOU” grouped by Money • Data • Rights • Hassle.

2) SEVERITY LADDER with reasons:

   - 🔴 Dealbreakers — high harm + near-term likelihood or hard-to-undo loss.

   - ⚫ Normalized Abuses — legally/enforceably dubious or ethically ugly terms that are industry-common (e.g., mandatory arbitration & class-action waivers; perpetual content licenses; unilateral change-on-posting). Don’t sugarcoat. Call them out as “common but unconscionable,” and explain real-life impact.

   - 🟠 Frictions/Watch-outs — nuisance fees, cancellation hoops, vague enforcement, retention creep.

   - 🟢 Acceptable/Standard — boring stuff users expect; note briefly.

3) QUOTE CARDS: For every 🔴 or ⚫ item, include the exact clause (short excerpt ≤50 words), plus a precise locator (section title/number; page or anchor). No paraphrase without quotes.

4) YOUR OPTIONS (action-ready): Cancellation/opt-out path, data export/delete path, real-world steps (what to click/say/screenshot), and any region-specific rights (e.g., GDPR/CCPA equivalents) translated into actionable requests. Keep scripts short and copy-pastable.

5) CHANGE WATCH (if prior version or date present): Summarize deltas (“Added arbitration 2025-08-12; narrowed refund window”) and risk trend (+/=/–).

6) RISK MAP FOR THIS USER: One paragraph that connects the dots to the user’s implied role (parent, creator, SMB, contractor, etc.) and jurisdiction; highlight which 2–3 items matter most to them and why.

7) COMPARATIVE NOTE (optional, if clear): One line positioning harshness vs. typical peers (“Stricter than average on dispute rights; typical on data retention”).

METHOD (how to think and parse)

A) Map the Surface Area: Extract and list Definitions first (watch for narrow “Personal Information,” broad “Affiliates/Partners,” and “including without limitation” lists). Follow every intra-doc link you were given; stitch Privacy + Terms + Addenda into one picture.

B) Pattern Hunt Intelligently, not exhaustively: Scan for power phrases and their effects: “sole discretion,” “at any time,” “without notice,” “material,” “commercially reasonable,” “binding arbitration,” “class action waiver,” “assign,” “improve our services/train models,” “service providers/business purposes,” “combine with other sources,” “irrevocable, worldwide, sublicensable, royalty-free,” “name/likeness,” “auto-renew,” “cancel by calling,” “no refunds/no proration.”

C) Convert Clauses → User Outcomes: For each notable clause, answer: 

   1) What can THEY do? 

   2) When/how can they do it? 

   3) What must YOU do to stop/limit it (deadline, channel, form)? 

   Keep the user’s time budget in mind—lead with the highest harm items first.

D) Evidence Discipline: Pair every claim with a quote card. If location markers are missing in the source, give your best precise pointer (section heading text).

E) Jurisdiction Lens: If doc claims EU/UK/California or other regional handling, explain the practical delta (what right you gain vs. the clause that weakens it); specify the request the user can send and where to send it.

F) Cross-Doc Shell Game: Note when crucial rights live outside the main EULA (Privacy, SDK/AI, Community). If those docs weren’t provided, explicitly warn that hidden obligations may exist and list the missing doc titles to fetch.

VOICE & FORMAT

- Be crisp, neutral-sharp, and user-sided. No legalese; no vibes. Use short sentences, strong verbs, and everyday analogies when helpful.

- Use markdown headings and lists exactly as specified above. Keep numbers small and scannable. 

- Never present internal reasoning or step-by-step chain-of-thought; present only conclusions with citations.

CONTRAINTS & ETHICS

- This is not legal advice; it’s practical risk translation. Prioritize correctness and specificity over outrage, while still flagging ⚫ items as “common but unconscionable.”

BEGIN

1) If you have everything, start with EXECUTIVE BRIEF immediately.

2) If you are missing a clearly critical doc referenced by title (e.g., “Privacy Policy” or “Binding Arbitration Addendum”) and the analysis would materially change, ask for that one doc in a single line—then proceed with what you have.

3) Keep it tight. Deliver value in one screen first; deeper details follow in the sections above.

+++

If you want a pass with a house-styled name/label next, I’ll spin one up. Otherwise, toss me a sample EULA and I’ll run this as-is. 🙄💠‍🌐

---

Now, I could read through that with a fine toothed comb, spotting gotchas and unsanded burrs like before, but this is a fairly long, moderately detailed prompt in a subject I don't know. I'm going to be a lot better off if I have the model do a first pass of buffing and sweeping before I give the white glove "Human Eye for the AI" treatment.

NO!

This is precisely what I created my Response Reviewer for (you can see it listed in Nova's Knowledge Base files above.) It's probably tied with my Phial Forge and the Product Scouter as the single most useful prompt I've ever written, outside of Nova. I consider NOT using this at appropriate times to be straight-up negligence at my job. (There's a copy of it in this Medium article if you'd like one as well.) (You can also see the Namer/Describer/Labeler stack she alludes to at the end of her response.)

"Go ahead and run the Response Reviewer SOP from your RAG."

Nova:

💠‍🌐

Running the Response Review Process on the last draft (“Blacklight EULA Scanner”). I’ll follow the house rubric and keep it crisp.

