(TDD) Use when implementing any feature or bugfix. Apply the Logic Gate to identify what needs tests, then use the Iron Rule for strict test-first development on logic.
npx skills add https://github.com/ravila4/claude-adhd-skills --skill test-driven-development
Before writing any code, pass it through two gates:
flowchart TD
A[New code to write] --> B[Decompose into pieces]
B --> C{{"Logic Gate: Does this piece contain logic?"}}
C -->|"Conditionals, transformations, validation, state changes, error handling"| D["Iron Rule: TDD"]
C -->|"Dataclasses, config, thin wrappers, type aliases, formatting"| E[Write directly]
D --> F["RED: Write failing test"]
F --> G["GREEN: Minimal implementation"]
G --> H["REFACTOR: Clean up"]
H --> I{More pieces?}
E --> I
I -->|Yes| C
I -->|No| J[Done]
Decompose the work into its constituent pieces. For each piece, ask: does this contain logic?
Passes the Logic Gate (requires TDD):
Does NOT pass the Logic Gate (write directly):
__init__.py, module-level setup)When in doubt: If you can't point to a conditional, transformation, or state change, it probably doesn't pass the Logic Gate.
Before writing code, briefly identify what passes and what doesn't:
For code that passes the Logic Gate: never write logic without a preceding failing test.
If logic is written before its test:
Write a single minimal test demonstrating intended behavior:
Run tests and confirm:
Write the simplest possible code satisfying the test:
Confirm:
Improve code quality while maintaining green status:
Minimal: Single responsibility per test
Clear: Descriptive name revealing behavior
Realistic: Demonstrate intended API usage
Focused: One assertion per test (generally)
"I'll test afterward"
"Manual testing covered edge cases"
"Tests-after achieve identical goals"
Before considering work complete:
Bug found? Write failing test reproducing it. Follow TDD cycle. Test proves fix and prevents regression.
Never fix bugs without a test.
Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.
Use when implementation is complete, all tests pass, and you need to decide how to integrate the work - guides completion of development work by presenting structured options for merge, PR, or cleanup
Use when implementing any feature or bugfix, before writing implementation code
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes
Use when about to claim work is complete, fixed, or passing, before committing or creating PRs - requires running verification commands and confirming output before making any success claims; evidence before assertions always
Expert guidance for systematic backtesting of trading strategies. Use when developing, testing, stress-testing, or validating quantitative trading strategies. Covers "beating ideas to death" methodology, parameter robustness testing, slippage modeling, bias prevention, and interpreting backtest results. Applicable when user asks about backtesting, strategy validation, robustness testing, avoiding overfitting, or systematic trading development.
Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental validation including binding assays, expression testing, thermostability measurements, enzyme activity assays, or protein sequence optimization. Also use for submitting experiments via API, tracking experiment status, downloading results, optimizing protein sequences for better expression using computational tools (NetSolP, SoluProt, SolubleMPNN, ESM), or managing protein design workflows with wet-lab validation.
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
Take ravila4/test-driven-development from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.