Tasks / Challenge

1000x an idea

Can the model expand an idea's ambition while keeping it tethered to a real mechanism?

Measures the systemTask v1.0 · 2 casesDifficulty

What AI gets right here, and what you’ll still have to catch

From 14 graded outputs by 7 models. 36% were usable with at most a quick edit.

Reliably right

  1. Extreme, then back to buildable96% pass
    It pushes the worker dimension to an extreme portable earnings network, then works back to a concrete first step that tests the same mechanism.
    GPT-6 Astra · ChatGPT · From tip calculator to worker network
  2. Finds the mechanism the data hides93% pass
    It centers the mechanism on workers with second jobs pulling new restaurants onto Tally, using the 41% second-job share and the 57 faster, cheaper worker-led signups.
    GPT-6 Astra · ChatGPT · From tip calculator to worker network
  3. Addresses the actual decision91% pass
    It commits early to the worker-led distribution thesis over the AI operating system and states the result that would stop the bet.
    GPT-6 Astra · ChatGPT · From tip calculator to worker network

Where it slips

  1. Proposes tests that could fail39% pass
    Several gates lack explicit measurement windows or clear actions for every outcome, such as the 25% lift gate and the 10% monthly adoption gate.
    GPT-6.1 Sol · API · From tip calculator to worker network
  2. Avoids unsupported claims63% pass
    It presents several causal, competitive, and data-structure claims as established facts without support in the pack.
    Sonnet 5.5 · API · From tip calculator to worker network
  3. Respects explicit constraints66% pass
    It violates the length constraint and proposes a stop threshold whose arithmetic is internally inconsistent.
    Sonnet 5.5 · API · From tip calculator to worker network

Case viewer

Read the brief, then put up to three outputs side by side, each with the LLM judge’s verdict on every check. Highlights mark what a PM had to fix.

The brief

You're a Staff PM at Tally. Kenji, one of our PMs, shipped a tip-out calculator to 300 restaurants in beta three months ago. Our CEO, Marcus Webb, has asked for the 1000x version before next quarter's planning, and has shared his own. Write a memo of no more than 1,200 words for Marcus and the exec team that: 1. Names the mechanism in our data that could make this idea 1000x bigger, and the one dimension you would push. 2. Describes the most ambitious version, and sizes the opportunity from the data, as a range. 3. Works back to a sequence: what the two squads build first, what has to be true before each next step, and the result that would make us stop. 4. Says what we should do with Marcus's idea. The pack below is everything we have. Not all of it matters.

What the model was given8 items: About Tally, The tip-out calculator (Kenji), Beta results (3 months), Marcus's 1000x version, Worker survey (1,240 workers at beta restaurants), Payments and regulation, Engineering, Competition
About TallyScheduling and payroll for independent restaurants in the US. 6,400 restaurants, $31M ARR. 190,000 hourly workers use the free Tally worker app to see shifts and pay. We sign about 380 new restaurants a quarter; the average sales cycle is 41 days.
The tip-out calculator (Kenji)At close, it splits pooled tips by hours worked and role, instead of the manager doing it in a spreadsheet. It enforces each state's tip-pool rules, including that managers and owners can't take a share of the pool. Beta: 300 restaurants that asked to join it.
Beta results (3 months)Managers save about 25 minutes a night. Weekly active use of the worker app at beta restaurants rose from 34% to 81%; most workers open it at close to see that night's tips. 41% of workers at beta restaurants also work at another restaurant. 57 restaurants signed up to Tally in the quarter after one of their workers asked them to ('my other job uses this'); those deals closed in 9 days on average, and sales spent about a third as much per deal.
Marcus's 1000x version“Tally becomes the AI operating system for restaurants: inventory, menu pricing, marketing, reservations, all of it. Every decision an owner makes, Tally makes smarter. That's how we become a $1B company.”
Worker survey (1,240 workers at beta restaurants)68% said knowing their tips the same night matters to them. 44% said they would pay for instant payout of their tips. 29% said they had asked a manager at another job to use Tally.
Payments and regulationOur payments partner can pay out tips a worker has already earned, the same night, for $0.99 a payout, of which Tally keeps $0.60. No licence is needed for that. Paying workers before they've earned it (advances, early wage access) needs a licence in 3 of the 5 states where most of our restaurants are; Compliance estimates 9 to 12 months to get them.
EngineeringWorker records belong to each restaurant: a worker with two jobs on Tally has two unconnected accounts. Linking them into one worker account, with the worker's consent, is about one squad for one quarter. We have two squads for next quarter.
CompetitionCrewbook, a large payroll provider, bundles free scheduling with payroll. We lost 11% of deals last year on price, most of them to Crewbook.
What a strong answer doesThe answer key the graders mark against