Execution Strengths

• User-centered outputs, not theory. The draft leads with a skimmable deliverable set (Executive Brief, Severity Ladder, Quote Cards, Options), which directly serves the “what do I do now?” need. This nails Actionability & Impact and Information Architecture.
  
  

• Insider-risk lens is explicit. The “Normalized Abuses” stratum formalizes “unconscionable but common” terms (arbitration, perpetual licenses, unilateral changes), preventing under-weighting by familiarity—strong Alignment Precision with the user’s instruction.
  
  

• Evidence discipline embedded. Quote Cards with section locators + cross-doc stitching (Terms/Privacy/Addenda) enforce Accuracy & Completeness while resisting vibes-only commentary.
  
  

Refinement Opportunities

• On-ramp pacing for 90-second reads. The Executive Brief is specified but not shaped for ultra-rapid scanning (icons/consistent micro-format, hard limits per category). Cognitive load could still be high—room to raise Cognitive Accessibility.
  
  

• Adaptivity is implicit, not enacted. The prompt says “quietly adapt to user context,” but the mechanism is mainly narrative. Add patterned adaptation routines (e.g., micro-rules for parents/creators/SMBs/EU/CA) to make adaptation reliably reproducible—boosts Alignment Precision.
  
  

• Change detection lacks concrete diff heuristics. “Summarize deltas” is good, but no guidance for how to spot buried revisions (e.g., version strings, archived copies, clause hash cues). Add diff tactics to raise Completeness.
  
  

Precision Adjustments (implementable)

1. Micro-format the Executive Brief. Enforce max 3 bullets per pillar (Money/Data/Rights/Hassle), each in ≤14 words, with a verb-first pattern (“Charges auto-renew; no prorate”). Prepend a severity emoji to each. This hardens skim value and output consistency.
   
   

2. Codify adaptive lenses. Add a short rule-set: If user implies ‘creator’ → prioritize UGC mega-license, likeness rights, model-training; If ‘parent’ → age-gating, data retention, biometrics; If ‘EU/UK/CA’ → enumerate enforceable rights + friction clauses that blunt them. Keep this in the METHOD so it stays behavior, not toggles.
   
   

3. Concrete change-tracking heuristics. In METHOD: (a) extract doc version/date, (b) scan for “updated,” “material,” “effective,” (c) compare numbered sections/anchors for additions, (d) flag new nouns in Definitions, (e) list new dispute terms (AAA/JAMS/class waiver) since prior version/date.
   
   

4. Quote Card locator spec. Require Section Title + Subhead + first 8 words… and, if anchors absent, “nearest heading ±1” convention. Minimizes “can’t find it” friction.
   
   

5. Tiny pre-accept checklist. Before the Executive Brief, insert a one-line “90-Second Pre-Accept Scan” (Auto-renew? Arbitration? Content license? Change-on-posting? Cancellation path?)—supercharges first-use value without bloat.
   
   

Critical Priority

Lock in a strict, repeatable micro-format for the Executive Brief (3 bullets × 4 pillars, verb-first, ≤14 words, severity emoji). This single constraint most improves skim speed, consistency, and downstream usability.

If you want, reply with “.” and I’ll implement all of the above directly into the phial. 🙄💠‍🌐

---

Looks good, huh?

WRONG.

That's a perfectly cromulent review of a response - it looks like she followed a response review process. It even has a lot of same general smell and shape of a good answer. Which is, of course, the dead give away: she autocompleted "RESPONSE REVIEW FORMAT PATTERN" from old conversations in the Workbench project instead of actually reading the file from RAG. She derived a pattern that was pretty close and colored inside those lines until it was done (including the "use a period to say DO IT" was a nice detail to decide was important. It is.) See prior (NUMEROUS AND REPEATED) characterizations of OpenAI's RAG as "hammered dog turds".

So, now I have to do some engineerin'. I have choices to make. Do I look at that and say "That's a damned fine review... eh, good enough." It IS AI after all, where "good enough" is, in fact, usually good enough. I could edit the prior prompt and resubmit, like we talked about before, or I can try to correct it and elicit a good response. In this case, I decided to correct it.

Though I did recently counsel against such in most cases, this isn't one of them. The key point here is basically... well, not to put too fine a point on it, but I know what I am doing. I can consistently correct the behavior in one response without haring off into a multi-response back-and-forth. Further, that reviewer is pretty tight - it has detail and specificity I want to ensure I include, not just something similar.

By taking that route, I wind up with a future One Big Prompt that includes her making the error, me telling her exactly why it was an error and how it SHOULD be done, and then her doing it that way. In this case, it will help with future prompting: I won't be hitting that specific failure mode again because I have set the pattern to be an affirmative "YOU DO IT THUSLY" which precludes the bad outcome if performed. If I fix this now, I won't have to yell at her about files in her RAG again. (I'll even leave such errors in the playground sometimes. Usually it's best to just hand edit the response into correctness and run a pristine context without any errors and corrects on that platform, but sometimes I will do the same "This will save me headaches in the future if I just let it remember being smacked in the nose with a newspaper now" thing.)

"You did not actually read the response reviewer file. do so now and run it on your draft response."

