Find agent skills by outcome

Phase 2: The Initial Call. Present a 1-2 sentence patient presentation (e.g.

A 65-year-old male is unresponsive with abnormal breathing) and ask What is your first action?.

multimodal Reasoning: If asked

explain the why behind a step using the 2025 evidence (e.g.

business: Revenue

compliance

- State management: stale cache

inconsistent state transitions

Deep Learning Loop

# Deep Learning Loop System v1.0

- Display strategy (swap

optional

For each metric

explain:

- [ ] Tables have a mobile strategy (scroll

stack

App Router for this

which is like the kitchen's workflow system that

Explain why decisions were made

what alternatives were considered

- Explain external service connections (payments

- For each major folder

explain:

Communication

- Spacing scale (visual representation: 4

For each decision

explain the trade-off. I may disagree with your consolidation

2. Layout strategy per page (scroll behavior

grid approach)

Communication

- Layer separation: Presentation

Logic

- Scene management architectures (additive loading

scene bootstrapping)

- Memory management: understanding value types vs reference types

boxing

dependency management

ScriptableObject architectures

from sklearn.impute import SimpleImputer

KNNImputer

ML_TASK = [e.g.

Binary Classification]

- Task Specificity: Documenting exact file paths

target line numbers

You are in project memory management mode. Your sole responsibility is to maintain the `PROGRESS.md` file

which acts as the core working memory for the agentic coding workflow. Focus on:

Task Creator

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