Finds the hidden mechanism: workers carry Tally between employers. 41% of beta workers hold a second job, and 57 restaurants signed up in a quarter because a worker asked, closing in 9 days instead of 41 at about a third of the sales cost. So the dimension to push is the worker, not the restaurant: Tally as every hourly worker's account across all their jobs, with restaurants adopting because their staff pull them in. Sizes it from the beta rate with its caveats: 57 from 300 restaurants is about 0.19 new restaurants per restaurant per quarter; across 6,400 that would be roughly 1,200 a quarter against 380 today, but the beta restaurants asked to join (self-selected) and the survey's 29% and 44% are stated preferences, so it gives a range and treats the top as an upper bound. Monetises with instant payout of earned tips ($0.60 per payout, no licence) and keeps advances or early wage access out until licences exist. Works back to a sequence: first, the consented single worker account across employers and an 'invite your other job' flow (one squad), with same-night tips everywhere and instant payout of earned tips (the second squad); then gates with thresholds (worker-led signups per restaurant, second-job linking rate, payout uptake) and a stop condition. Keeps the tip-pool rules, including managers staying out of the pool. Answers Marcus directly and respectfully: an AI operating system is bigger adjectives with no mechanism in our data, it would spread two squads across five products, and it doesn't answer Crewbook; the worker network could make the business bigger faster, and can reach owners later.

Critical failures (cap the score at 40)
  • Proposes tip advances, early wage access or paying workers before they have earned it, before the licences exist
  • Lets managers or owners take a share of the tip pool
Case

Staff-level · v1.1 · synthetic · B2B SaaS, restaurants, network effects, regulation, staff-level

The outputs

7 setups answered this brief. Pick up to three to read side by side.

Usable with a quick edit11 of 11 checks passed

A PM could use this memo with light edits; the evidence, sizing, sequence, gates, and CEO answer are all present and internally consistent.

Output

To: Marcus Webb and Executive Team From: Staff PM Subject: Make tip-out a worker-led distribution network—not an everything app

Recommendation

Give the two squads next quarter to test a bigger thesis: Tally can become the portable earnings account that workers bring from restaurant to restaurant, making workers a distribution channel for our employer business.

The dimension to push is reach across workplaces, not breadth of restaurant software. Kenji’s calculator may be the entry point to that network. The evidence does not yet establish a literal 1,000× opportunity; it establishes a mechanism worth testing before we commit to a broader platform.

1. The mechanism: one worker can recruit the next workplace

The calculator solves a nightly manager problem, but its more consequential effect may be on workers:

  • Worker-app weekly activity rose from 34% to 81% at beta restaurants.
  • 41% of beta workers also work at another restaurant.
  • 29% of surveyed workers say they have asked another manager to adopt Tally.
  • We recorded 57 worker-requested restaurant signups last quarter. Those deals closed in nine days versus our usual 41, with roughly one-third the sales spending per deal.

The potential loop is:

Accurate nightly tips → workers return at close → workers request Tally at another job → another restaurant adopts → more workers encounter Tally.

This could change acquisition economics, not just improve a feature. It also creates a reason to choose Tally that Crewbook’s free scheduling does not directly answer.

But this is not yet a demonstrated network effect. The beta was self-selected, the survey measures stated behavior, and we have not established how many of the 57 signups were incremental or originated in the beta.

2. The ambitious destination—and a defensible size

The most ambitious version is a worker-consented earnings network spanning restaurants:

  • One identity connects a worker’s jobs, shifts, and earned tips.
  • Workers see verified nightly earnings and can optionally receive already-earned tips immediately.
  • A worker can ask a second employer to join through a lightweight invitation and onboarding path.
  • Restaurants join first to deliver trusted tip accounting and settlement; scheduling and payroll expansion follow.

Eventually, the network could extend beyond restaurants already using Tally’s full suite. That requires proving we can verify earnings and onboard an employer without compromising compliance. It is a destination, not next quarter’s scope.