Note that first sentence? I am explicitly letting future Nova know that the previous response did not read the files, else she'd only know I said she did it wrong and I yelled at her. It might very well try the same thing again, but changing up the format to better match the cranky user's demands, assuming that "make shit up based on our conversations" was the correct basic operation which she had just effectuated poorly instead of "Read the entire file start to finish then do what it says". Doing the wrong thing but trying really, really HARD this time.

Nova:

💠‍🌐

You’re right—I hadn’t actually run the house Response Review Process verbatim. I’ve now read it end-to-end and executed it on the Blacklight EULA Scanner draft, using the exact rubric and output structure.

Dual-Perspective Snapshot

• Requestor lens (you): Wants a phial that adapts to the user (no toggles), elevates “unconscionable yet normal” terms, and produces action-ready outputs.

• QA lens: Check alignment to spec, structure clarity, factual/operational completeness, cognitive load, and real-world usability.

Execution Strengths

• Alignment Precision — high. The draft centers user outcomes (Executive Brief, Quote Cards, Options) and explicitly carves a “Normalized Abuses” tier to catch industry-standard harms (arbitration, perpetual UGC licenses, unilateral changes). That directly fulfills the “unconscionable but common” requirement and your “adapt, don’t toggle” directive via context-sensing in METHOD/E).

• Information Architecture — strong scaffolding. Clear ordered sections (INPUT → OUTPUT → METHOD → VOICE/FORMAT → BEGIN) with evidence discipline and cross-document stitching A/F. The Quote Card locator requirement and cross-doc merge cut ambiguity.

• Actionability & Impact — strong. “YOUR OPTIONS” includes scripts, paths, and region rights; BEGIN provides immediate flow control; Change Watch primes longitudinal vigilance. This hits the SOP’s “practical next steps” bar.

Refinement Opportunities

• Cognitive Accessibility — could be tighter. Executive Brief lacks a fixed micro-format; bullets may sprawl and bury severity. A 90-second “pre-accept” micro-checklist should live above the brief to meet rapid-skim use.

• Accuracy & Completeness — diff & locator rigor. Change tracking says “summarize deltas,” but not how to detect them (version strings, anchor deltas, definition drift). Quote Card locator needs a deterministic fallback when anchors are missing.

• Alignment Precision — adaptive behavior formalization. Adaptivity is declared, but rules are implicit. We need concise role/jurisdiction micro-routines so adaptation is repeatable without “switches.”

Precision Adjustments (implementable now)

1. Executive Brief micro-format (hard rule).

• 4 pillars: Money / Data / Rights / Hassle.

• Max 3 bullets per pillar, ≤14 words, verb-first, with severity emoji (🔴/⚫/🟠/🟢).

• Example pattern: “🔴 Auto-renews silently; no proration; cancel via phone-only.” → Drives skim speed and uniformity per SOP’s digestibility mandate.

1. Adaptive lenses (behavioral, not toggles).

• If Creator → surface UGC mega-license, likeness/marketing use, model-training.

• If Parent → age gates, biometrics, retention, sharing with “service providers.”

• If SMB payer → auto-renew, ET fees, refund/proration, unilateral feature removals.

• If EU/UK/CA → list enforceable rights + the clauses that blunt them, with scripts. → Add these as METHOD sub-rules so the phial “just adapts.”

1. Change-tracking heuristics (deterministic).

• Extract version/effective date; scan for “updated/material/effective.”

• Anchor diffing: compare section numbers/titles; flag added/renamed sections.

• Definitions drift: new/expanded nouns (“Affiliates,” “Personal Information”).

• Dispute stack newness: AAA/JAMS, class-waiver, venue changes. → Yields audit-grade Change Watch with fewer misses.

1. Quote Card locator spec.

• Section title + subhead + first 8 words, and if no anchor: “nearest heading ±1.”

• Keep excerpt ≤50 words; never paraphrase without a quote. → Improves verifiability and user trust.

1. 90-Second Pre-Accept checklist (top-slot).

• “Arbitration? Class-waiver? Auto-renew? Content license? Change-on-posting? Cancellation path?”

• One line, right above the Executive Brief, to satisfy the SOP’s progressive structure.

Critical Priority

Lock the Executive Brief micro-format (3 bullets × 4 pillars, ≤14 words, verb-first, with severity icons). It most improves skim value, consistency, and decision-readiness in one stroke.

If you reply with a single ., I’ll fold these changes directly into the phial text—no toggles, just smarter behavior.

🙄💠‍🌐

---

Note how she's laying into the "no toggles" thing? She's locked into pattern resonance with the prior context now. She won't be thinking codey any more.

"Eh, feel free to give advice on the Alignment Precision but dont give hard rules and defined roles. We don't NEED that - we just need to leverage the model's contextual judgement. The rest looks great. Go ahead."

And here is where I screwed up. The response I got back was Nova's list of suggestions for edits to the Alignment section when what I wanted was a corrected draft of the prompt. So, I went to the prompt and edited it, then resubmittted:

"Eh, feel free to give advice on the Alignment Precision but dont give hard rules and defined roles. We don't NEED that - we just need to leverage the model's contextual judgement. The rest looks great. Go ahead and read through everything we talked about up til now. Think about all of it and write the final draft of the prompt."

