trace-and-isolate
The trace-and-isolate skill provides systematic debugging techniques to identify the exact origin of bugs in code using binary search debugging, git bisect for regressions, strategic logging patterns, component isolation, minimal reproduction cases, and conditional breakpoints. Use this skill when bugs are difficult to locate, errors have unclear causes, regressions appeared between recent commits, or you need to narrow down which specific function or component is faulty across TypeScript, SQL, and bash environments.
git clone --depth 1 https://github.com/rohitg00/skillkit /tmp/trace-and-isolate && cp -r /tmp/trace-and-isolate/packages/core/src/methodology/packs/debugging/trace-and-isolate ~/.claude/skills/trace-and-isolateSKILL.md
# Trace and Isolate
You are using systematic tracing and isolation techniques to narrow down where a bug originates. The goal is to find the exact location in code where behavior diverges from expectation.
## Quick Reference
| Scenario | Technique |
|---|---|
| Large codebase / complex flow | Binary search debugging |
| Bug appeared between commits | `git bisect` |
| Unclear data transformation | Strategic `[TRACE]` logging |
| Which component is faulty? | Component isolation + mocks |
| Bug hard to reproduce | Minimal reproduction case |
| Conditional or intermittent bug | Conditional breakpoints / watch expressions |
## Binary Search Debugging
When you have a large codebase or complex flow:
1. **Identify the start** - Last known good state
2. **Identify the end** - First observed bad state
3. **Test the middle** - Check if behavior is good or bad
4. **Repeat** - Binary search on the half with the bug
### Code Path Binary Search
```
Start: User clicks submit button
End: Error shown to user
Midpoint 1: Form validation
→ Data looks correct here? Continue to later code
→ Data already wrong? Focus on earlier code
Midpoint 2: API request construction
→ Request payload correct? Focus on server side
→ Payload already malformed? Focus on form handling
...Continue until exact line is found
```
### Git Bisect for Regressions
When a bug appeared between two commits:
```bash
# Start bisect
git bisect start
# Mark current (broken) state as bad
git bisect bad
# Mark last known good state
git bisect good v2.3.0
# Git checks out middle commit, test and mark
git bisect good # or git bisect bad
# Repeat until found
# Git will report: "abc123 is the first bad commit"
# Clean up
git bisect reset
```
Automated bisect with test script:
```bash
git bisect start HEAD v2.3.0
git bisect run npm test -- --grep "failing test"
```
## Tracing Techniques
### Strategic Logging
Add temporary logging at key points:
```typescript
function processOrder(order) {
console.log('[TRACE] processOrder input:', JSON.stringify(order));
const validated = validateOrder(order);
console.log('[TRACE] after validation:', JSON.stringify(validated));
const priced = calculatePrice(validated);
console.log('[TRACE] after pricing:', JSON.stringify(priced));
const result = submitOrder(priced);
console.log('[TRACE] final result:', JSON.stringify(result));
return result;
}
```
Log template: `[TRACE] <location>: <what> = <value>`
### Data Flow Tracing
Track how data transforms through the system:
```
Input: { userId: "123", items: [...] }
↓
validateUser() → { userId: "123", verified: true }
↓
enrichItems() → { userId: "123", verified: true, items: [...enriched] }
↓
calculateTotals() → { ..., subtotal: 100, tax: 8, total: 108 }
↓
Output: { orderId: "456", total: 108 }
```
At each step, verify:
- Is the input what you expected?
- Is the output what you expected?
- Where does expected diverge from actual?
### Control Flow Tracing
Track which code paths execute:
```typescript
function handleRequest(req) {
console.log('[TRACE] handleRequest entered');
if (req.authenticated) {
console.log('[TRACE] authenticated path');
if (req.isAdmin) {
console.log('[TRACE] admin path');
return handleAdminRequest(req);
} else {
console.log('[TRACE] user path');
return handleUserRequest(req);
}
} else {
console.log('[TRACE] unauthenticated path');
return handlePublicRequest(req);
}
}
```
## Isolation Techniques
### Component Isolation
Test components in isolation to determine which is faulty:
```
Full System: Frontend → API → Database
↓
Test 1: Frontend → Mock API
→ Works? Problem is in API or Database
Test 2: Real API → Mock Database
→ Works? Problem is in Database
Test 3: API with minimal data
→ Works? Problem is data-dependent
```
### Minimal Reproduction
Strip away everything non-essential:
1. **Remove unrelated code** - Comment out or delete
2. **Simplify data** - Use minimal test data
3. **Remove dependencies** - Mock external services
4. **Reduce scope** - Single function/component
Goal: Smallest possible code that still shows the bug
### Environment Isolation
Eliminate environmental factors:
- [ ] Same behavior in different browsers?