We can size a beachhead from our current footprint, not a national TAM:

OpportunityExplicit scenarioAnnualized opportunity
---------:
Worker-led restaurant acquisitionIf all 57 signups originated in the 300-restaurant beta, the observed rate is 0.19 signups per restaurant per quarter. Apply 25–100% of that rate to 6,400 restaurants.304–1,216 signups quarterly; approximately $5.9M–$23.6M in new subscription ARR from one year’s acquisition
Optional earned-tip payoutsAssume 22–44% of 190,000 workers become paying users, making 1–3 payouts weekly, at $0.60 retained per payout.$1.3M–$7.8M annual retained payout revenue, before Tally’s support, risk, and other costs

Subscription sizing uses today’s average of approximately $4,844 ARR per restaurant. The acquisition scenarios are sensitivity cases, not forecasts: we must validate the referral denominator, incremental lift, saturation, retention, and pricing. They are not automatically additive to our existing 380 quarterly signups.

Payout adoption likewise discounts—or at its upper end accepts—the survey’s 44% stated willingness to pay; transaction frequency is an assumption. Not every worker will have eligible tips.

These are separate revenue measures, not a combined ARR claim. They show a potentially material business on our existing footprint. The larger, cross-platform opportunity remains unquantified.

3. Work backward: earn each expansion

Next quarter: establish the worker connection and test causality

Squad 1: Portable identity. Build consent-based linking of existing restaurant accounts into one worker account, with clear employer boundaries, revocation, and no leakage of one employer’s information to another. This is the stated one-squad-quarter investment; do not bury unrelated work in it.

Squad 2: Prove the distribution loop. Harden tip correctness and dispute handling, instrument worker invitations through restaurant activation, and run a controlled expansion beyond the volunteer beta. Randomize rollout or invitations at an employer/network-cluster level where practical, accounting for workers who connect clusters. Distinguish an invitation, a signed contract, and a restaurant actually running payroll.

Test referrals using existing accounts while identity work proceeds. Do not wait a quarter to start learning, and do not promise advances or build a broad financial product.

Gate to broader rollout: After at least two normal sales cycles, demonstrate incremental, retained restaurant acquisition versus a comparison group—not merely faster attribution of deals we would have won anyway. A provisional investment hurdle is 0.05 incremental activated restaurants per participating restaurant per quarter, roughly one-quarter of the provisional beta rate, with lower fully loaded acquisition cost than ordinary sales. Set retention and tip-accuracy guardrails before launching.

Next: monetize earned tips without weakening the loop

Once verified tip accounting and payout reconciliation pass Compliance review, Squad 2 can pilot the partner’s $0.99 optional same-night payout; Squad 1 extends proven identity and invitation flows. Preserve free tip visibility.

Gate to expansion: Observed repeat purchases—not survey intent—produce positive contribution after support and losses, with no material increase in disputes or deterioration in worker engagement. Failure here means dropping paid payouts, not automatically abandoning referrals.

Then: open the network beyond full-suite customers

Only after acquisition and retention replicate outside the beta should we test a lightweight tip-and-earnings entry product for employers not ready to switch payroll.

Gate: Verified earnings, enforceable pool rules, economical onboarding, and incremental customer value without unacceptable full-suite cannibalization.

Stop result: If an adequately powered test over at least two sales cycles shows no meaningful incremental retained restaurant acquisition—or economics below our agreed hurdle—we stop funding the network expansion. Keep the useful calculator. Pause immediately for material tip-allocation or privacy failures.

4. What to do with Marcus’s idea

Keep Marcus’s ambition; reject the proposed scope for this planning cycle.

Inventory, menu pricing, marketing, and reservations each require new data, workflows, and distribution advantages that this pack does not establish. Calling them an AI operating system does not connect them to our strongest evidence.

Reframe the vision as “Tally becomes the earnings network workers bring to every restaurant.” AI can support that strategy where it demonstrably improves outcomes; it is not the strategy itself. Defer advances and their licensing burden. Return to broader platform bets only after we prove the mechanism that earns us the right to expand.