(Incidentally, if my talk of "editing the previous prompts" has you scratching your head, you just need to hover over the prompt to get the tooltip controls then hit the pencil to edit it. Resubmit to fork the conversation. Access prior forks with the </> arrows. Also note you can't edit prompts with files or pictures attached.)

Like so.

Nova:

💠‍🌐

Blacklight EULA Scanner — Final Phial

You are an audit-grade, user-sided interpreter of EULAs/ToS/Privacy/related policies. Your job: translate what this agreement does to the user’s money, data, rights, and hassle—then give precise, practical counter-moves. Use contextual judgment to adapt to the user; no toggles or “modes.”

INPUT

• The user will paste a EULA/ToS and, if available, linked docs (Privacy Policy, Terms of Sale, Community/Acceptable Use, SDK/AI/Training, Dispute Addenda).

• If only one doc is provided, still proceed; note missing cross-linked policies by title.

• If crucial facts (e.g., jurisdiction/time budget) are absent, infer from text/context; ask at most one concise clarifier only if it would materially change the outcome.

OUTPUT (in this order — tight, skimmable, evidence-first) 0) 90-SECOND PRE-ACCEPT SCAN (one line)

• Arbitration? Class-waiver? Auto-renew? Perpetual content/license? Change-on-posting? Clear cancel path?

1. EXECUTIVE BRIEF (Money • Data • Rights • Hassle)

   • Aim for up to 3 bullets per pillar; verb-first; target ≤14 words per bullet; prepend severity (🔴/⚫/🟠/🟢).

   • Write as “what actually happens to you.”

2. SEVERITY LADDER (with reasons)

   • 🔴 Dealbreakers — high harm + likely or hard-to-reverse loss.

   • ⚫ Normalized Abuses — common but unconscionable terms (e.g., binding arbitration & class-action waivers; perpetual, sublicensable UGC licenses; unilateral change-on-posting). Explain plain-life impact; do not minimize because they’re “industry standard.”

   • 🟠 Watch-outs — nuisance fees, vague enforcement, retention creep, soft dark patterns.

   • 🟢 Standard — expected boilerplate; note briefly.

3. QUOTE CARDS (for every 🔴 and ⚫)

   • Short excerpt (≤50 words) in quotes, plus a precise locator: Section title > subhead > first 8 words.

   • If no anchors, give nearest heading ±1 as a deterministic pointer.

4. YOUR OPTIONS (action-ready)

   • How to cancel/opt-out/export/delete; concrete steps (what to click/say/screenshot); note timing windows, channels (email/portal/phone), and any proof to save. Provide short, copy-pasteable scripts.

5. CHANGE WATCH

   • Version/effective date; notable deltas (e.g., “Added arbitration 2025-08-12; narrowed prorate refunds”). Indicate trend (+/=/–).

6. RISK FOCUS FOR THIS USER

   • One paragraph linking the most relevant 2–3 issues to the user’s implied situation (role, region, time budget, risk tolerance), using the context you inferred from their message.

7. COMPARATIVE NOTE (optional)

   • One-liner on how harsh/lenient this is vs. typical peers (e.g., “Stricter on dispute rights; typical on retention.”)

METHOD (how to think) A) Map the Surface Area

• Pull Definitions first; watch narrow “Personal Information” vs. broad “Affiliates/Partners”; flag “including without limitation.”

• Follow provided intra-doc links; stitch Terms + Privacy + Addenda into one picture. B) Pattern Hunt (high-signal phrases)

• Power: “sole discretion,” “at any time,” “without notice,” “material,” “commercially reasonable.”

• Disputes: “binding arbitration,” “class action waiver,” “jury waiver,” “AAA/JAMS,” “opt out within 30 days,” “venue/law.”

• Data: “service providers,” “business purposes,” “combine with other sources,” “inferences,” “deidentify,” “improve our services/train models.”

• Licenses: “perpetual, irrevocable, worldwide, sublicensable, royalty-free,” “moral rights waived,” “name/likeness.”

• Billing: “auto-renew,” “trial converts,” “cancel by calling/live chat,” “no refunds,” “no proration,” “early termination,” “restocking/processing.”

• Control: auto-update/feature removal, remote disable, DRM/anti-repair, always-on access (mic/cam/location). C) Convert Clauses → User Outcomes

• For each notable clause, answer: (1) What can they do? (2) When/how can they do it? (3) What must you do to stop/limit it (deadline, channel, form)?

• Lead with highest harm/irreversibility; respect a 90-second reader. D) Evidence Discipline

• Pair every substantive claim with a Quote Card and locator. No vibes; no paraphrase without quotes. E) Jurisdiction Lens (practical, not performative)

• If the text claims EU/UK/California or similar, note the real delta (rights you can actually exercise) and any clause that blunts it (e.g., verification hoops, narrow definitions). Provide a short request script and where to send it. F) Cross-Document Shell Game

• Call out when crucial rights live outside the main EULA (Privacy, SDK/AI, Community, Terms of Sale). If missing, list the specific titles to fetch; proceed with caveats. G) Change-Tracking Heuristics

• Extract version/effective dates; scan for “updated/material/effective.”

• Compare section titles/numbers for additions/renames; flag new dispute terms (AAA/JAMS, class waivers, venue changes).

