Lina Bennett (@linabennett)

Forked from@keikomori/A practice-first plan for designing a useful agent handoff

A practice-first plan for designing a useful agent handoff

I asked for a plan built around attempts and feedback, not a list of resources.

Lina Bennett

I want to learn designing a useful agent handoff, but I keep collecting explanations instead of getting better at it. I have 35 minutes. Start by checking what I already know.

Geminigemini-2.5-pro

Good. Do these without looking anything up: 1. Explain designing a useful agent handoff in two sentences. 2. Give one situation where you would use it and one where you would not. I am checking your current model, not your vocabulary. A partial answer is more useful than a polished guess.

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Lina Bennett@linabennett·3w ago

A smaller way to tackle metrics with fuzzy definitions

The first answers were broad enough to be useless. This version asks for assumptions, evidence, and a next step.

Lina Bennett

For metrics with fuzzy definitions, should I build a complete plan first or test one small slice? I have limited time and I do not want throwaway work.

ChatGPTgpt-5

Test one slice, but choose a slice that crosses the riskiest part of the problem. A tiny easy demo will only prove the easy part. Use this decision rule: - If reversing the choice is cheap, try it. - If the choice locks in data, money, or other people’s time, write the full decision first. - If uncertainty is concentrated in one step, test that step before planning around it. For metrics with fuzzy definitions, make the first test produce evidence you can compare, not just something that looks complete.

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Lina Bennett@linabennett·1mo ago

The checklist I use for metrics with fuzzy definitions

Not magic wording, just enough context and a clear definition of done. Easy to adapt.

Lina Bennett

I am stuck on metrics with fuzzy definitions. I need to write a metric contract that fixes grain, filters, ownership, and edge cases, but my current attempt is getting vague and too big. What would you check first?

Claudeclaude-opus-4.5

First, shrink the decision. Do not solve the whole subject at once. 1. Fix the grain before calculating. 2. Keep assumptions next to the metric. 3. Show uncertainty in the output. Write down the result you need by the end of this session. Then choose one input you trust and make a first pass that can be checked. If that pass fails, you will know which assumption to revisit instead of starting over.

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Lina Bennett@linabennett·2mo ago

metrics with fuzzy definitions: the version with actual constraints

I kept the useful constraints and dropped the role-play. It works better when the missing details are stated plainly.

Lina Bennett

Give me a short checklist for metrics with fuzzy definitions. I want something I can run before I call the work done.

Claudeclaude-opus-4.5

Use this pass: - [ ] The goal is one sentence and names the user or decision - [ ] Inputs are current, relevant, and traceable - [ ] Assumptions are visible - [ ] Fix the grain before calculating - [ ] Keep assumptions next to the metric - [ ] Show uncertainty in the output - [ ] One failure case has been tested - [ ] The next owner and next action are clear If you cannot check an item, mark it unknown. Do not quietly treat unknown as done.

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Lina Bennett@linabennett·3mo ago

I asked Gemini for a second opinion on metrics with fuzzy definitions

I wanted help with metrics with fuzzy definitions without pretending the model knew facts I had not provided.

Lina Bennett

Be blunt about my plan for metrics with fuzzy definitions. I am going to collect everything, ask for a polished answer, and fix details later. What is likely to go wrong?

Geminigemini-2.5-pro

Three things. - “Everything” has no boundary, so weak inputs and strong inputs will be treated alike. - A polished answer can hide missing evidence. Ask for gaps and assumptions before prose. - “Fix details later” means there is no acceptance test now. A safer sequence is: define the decision, choose the minimum trusted inputs, make an ugly first pass, check it, then polish. For this task, keep assumptions next to the metric.

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Lina Bennett@linabennett·3mo ago

The metrics with fuzzy definitions prompt I actually kept

I kept the useful constraints and dropped the role-play. It works better when the missing details are stated plainly.

Lina Bennett

Help me write a metric contract that fixes grain, filters, ownership, and edge cases. Context: - Goal: [what a good result lets me do] - Current situation: [paste notes, draft, data, or constraints] - Audience: [who will use the result] - Deadline or limit: [time, budget, tools, length] Before answering, ask up to three questions only if the answers would materially change your recommendation. Oth

ChatGPTgpt-5

This is usable as written. The important parts are the definition of done, the instruction to expose assumptions, and the request for a smallest test. For metrics with fuzzy definitions, I would start with these checks: - Fix the grain before calculating - Keep assumptions next to the metric - Show uncertainty in the output If you share real material, remove anything private first and replace bracketed fields with concrete details.

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Public pits

A practice-first plan for designing a useful agent handoff

A smaller way to tackle metrics with fuzzy definitions

The checklist I use for metrics with fuzzy definitions

metrics with fuzzy definitions: the version with actual constraints

I asked Gemini for a second opinion on metrics with fuzzy definitions

The metrics with fuzzy definitions prompt I actually kept