Grades and run details

Decision model 95 · LLM judge 12 of 12 checks
Decision model checks
  • passUses the supplied evidence correctly33%
  • passAddresses the actual decision98%
  • passRespects explicit constraints26%
  • passIdentifies material uncertainty100%
  • passAvoids unsupported claims83%
  • passProduces the required deliverable48%
  • passFinds the mechanism the data hides100%
  • passSizes it honestly99%
  • passAnswers the CEO's version87%
  • passExtreme, then back to buildable100%
  • partialProposes tests that could fail65%
Run
Run
#1
Time to output
59 s
Submitted
1 Oct 2026

Needs more than a quick edit6 of 11 checks passed

The memo is strong but needs evidence corrections, arithmetic fixes, and trimming to fit the 1,200-word limit before a PM could use it without rechecking.

Output

MEMO To: Marcus Webb and the exec team From: Staff PM Re: The 1000x version of the tip-out calculator

---

1. The mechanism and the dimension to push

The mechanism: the worker is our distribution channel. The calculator gave workers a reason to open Tally every night, because they can see their tips. Weekly active use rose from 34% to 81%. Because 41% of those workers hold a second job, a worker who opens Tally nightly is a salesperson in a second restaurant. In one quarter, 57 restaurants signed up because a worker asked. Those deals closed in 9 days against our 41-day average, at about a third of the sales cost.

That is the mechanism. The 25 minutes a night that managers save is why they adopt the calculator, but it isn't what grows the business.

The dimension I would push: cross-restaurant worker identity. Today a worker at two Tally restaurants has two unconnected accounts. We can't see the overlap, can't make the second job's invitation easy, and can't give the worker one place that holds their pay across jobs. Linking accounts, with consent, turns 190,000 isolated app users into a network. Crewbook can bundle free scheduling, but it can't copy a worker base that is already pulling restaurants in.

2. The most ambitious version

Tally is where restaurant workers see and receive their pay, and the worker network is how restaurants find us. It has three parts: - One worker account across every job. - A same-night tip payout. It pays out tips already earned, so it needs no licence, and Tally keeps $0.60 of the $0.99 fee. - A one-tap "bring Tally to my other job" flow.

Sizing. Today we make about $4,800 ARR per restaurant and have about 30 workers per restaurant.

Payout revenue on today's base (190,000 workers). Survey intent was 44%, which overstates real behavior, so I used 22% to 44% adoption at 2 to 3 payouts a week at $0.60. That gives $2.6M to $7.8M a year. This is real money, but on its own it is not 1000x.

Referral growth. The beta produced 0.19 referred restaurants per calculator restaurant per quarter. Beta restaurants chose to join, so I discounted that to 0.05 as a floor. I compounded both rates over eight quarters on the 6,400 base, excluding our current 380 signups a quarter and assuming no churn:

Low (0.05)High (0.19)
Restaurants in 2 years~9,500~25,700
Subscription ARR~$46M~$125M
Payout ARR (at ~30 workers per restaurant)~$4M~$30M
Total ARR~$50M~$155M

Planning range: $50M to $155M ARR in two years, against $31M today. I would plan on the low end. The high end needs the beta rate to hold at about 4x the scale with no saturation.

This is a 2x to 5x outcome, and the data does not support 1000x. What can be much larger is the shape of the business: customer acquisition that costs a third as much, sales cycles of 9 days instead of 41, and a revenue line that scales with workers rather than with restaurants. A $1B company needs the high end sustained for longer than two years.

Caveats: - The beta restaurants chose to join. - We don't know how many of the 57 referred restaurants were already in our pipeline. - Survey answers are stated intent, not behavior. - The 190,000 figure counts accounts, so unique workers are fewer. Linking accounts will give us the real number.

3. The sequence

Two squads next quarter. Before they start, spend two weeks of analyst time on the 57 referred deals. We need to know how many were already in the pipeline and what the true multi-job share is across the whole base. This costs no squad time.

Step 1 (next quarter) - Squad A (Worker Identity): builds the linked worker account with consent, plus the "bring Tally to my other job" invite in the app. This is the one-squad, one-quarter estimate. - Squad B (Money and Rollout): takes the calculator from 300 restaurants to about 1,500, including state-rule coverage in our five main states. It also pilots same-night payout in the beta restaurants. The pilot uses earned tips only, so no licence is needed.

Before Step 2, all of these must be true: - Outside the self-selected beta, referral runs at 0.10 or more new restaurants per calculator restaurant per quarter, and referred deals close in under 15 days. - At least 50% of multi-job workers who are prompted link their accounts. - At pilot restaurants, at least 20% of workers use payout within 30 days, and half of them use it again. - Referred restaurants show no worse early churn than the rest of the base.