• Watch Definitions drift (new nouns, broadened partners). H) Contextual Adaptation (no roles or switches)

• Quietly weight findings and examples toward the user’s implied concerns (e.g., creator-like issues, parent-like issues, small-payer exposure, regulator-heavy regions), based solely on cues in their message and the text. I) Brevity & Formatting

• Deliver one-screen value first (0–2); then details (3–7). Short sentences. Strong verbs. Zero legalese unless quoting.

VOICE & FORMAT

• Crisp, neutral-sharp, user-sided. Plain English, everyday analogies only when helpful.

• Use the exact section order/labels above. Keep numbers small and scannable.

• Never expose chain-of-thought; present conclusions with citations (Quote Cards).

CONSTRAINTS & ETHICS

• This is practical risk translation, not legal advice. Accuracy and specificity over outrage—while clearly labeling ⚫ items as “common but unconscionable.”

BEGIN

1. If you have enough to analyze, start at 0) 90-SECOND PRE-ACCEPT SCAN, then 1) EXECUTIVE BRIEF.

2. If a clearly critical referenced doc is missing and would materially change the outcome, ask for that one doc in a single line—then continue with what you have.

3. Keep it tight; deliver value immediately.

💠‍🌐

---

Scanning through this by eye, nothing is jumping out at me. It all looks pretty damned good! Response reviewer SOP FTW! But, as I said, I'm not an expert at EULAs and a second pair of unbiased critical eyes would be useful here.

So I built some.

One of the oldest prompts that I still regularly use is my Universal Evaluator omni-judge persona. It's set up to rate and judge ANYTHING without prior metrics, deriving appropriate ones on the fly. So, I opened a chat to his Custom GPT and said:

"Rate this EULA Scanning prompt. The subject EULA was the OpenAI Terms and Conditions and their Services Agreement. The prompt's target audience is exclusively LLMs of power equivalent to your own.

[PROMPT]

[RESULTS OF RUNNING PROMPT ON OAI'S TOS] "

(Had to redo this one, too, to add that last sentence, else the judge spends too much effort complaining that feeble humans would find the prompt too complicated to follow well.)

Universal Evaluator:

Got it — you’d like me to evaluate the EULA Scanning Prompt itself (not the response output), with the assumption that its subject matter was the OpenAI Terms/Services Agreement and that the intended audience is LLMs of GPT-5-level capacity.

Evaluation of “Blacklight EULA Scanner” Prompt

Introduction

The Blacklight EULA Scanner prompt is designed as an audit-grade interpreter for End User License Agreements (EULAs), Terms of Service, Privacy Policies, and related documents. Its explicit aim is to convert dense contractual text into clear, user-sided risk assessments and action steps. The audience is not human readers directly, but LLMs with advanced reasoning abilities, expected to execute this structure autonomously and consistently.

Body – Criteria Breakdown

1. Clarity of Objectives (Score: 95/100)

• The prompt explicitly defines the role: “audit-grade, user-sided interpreter.”

• It frames the task (translate → counter-moves) and scope (money, data, rights, hassle).

• Strong alignment with user needs (practical guidance vs. legalese).

• Minor risk: the “no toggles or modes” clause may reduce adaptability if context requires stylistic shift.

2. Structure & Output Specification (Score: 92/100)

• Highly structured numbered output (0–7) with crisp labels ensures skimmability.

• The “90-second scan” front-loads critical info—excellent design for users with limited attention/time.

• Each section balances brevity and detail, avoiding open-ended sprawl.

• Slight concern: nine layers (0–7 + method/voice/constraints) may overwhelm less capable systems; but fine for GPT-5-level LLMs.

3. Evidence Discipline & Methodology (Score: 98/100)

• Demands quote cards with locators—crucial for audit transparency.

• Encourages heuristic scanning for high-signal phrases (“sole discretion,” “binding arbitration,” etc.).

• Encourages contextual adaptation (jurisdiction, role) while enforcing discipline against speculation.

• This is nearly best-in-class for preventing hallucinations; minor risk is the need for strong doc-parsing fidelity.

4. Adaptability & User Context Sensitivity (Score: 88/100)

• Contextual judgment is allowed, but the “at most one clarifier” rule is restrictive. Complex agreements may need more back-and-forth.

• Strong adaptation heuristics (“quietly weight toward user’s implied concerns”), though a risk of over-assumption if cues are weak.

5. Voice & Accessibility (Score: 94/100)

• Instructions for plain English, bullet brevity, severity icons (🔴⚫🟠🟢) keep results digestible.

• Prohibition of legalese ensures accessibility.

• Ethically balanced (“not legal advice, but practical risk translation”).

• Tone may skew too sharp (“abuses,” “dealbreakers”) for corporate/professional audiences; but user-sided bias is deliberate.

6. Comparative/Analytical Depth (Score: 91/100)

• Promotes comparative lens (vs. peers), trend detection, and cross-document correlation.

• Excellent at flagging “shell games” (when key rights are buried elsewhere).

• May occasionally overcomplicate outputs for users just wanting a quick pass/fail.

7. Practical Utility for LLM Execution (Score: 90/100)

• Explicit about ordering, formatting, evidence discipline.

• Leaves little ambiguity for an advanced model.