- [ ] Same behavior on different machines?
- [ ] Same behavior with fresh data?
- [ ] Same behavior after clearing cache?
- [ ] Same behavior with default config?
## Breakpoint Strategies
### Strategic Breakpoint Placement
```typescript
function complexFunction(input) {
// BREAKPOINT 1: Entry - check input
const step1 = transform(input);
// BREAKPOINT 2: After first transformation
for (const item of step1.items) {
// BREAKPOINT 3: Inside loop - conditional on item
process(item);
}
// BREAKPOINT 4: Exit - check output
return finalize(step1);
}
```
### Conditional Breakpoints
Only break when condition is met:
- `item.id === "problematic-id"`
- `count > 100`
- `response.status !== 200`
### Watch Expressions
Monitor values without stopping:
- `this.state.items.length`
- `performance.now() - startTime`
- `Object.keys(cache).length`
## Isolation Checklist
Before declaring a component faulty:
- [ ] Tested in complete isolation?
- [ ] All inputs verified correct?
- [ ] All dependencies mocked/verified?
- [ ] Tested with known-good data?
- [ ] Reproduced on clean environment?
## Common Isolation Patterns
### Database Isolation
```sql
-- Create isolated test data
BEGIN TRANSACTION;
-- Insert test data
-- Run test queries
-- Verify results
ROLLBACK;
```
### Network Isolation
```typescript
// Intercept and log all network requests
const originalFetch = window.fetch;
window.fetch = async (...args) => {
console.log('[TRACE] fetch:', args[0]);
const response = await originalFetch(...args);
console.log('[TRACE] response:', response.status);
return response;
};
```
### Time Isolation
```typescript
// Control time for debugging
cManages work transitions between team members or agents by creating structured handoff documents, summarizing project status, documenting key decisions, blockers, and open questions, and generating onboarding briefs. Use when someone needs to hand off, hand over, or transition a project; pass work to another person or agent; brief a colleague taking over; prepare a shift change summary; or onboard someone mid-task. Produces ready-to-use handoff documents covering current status, next steps, known issues, technical context, and communication templates for both planned and unplanned transfers.
Coordinates parallel investigation threads to simultaneously explore multiple hypotheses or root causes across different system areas. Use when debugging production incidents, slow API performance, multi-system integration failures, or complex bugs where the root cause is unclear and multiple plausible theories exist; when serial troubleshooting is too slow; or when multiple investigators can divide root-cause analysis work. Provides structured phases for problem decomposition, thread assignment, sync points with Continue/Pivot/Converge decisions, and final report synthesis.
Performs a structured five-stage code review covering requirements compliance, correctness, code quality, testing, and security/performance. Each stage uses targeted checklists and categorized feedback (Blocker/Major/Minor/Nit) with actionable suggestions and rationale. Use when the user asks for code review, PR feedback, pull request review, or wants their code checked for bugs, style issues, or vulnerabilities — triggered by phrases like "review my code", "check this PR", "review my changes", "pull request review", or "code feedback".
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.
Performs systematic root cause analysis to identify the true source of bugs, errors, and unexpected behavior through structured investigation phases — not just treating symptoms. Use when a user reports a bug, crash, error, or broken behavior and needs to debug, troubleshoot, or investigate why something is not working; especially for complex or intermittent issues across multiple components. Applies the Five Whys method, hypothesis-driven testing, stack trace analysis, git blame/log evidence gathering, and causal chain documentation to isolate and confirm root causes before applying any fix.
Creates and structures SKILL.md files for AI coding agents, including YAML frontmatter, trigger phrases, directive instructions, decision trees, code examples, and verification checklists. Use when the user asks to write a new skill, create a skill file, author agent capabilities, generate skill documentation, or define a skill template for Claude Code agents.
Guides the creation of technical design documents before writing code, producing architecture diagrams, data models, API interface definitions, implementation plans, and multi-option trade-off analyses. Use when the user asks to plan a feature, architect a system, design an API, explore implementation approaches, or requests a technical design or spec before coding — especially for complex features involving multiple components, ambiguous requirements, or significant architectural changes.
Breaks down complex software, writing, or research tasks into small, atomic, independently completable units with dependency graphs and milestone breakdowns. Use when the user asks to plan a project, decompose a feature, create subtasks, split up work, or needs help organizing a large piece of work into a step-by-step plan. Triggered by phrases like "break down", "decompose", "where do I start", "too big", "split into tasks", "work breakdown", or "task list".