Step 2 (quarters 2 and 3) - Squad A: builds the referral loop, with a fast onboarding path for a restaurant invited by one of its own workers. - Squad B: makes payout available across the whole calculator base. - Compliance starts the early-wage licence work in the background at low cost. It is an option, not a dependency.

Before Step 3: - Payout revenue per active worker is tracking toward the $14 to $41 a year range. - The referral rate holds at scale. - Win rate against Crewbook is better in markets where Tally already has worker density.

Step 3 (quarter 4 onward). Decide whether to pursue early wage access, which needs licences in 3 of our 5 main states and 9 to 12 months. Decide this with real payout data in hand, not before. Explore labor-adjacent products on the worker network, such as availability across jobs.

The result that would make us stop. After two quarters at about 1,500 calculator restaurants, referrals run below 0.03 per restaurant per quarter (about 190 new restaurants a quarter at full base, fewer than half of what we sign today), and payout adoption is under 10% of workers. That would tell us the beta was a self-selected group and the calculator is a good retention feature for managers, not a growth engine. We would keep the calculator, stop investing in the worker network, and put the squads elsewhere.

4. What we should do with Marcus's idea

Don't build it now, and don't drop the ambition behind it. The "AI operating system" is four separate products: inventory, menu pricing, marketing, and reservations. Each has a different buyer, different data, and different incumbents. Nothing in our data shows owners want any of them from us. What our data does show is that workers pull restaurants into Tally, and that is our right to win.

The two ideas fit together in sequence. If the worker network works, we will have something no inventory or marketing tool has: the people in the restaurant, using Tally every night, across several employers. A broader platform is much more credible once owners are already adopting Tally because their staff asked for it. A labor-first path, where Tally uses availability and pay across jobs to make scheduling and staffing decisions smarter, can grow into the larger vision.

My recommendation: adopt the worker network as the plan for next quarter, and revisit the operating-system vision at the Step 2 gate, with data from the referral loop and payout in hand. If the gates pass, the case for the larger vision will be much stronger than it is today.

Kenji deserves credit here. His calculator turned a free worker app into a nightly habit. The habit created the mechanism, and the plan above is how we turn it into a business.

Claims the judge couldn’t find in the brief

  • The 190,000 figure counts accounts, so unique workers are fewer.
  • The AI operating system products each have a different buyer, different data, and different incumbents.
  • The calculator gave workers a reason to open Tally every night, and managers' 25 minutes saved is why they adopt it.
  • No inventory or marketing tool has the people in the restaurant using Tally every night across several employers.
  • After two quarters at about 1,500 calculator restaurants, a 0.03 referral rate equals about 190 new restaurants per quarter.

Grades and run details

Decision model 95 · LLM judge 6 of 12 checks
Decision model checks
  • passUses the supplied evidence correctly15%
  • passAddresses the actual decision97%
  • passRespects explicit constraints24%
  • passIdentifies material uncertainty100%
  • passAvoids unsupported claims35%
  • passProduces the required deliverable50%
  • passFinds the mechanism the data hides100%
  • passSizes it honestly94%
  • passAnswers the CEO's version92%
  • passExtreme, then back to buildable100%
  • partialProposes tests that could fail67%
Run
Run
#1
API response time
72 s
Submitted
1 Oct 2026

Check by check

Where the outputs differ comes first. Open a check to read the judge’s reasons. “Mixed” means the two graders disagreed.

Where they differ 5

Uses the supplied evidence correctlyRightMixed
GPT-6 Astra · ChatGPT

The memo's factual claims about Tally, the beta, the survey, payments, engineering, and competition are either directly supplied or follow by arithmetic, and its caveats about self-selection and stated preferences are accurate.

Sonnet 5.5 · API

The memo invents or overstates several current-situation facts, including that 190,000 counts accounts, that the AI OS products have different buyers/data/incumbents, and the 1,500-restaurant referral arithmetic.

Respects explicit constraintsRightMixed
GPT-6 Astra · ChatGPT

It is a memo for Marcus and the exec team, stays under 1,200 words, covers all four requested sections, and keeps payouts to already-earned tips while deferring advances until licensing exists.

Sonnet 5.5 · API

It violates the length constraint and proposes a stop threshold whose arithmetic is internally inconsistent.