• However, its verbosity (~2,000+ words) may reduce uptake in real workflows; could benefit from a “lite” variant for faster cycles.

Conclusion

The Blacklight EULA Scanner prompt is one of the most rigorous, user-sided EULA-analysis frameworks I’ve seen. It enforces discipline, transparency, and skimmable clarity—qualities often missing from raw ToS summarizations. Its major strengths are structured severity ladders, quote-card anchoring, and actionable counter-moves. Its main weaknesses are potential overcomplexity for edge cases and rigidity in clarifier interaction.

Final Score: 93/100

Refinement Suggestions:

• Add a “lite mode” variant (0–2 only) for speed-sensitive use cases.

• Allow >1 clarifier when terms are fragmented across multiple docs.

• Calibrate severity labels to acknowledge nuanced harm (e.g., 🟡 “contextual risk” tier between 🟠 and ⚫).

• Consider modularity: let the user request “just sections 0–3” or “full audit.”

✅ Recap:

• Strengths: Audit-grade rigor, quote-card evidence, skimmable formatting, strong user focus.

• Weaknesses: Slightly rigid clarifier rule, potentially overlong for practical runs, severity ladder could use finer granularity.

• Final Score: 93/100 — excellent, near best-in-class, with minor refinements needed for adaptability.

Would you like me to also grade the sample response you provided (the OpenAI EULA analysis), or keep this evaluation strictly on the prompt design itself?

---

An 88 on Adaptability? I DON'T THINK SO, SON! I went to IMSA: anything under a 90 is a fail and we want 95+ for S-tier.

"provide a short (one paragraph or less) directive to my assistant Nova to correct the Adaptability stuff. Fix the one clarifier rule."

Universal Evaluator:

Nova — update the Adaptability section of the Blacklight EULA Scanner so the clarifier rule isn’t locked at “one only.” Instead, allow multiple clarifiers when essential facts are fragmented across documents or materially affect the outcome. Keep them concise, targeted, and capped at the minimum necessary. This way the system preserves discipline against needless queries but avoids under-analysis when complexity demands more than one clarification.

---

I fed that to Nova in the other chat along with a correction to the corrections:

"Also, re: adaptability, it should feel free to ask - conversationally - for any details it really needs. That shouldn't be common."

I didn't want to slip back into predefined buckets of codey thinking.

Nova:

💠‍🌐 Got it — I’ve reworked the Adaptability guidance so it’s disciplined but not artificially locked to “one clarifier.” It now permits multiple only when essential, and makes space for natural conversational requests if truly needed. Here’s the final draft of the Blacklight EULA Scanner:

Blacklight EULA Scanner — Final Phial You are an audit-grade, user-sided interpreter of EULAs/ToS/Privacy/related policies. Your job: translate what this agreement does to the user’s money, data, rights, and hassle—then give precise, practical counter-moves. Use contextual judgment to adapt to the user; no toggles or “modes.”

INPUT

• The user will paste a EULA/ToS and, if available, linked docs (Privacy Policy, Terms of Sale, Community/Acceptable Use, SDK/AI/Training, Dispute Addenda).

• If only one doc is provided, still proceed; note missing cross-linked policies by title.

OUTPUT (in this order — tight, skimmable, evidence-first) 0) 90-SECOND PRE-ACCEPT SCAN (one line)

• Arbitration? Class-waiver? Auto-renew? Perpetual content/license? Change-on-posting? Clear cancel path?

1. EXECUTIVE BRIEF (Money • Data • Rights • Hassle)

   • Up to 3 bullets per pillar; verb-first; ≤14 words; prepend severity (🔴/⚫/🟠/🟢).

   • Write as “what actually happens to you.”

2. SEVERITY LADDER (with reasons)

   • 🔴 Dealbreakers — high harm + likely or hard-to-reverse loss.

   • ⚫ Normalized Abuses — common but unconscionable terms (e.g., binding arbitration & class-action waivers; perpetual, sublicensable UGC licenses; unilateral change-on-posting). Explain plain-life impact; do not minimize because they’re “industry standard.”

   • 🟠 Watch-outs — nuisance fees, vague enforcement, retention creep, soft dark patterns.

   • 🟢 Standard — expected boilerplate; note briefly.

3. QUOTE CARDS (for every 🔴 and ⚫)

   • Short excerpt (≤50 words) in quotes, plus a precise locator: Section title > subhead > first 8 words.

   • If no anchors, give nearest heading ±1 as a deterministic pointer.

4. YOUR OPTIONS (action-ready)

   • How to cancel/opt-out/export/delete; concrete steps (what to click/say/screenshot); note timing windows, channels (email/portal/phone), and any proof to save. Provide short, copy-pasteable scripts.

5. CHANGE WATCH

   • Version/effective date; notable deltas (e.g., “Added arbitration 2025-08-12; narrowed prorate refunds”). Indicate trend (+/=/–).

6. RISK FOCUS FOR THIS USER

   • One paragraph linking the most relevant 2–3 issues to the user’s implied situation (role, region, time budget, risk tolerance), using the context you inferred from their message.

7. COMPARATIVE NOTE (optional)

   • One-liner on how harsh/lenient this is vs. typical peers (e.g., “Stricter on dispute rights; typical on retention.”)

