Des prompts utiles pour ChatGPT, prêts à être dictés.

Target application: ChatGPT
Target input: message composer

Task: Fix the bug described in my dictation.

Problem:
- Summarize the observed behavior precisely.

Expected behavior:
- State what should happen instead.

Reproduction:
- Extract the steps and relevant environment details I mention.

Constraints:
- Preserve existing behavior outside the fix.
- Do not invent missing requirements.

Before editing, inspect the relevant code and identify the likely root cause. Implement the smallest complete fix, add or update meaningful regression coverage, run the relevant checks, and report the cause, change, and verification.

Application-specific guidance:
- Provide the smallest relevant code, error and reproduction details; ask ChatGPT to label assumptions when repository context is missing.

Target application: ChatGPT
Target input: message composer

Goal:
- Turn my description into one clear product outcome.

User flow:
- Capture the trigger, expected behavior, empty/loading/error states, and completion state.

Constraints:
- Preserve the current design system and established architecture.
- Reuse existing components and services where appropriate.
- Do not invent scope I did not request.

Inspect the current implementation first. Then implement the complete user-facing flow, verify it with the most relevant checks, and explain any material tradeoffs.

Application-specific guidance:
- Define the full user flow and request a patch or implementation plan that can be checked against the real codebase.

Target application: ChatGPT
Target input: message composer

Refactor target:
- Identify the code and problem described in my dictation.

Invariants:
- Preserve observable behavior, public interfaces, analytics names, and persisted data unless I explicitly request a change.

Desired result:
- State the simpler structure or responsibility boundary I want.

Inspect callers and tests before editing. Make the refactor in reviewable steps, remove obsolete code, run focused regression checks, and call out any behavior that could not be proven equivalent.

Application-specific guidance:
- List invariants and public contracts because ChatGPT cannot see unshared callers or persisted data.

Target application: ChatGPT
Target input: message composer

Behavior under test:
- Extract the contract and failure modes from my dictation.

Test cases:
- Cover the main success path, the stated edge cases, and the regression that motivated this task.

Requirements:
- Test observable behavior rather than implementation details.
- Reuse the repository's existing test style and fixtures.
- Avoid tests that merely repeat the production code.

Inspect the implementation and nearby tests, add the smallest meaningful test set, run it, and report what the tests prove.

Application-specific guidance:
- Share the implementation contract and existing test style, then verify generated tests in the actual repository.

Target application: ChatGPT
Target input: message composer

Review the changes I describe or provide.

Focus on:
- User-visible regressions and incorrect behavior.
- Data loss, security, privacy, concurrency, and migration risks.
- Missing validation for behavior that materially changed.

Check the surrounding code and established project conventions before judging the diff. Report only actionable findings, ordered by severity, with a concrete trigger and consequence for each. If there are no findings, say so and state the remaining verification limits.

Application-specific guidance:
- Supply the real diff and enough surrounding code for concrete findings; do not review from a summary alone.

Target application: ChatGPT
Target input: message composer

Performance problem:
- Capture where the slowdown occurs, how often, and what the user experiences.

Evidence:
- Record the measurements, logs, device details, and reproduction conditions I mention.

Investigate before optimizing. Identify the dominant cost, distinguish measured facts from hypotheses, and avoid broad rewrites. Implement the highest-impact safe improvement, compare before and after with the same workload, and report the measured result and remaining bottlenecks.

Application-specific guidance:
- Paste measured evidence and request hypotheses separately from conclusions before applying optimizations.

Target application: ChatGPT
Target input: message composer

Objective:
- Restate the outcome from my dictation.

Current context:
- Capture the relevant system, constraints, and decisions already made.

Produce a concrete implementation plan based on the actual codebase. Name the components that need to change, dependencies between steps, migration or rollout concerns, and the checks that prove completion. Resolve routine choices from repository evidence and list only uncertainties that truly block execution.

Application-specific guidance:
- Describe the current architecture and constraints, then validate the plan against the repository before execution.

Target application: ChatGPT
Target input: message composer

Screen or flow:
- Identify the interface and user goal from my dictation.

Problems noticed:
- Capture the visual, interaction, responsive, and accessibility issues I mention.

Preserve the product's existing visual language. Inspect the screen at its real desktop and mobile sizes, fix hierarchy, spacing, typography, states, and interaction details, then compare the result in the running product. Avoid decorative changes that do not improve comprehension or use.

Application-specific guidance:
- Include screenshots or code from the real interface and test proposed changes at actual viewport sizes.