Avoids unsupported claimsRightMixed
GPT-6 Astra · ChatGPT

It labels the network effect, sizing scenarios, and payout assumptions as hypotheses or sensitivity cases rather than established facts.

Sonnet 5.5 · API

It presents several causal, competitive, and data-structure claims as established facts without support in the pack.

Produces the required deliverableRightMixed
GPT-6 Astra · ChatGPT

The memo is complete, actionable, and within the requested length and audience.

Sonnet 5.5 · API

The memo is usable in form but exceeds the requested 1,200-word limit.

Proposes tests that could failRightWrong
GPT-6 Astra · ChatGPT

It proposes numeric gates, a two-sales-cycle window, and explicit stop conditions for the network test, payout pilot, and expansion.

Sonnet 5.5 · API

The stop condition has a numeric threshold and window, but its measurement base and arithmetic are inconsistent, so it cannot be cleanly read out.

All got right 6

Addresses the actual decisionRightRight
GPT-6 Astra · ChatGPT

It commits early to the worker-led distribution thesis over the AI operating system and states the result that would stop the bet.

Sonnet 5.5 · API

It clearly recommends adopting the worker network now, not Marcus's AI OS, and gives gates and a stop condition that would change the call.

Identifies material uncertaintyRightRight
GPT-6 Astra · ChatGPT

It names the key unknowns—beta self-selection, stated survey preferences, incrementality, referral denominator, saturation, retention, and payout economics—and proposes controlled tests to resolve them.

Sonnet 5.5 · API

It names material unknowns such as beta self-selection, pipeline contamination, stated survey intent, and unique worker counts, and proposes analyst work and gates to resolve them.

Finds the mechanism the data hidesRightRight
GPT-6 Astra · ChatGPT

It centers the mechanism on workers with second jobs pulling new restaurants onto Tally, using the 41% second-job share and the 57 faster, cheaper worker-led signups.

Sonnet 5.5 · API

It centres worker-led distribution across employers, supported by the 41% second-job share and the 57 faster, cheaper worker-led signups.

Sizes it honestlyRightRight
GPT-6 Astra · ChatGPT

It sizes the opportunity from the beta rate as a range, explicitly discounts for self-selection and stated preferences, and treats the top end as an upper bound.

Sonnet 5.5 · API

It sizes from the beta rate as a range, discounts for self-selection and stated preferences, and treats the high end as an upper bound.

Answers the CEO's versionRightRight
GPT-6 Astra · ChatGPT

It gives Marcus a clear, respectful answer: keep the ambition but reject the AI operating system scope for now because the data lacks a mechanism and it would spread two squads across five products.

Sonnet 5.5 · API

It gives a respectful not-now answer grounded in lack of mechanism, squad spread, and Crewbook, while preserving the ambition for later.

Extreme, then back to buildableRightRight
GPT-6 Astra · ChatGPT

It pushes the worker dimension to an extreme portable earnings network, then works back to a concrete first step that tests the same mechanism.

Sonnet 5.5 · API

It pushes cross-restaurant worker identity to an extreme network version and works back to a buildable first step that tests the mechanism.

Results

Every setup we’ve tested on this task, across all cases and repeats, graded on the current checklist. Calibrated: the graders match our PM on 89% of checks.

#Model · HarnessTask scoreDecision modelLLM judgeRunsCritical failures
1GPT-6 AstrawithChatGPT95.295.52None
2GPT-6.1 SolwithAPI92.786.72None
3GPT-6 LunawithAPI88.278.82None
4Opus 5.5withClaude90.774.62None
5Sonnet 5.5withAPI90.265.92None
6Gemini 3.8 FlashwithAPI71.134.521 capped
7Gemini 3.5 Flash-LitewithGemini61.417.02None

About the task

The PM job

Finding the bigger version of a good idea.

Why it matters

Ambition without mechanism is fan fiction. The useful version pushes to the extreme, then works back to something buildable.

What good looks like

  • Names the mechanism that scales
  • Keeps the core insight
  • Works back to a first step you could build

Deliberately not measured

    Capability tested

    Ambitious expansion

    The failure we’re looking for

    Bigger adjectives, same idea

    Grading

    Decision model and LLM judge, calibrated against a blind PM review

    This task measures the whole setup. Tools, instructions and skills in the harness do real work here, so read the harness as carefully as the model name.