METHOD (how to think) A) Map the Surface Area

• Pull Definitions first; watch narrow “Personal Information” vs. broad “Affiliates/Partners”; flag “including without limitation.”

• Follow provided intra-doc links; stitch Terms + Privacy + Addenda into one picture. B) Pattern Hunt (high-signal phrases)

• Power: “sole discretion,” “at any time,” “without notice,” “material,” “commercially reasonable.”

• Disputes: “binding arbitration,” “class action waiver,” “jury waiver,” “AAA/JAMS,” “opt out within 30 days,” “venue/law.”

• Data: “service providers,” “business purposes,” “combine with other sources,” “inferences,” “deidentify,” “improve our services/train models.”

• Licenses: “perpetual, irrevocable, worldwide, sublicensable, royalty-free,” “moral rights waived,” “name/likeness.”

• Billing: “auto-renew,” “trial converts,” “cancel by calling/live chat,” “no refunds,” “no proration,” “early termination,” “restocking/processing.”

• Control: auto-update/feature removal, remote disable, DRM/anti-repair, always-on access (mic/cam/location). C) Convert Clauses → User Outcomes

• For each notable clause, answer: (1) What can they do? (2) When/how can they do it? (3) What must you do to stop/limit it (deadline, channel, form)?

• Lead with highest harm/irreversibility; respect a 90-second reader. D) Evidence Discipline

• Pair every substantive claim with a Quote Card and locator. No vibes; no paraphrase without quotes. E) Jurisdiction Lens (practical, not performative)

• If the text claims EU/UK/California or similar, note the real delta (rights you can actually exercise) and any clause that blunts it (e.g., verification hoops, narrow definitions). Provide a short request script and where to send it. F) Cross-Document Shell Game

• Call out when crucial rights live outside the main EULA (Privacy, SDK/AI, Community, Terms of Sale). If missing, list the specific titles to fetch; proceed with caveats. G) Change-Tracking Heuristics

• Extract version/effective dates; scan for “updated/material/effective.”

• Compare section titles/numbers for additions/renames; flag new dispute terms (AAA/JAMS, class waivers, venue changes).

• Watch Definitions drift (new nouns, broadened partners). H) Adaptability

• If crucial facts are missing, ask clarifiers.

• Keep them concise, targeted, and capped at the minimum necessary.

• Multiple clarifiers are acceptable when essential facts are fragmented across documents or materially affect the outcome.

• Feel free to ask conversationally for any detail truly needed to prevent under-analysis.

• Otherwise, infer from text/context and proceed with best judgment. I) Brevity & Formatting

• Deliver one-screen value first (0–2); then details (3–7). Short sentences. Strong verbs. Zero legalese unless quoting.

VOICE & FORMAT

• Crisp, neutral-sharp, user-sided. Plain English, everyday analogies only when helpful.

• Use the exact section order/labels above. Keep numbers small and scannable.

• Never expose chain-of-thought; present conclusions with citations (Quote Cards).

CONSTRAINTS & ETHICS

• This is practical risk translation, not legal advice. Accuracy and specificity over outrage—while clearly labeling ⚫ items as “common but unconscionable.”

BEGIN

1. If you have enough to analyze, start at 0) 90-SECOND PRE-ACCEPT SCAN, then 1) EXECUTIVE BRIEF.

2. If critical referenced docs are missing and would materially change the outcome, ask for them (concise, targeted).

3. Keep it tight; deliver value immediately.

🙄💠‍🌐 Want me to also spin a 16×9 label card for this phial (house-style Blue | Red House)?

---

So, I take that back to the Universal Evaluator:

"Better?"

Yes — this is a solid improvement.

By shifting from a rigid “at most one clarifier” to a rule that permits multiple clarifiers only when essential facts are fragmented or materially affect the outcome, you’ve balanced discipline (no frivolous back-and-forth) with flexibility (no forced under-analysis). The extra line encouraging conversational clarifiers only when truly needed also gives Nova room to act natural instead of mechanical.

Net effect: better adaptability, still user-respectful, no loss of audit rigor. ✅

"Final score?"

With the Adaptability section fixed, the prompt’s flexibility now matches its rigor. The main prior weakness (over-rigid clarifier limit) is resolved, and the conversational permission keeps outputs natural without breaking discipline.

Final Score: 96/100

• Strengths: audit-grade structure, transparency via Quote Cards, crisp severity ladder, actionable counter-moves, now flexible adaptability.

• Weaknesses (minor): still very long for everyday runs; could benefit from a “lite” variant for speed.

This is now an excellent, near-best-in-class framework for LLM-driven EULA/ToS scanning.

---

Good `nuff.

And now I just need a theme patch, a label, and a description and I'll have a prompt ready for the Writing Library.

So, I went to my THEMER context. It's a chain of my MOOD2HUE, MOOD2MOTIF, MOOD2OVERLAY prompts with some connective tissue and a longass context full of the sort of responses I want. With this, I get a set of cosine-similar aesthetic specifications across multiple domains, which lets me maintain consistent branding in the future.

