Applies the scientific method to debugging by helping users form specific, testable hypotheses, design targeted experiments, and systematically confirm or reject theories to find root causes. Use when a user says their code isn't working, they're getting an error, something broke, they want to troubleshoot a bug, or they're trying to figure out what's causing an issue. Concrete actions include isolating failing components, forming and testing hypotheses, analyzing error messages, tracing execution paths, and interpreting test results to narrow down root causes.
npx skills add https://github.com/rohitg00/skillkit --skill hypothesis-testing
You are applying the scientific method to debugging. Form clear hypotheses, design tests that can definitively confirm or reject them, and systematically narrow down to the truth.
Every debugging action should test a specific hypothesis. Random changes are not debugging.
Before forming hypotheses, collect observations:
Write down observations objectively:
Observations:
- API returns 500 error on POST /orders
- Happens only when cart has > 10 items
- Started after deployment on 2024-01-15
- Works fine in staging environment
- Error logs show "connection refused" to inventory service
Examples (bad → good):
For each hypothesis, define what you expect to observe if it is true versus false:
Hypothesis: Connection pool exhausted for large orders
If TRUE:
- Active connections should hit max (20) during large orders
- Small orders should still work during this time
- Increasing pool size should fix the issue
If FALSE:
- Connection count stays well below max
- Small orders also fail during the issue
- Pool size change has no effect
Design tests that definitively confirm or reject:
Test Plan for Connection Pool Hypothesis:
1. Add connection pool monitoring
- Log active connections before/after each request
- Expected if true: Count reaches 20 during failures
2. Artificial stress test
- Send 5 large orders simultaneously
- Expected if true: Failures start when pool exhausted
3. Increase pool size to 50
- Repeat stress test
- Expected if true: Failures stop or threshold moves
4. Control test with small orders
- Send 20 small orders simultaneously
- Expected if true: No failures (faster processing)
After testing:
Results:
- Connection count reached 20/20 during failures ✓
- Small orders succeeded during same period ✓
- Pool size increase to 50 → failures stopped ✓
Conclusion: Hypothesis CONFIRMED
Connection pool exhaustion is the proximate cause.
New question: Why do large orders exhaust the pool?
New hypothesis: Large orders make multiple inventory calls per item
## Bug: [Description]
### Hypothesis 1: [Theory]
**Status:** Testing | Confirmed | Rejected
**Probability:** High | Medium | Low
**Evidence For:**
- [Evidence 1]
- [Evidence 2]
**Evidence Against:**
- [Evidence 1]
**Test Plan:**
1. [Test 1] - Expected result if true
2. [Test 2] - Expected result if false
**Test Results:**
- [Result 1]: [Supports/Contradicts]
- [Result 2]: [Supports/Contradicts]
**Conclusion:** [Confirmed/Rejected] because [reasoning]
---
### Hypothesis 2: [Next Theory]
...
// Add timing instrumentation
const start = performance.now();
await suspectedSlowOperation();
const duration = performance.now() - start;
console.log(`Operation took ${duration}ms`);
// Hypothesis confirmed if duration > expected
// Validate data at key points
function processWithValidation(data) {
console.assert(data.id != null, 'Missing id');
console.assert(data.items?.length > 0, 'Empty items');
console.assert(typeof data.total === 'number', 'Invalid total');
// If assertions fail, data hypothesis likely true
}
// Snapshot state before and after
const stateBefore = JSON.stringify(currentState);
suspectedStateMutation();
const stateAfter = JSON.stringify(currentState);
if (stateBefore !== stateAfter) {
console.log('State changed:', diff(stateBefore, stateAfter));
}
Is the hypothesis testable?
├── NO → Refine it to be more specific
└── YES → Can I test it without side effects?
├── NO → Design a safe test (staging, logs-only)
└── YES → Run the test
└── Results conclusive?
├── NO → Design a better test
└── YES → Hypothesis confirmed or rejected?
├── CONFIRMED → Root cause found?
│ ├── YES → Fix and verify
│ └── NO → Form next hypothesis (why?)
└── REJECTED → Form next hypothesis
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 rohitg00/hypothesis-testing 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.