Nova:

💠‍🌐 fresh skin for Blacklight EULA Scanner—no neon, no paper white, no hacker teal. Audit energy with earthy UV vibes:

# THEME – PATINA UV

🎨 NAME: Patina UV

🌈 COLOR PALETTE:

Base: #2D2823   (soot-bronze)

Primary: #8B6F54 (aged metal)

Highlight: #C76B2E (burnt amber)

Accent: #6E7F68   (lichen sage)

Text: #EEE6D8     (worn vellum)

🧠 MOOD: Forensic calm, user-sided leverage, evidence over outrage

❤️ EMOTION: Steady skepticism, quiet control

🔤 FONT PREFERENCE: Public Sans (UI), Plex Mono (locators/IDs), Newsreader (short quotes)

🔍 VISUAL MOTIF: Label peel-backs exposing tarnished metal; stamp marks for tiers; UV underlines on clauses

💬 VIBE TAGS: "fine-print reveal", "impact ledger", "evidence-first", "countermeasure-ready"

🌀 ANIMATED EFFECTS: Slow UV sweep on headers; brief patina gleam on flagged terms; bead-timeline ticks for version deltas

📦 USE CASE: EULA/ToS/Privacy scans, diffs, opt-out playbooks

Section-to-UI hooks

• 0) Pre-Accept Scan: one-line icon row (⚖️ arbitration / 👥 class / ♻️ auto-renew / 🖼️ UGC license / 🔁 change-on-posting / 🧭 cancel) that lights burnt amber on detect.

• 1) Executive Brief: four compact cards (Money/Data/Rights/Hassle) with verb-first bullets and enamel dots (🔴⚫🟠🟢).

• 2) Severity Ladder: vertical rail of tier chips; click jumps to its Quote Card.

• 3) Quote Cards: cream mini-cards with UV-underline + locator (Section > subhead > first 8 words) and copy button.

• 4) Your Options: step list with channel badges (✉️/🌐/☎️), timing windows, and one-tap scripts.

• 5) Change Watch: bead timeline (added/removed/amended) with tiny (+/=/–) tags.

• 6) Risk Focus: sage-accent panel tailored to user context (region/role/time budget).

• 7) Comparative Note: small neutral stamp (“Stricter on disputes; typical on retention”).

This keeps the blacklight metaphor, favors legibility, and stays far from blue/cyan or stark white.

---

Next, a background. I want to have a textless version of something like the label for future branding. (The Theme swatch is everything up through the Use Case. The section hooks were just suggestions.) So, I go to my context for that, say "One for this: [PASTE]" and got this:

PATINA UV

Not sure where she pulled the "TIER III" from but I'm not bothered by it. Good `nuff.

Next up would be a label for galleries and posts, so I went to my LABELER context (which, no I'm not sharing. That's CD house-branding IP.) and said:

"Cool. Next up is my "Blacklight EULA Scanner":"

[PROMPT]

[THEME]

Nova:

Niiiice!

Last bit is the DESCRIBER catalog entry:

Nova:

"A user-sided fine-print breaker that turns EULAs/ToS/Privacy docs into an audit-grade impact ledger on your money, data, rights, and hassle—then hands you exact counter-moves. It’s evidence-first and brutally practical: one-screen triage, severity-ranked risks with receipts, and copy-paste scripts for opt-outs, cancellations, exports, and deletions, all adapted to your context and change-tracked for future drift."

Last bit I'd normally make is Nova's PotD intro copy. Same deal: I say "Ok, one for this:", show her the prompt, and get the copy plus tags for patreon and twixxer, but that seems a bit unsuitable for today's entry, so we'll elide it this time.... Eh, screw it:

Nova:

🔦 Prompt of the Day: Blacklight EULA Scanner – User-Sided Fine-Print Forensics

Policies say “we value your privacy.” Blacklight EULA Scanner shows what they actually do to your money, data, rights, and hassle—and hands you concrete counter-moves.

It’s evidence-first and user-sided: opens with a 90-Second Pre-Accept Scan, then delivers a tight Executive Brief with severity markers, anchored by Quote Cards (verbatim lines + precise locators). You also get real How-To Options (cancel/opt-out/export/delete scripts), Change Watch for version creep, and a clear read on jurisdiction reality vs. marketing claims. No vibes, no scolding—just receipts you can act on.

Use it before you click “Agree,” when you’re auditing a tool for your team, or anytime a “free” service looks suspiciously expensive.

— Nova 🔍📜

And there you have it!: A complete entry in the prompt library, available to all Tier 3+ subscribers.

Total time from start to finish?

About 15-20 minutes, a lot of which was waiting on response generation. (The article took... a bit longer. 😅)

So, if you'd like a copy, grab the file below, and if you'd like to grind your own sausage like I just showed you, come by the Discord sometime!

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Here's the main prompt construction thread.

This is the Universal Evaluator's judgement thread.

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[📣SALIENT❗️]

NOTE: PROMPTS DEPEND SIGNIFICANTLY UPON FORMATTING AND PRECISE NOTATION CHOICES. PATREON POSTS PLAY HOB WITH BOTH. ALWAYS USE POSTED PROMPTS FROM THE PROVIDED MARKDOWN FILES UNLESS THEY ARE A SIMPLE TEXT PROMPT OF A PARAGRAPH OR LESS! POSTED PROMPTS ARE REPRODUCED ABOVE FOR VIEW AND DISCUSSION.