debug-investigator

name	debug-investigator
description	Hypothesis-driven debugging with ranked hypotheses, git bisect strategy, instrumentation planning, and minimal reproduction design. Triggers on: "debug this systematically", "root cause analysis", "bisect this bug", "rank hypotheses", "isolate this issue", "minimal reproduction". NOT for general reasoning.
metadata	{"version":"1.1.1","category":"development","tags":["debugging","root-cause","hypothesis","bisect"],"difficulty":"intermediate","phase":"build"}

Debug Investigator

Structured debugging methodology that replaces ad-hoc exploration with hypothesis-driven investigation. Captures symptoms, builds a deterministic feedback loop, analyzes evidence (stacktraces, logs, state), generates ranked hypotheses, designs bisection strategies, identifies instrumentation points, and produces minimal reproductions — documenting every step so dead ends are never revisited.

When to use this skill vs native debugging: The base model handles straightforward debugging (clear stacktraces, obvious errors) natively. Use this skill for non-obvious bugs requiring systematic investigation: intermittent failures, bugs with no clear stacktrace, performance regressions, or issues requiring git bisection and hypothesis ranking.

Reference Files

File	Contents	Load When
references/stacktrace-patterns.md	Exception taxonomy, traceback reading, common Python/JS error signatures	Stacktrace or exception present
references/hypothesis-templates.md	Bug category catalog, probability ranking, confirmation/refutation tests	Always
references/bisection-guide.md	git bisect workflow, binary search debugging, narrowing techniques	Bug appeared after a change
references/log-analysis.md	Log pattern extraction, anomaly detection, timeline correlation	Log output available
references/instrumentation-points.md	Strategic logging placement, breakpoint strategy, state inspection techniques	Investigation plan needed

Prerequisites

• git — for bisection and history analysis
• Access to source code — cannot debug opaque binaries
• Reproducible environment — or at minimum, error output (stacktrace, logs)

Project Context

Before deep investigation, check for repo-local agent context:

• docs/agents/domain.md for CONTEXT.md, CONTEXT-MAP.md, and ADR lookup rules
• CONTEXT.md or relevant context-local glossary for domain vocabulary
• docs/adr/ and context-local ADRs for decisions near the failing area

Use the project glossary in hypotheses, repro names, and prevention recommendations. If the repo lacks these files, continue normally; do not block debugging on context setup.

Workflow

Phase 1: Symptom Capture

Before touching code, document the observable problem:

1. What is happening? — Describe the observed behavior precisely. "It crashes" is insufficient. "Raises KeyError('user_id') on line 42 of auth.py when calling get_current_user() with a valid session token" is actionable.
2. What should happen? — Define the expected behavior. If unknown, state that.
3. Reproducibility — Always, intermittent (with frequency), or one-time? Intermittent bugs require different strategies than deterministic ones.
4. Recency — When did this start? Correlate with recent changes: git log --oneline -20. If the bug appeared after a specific commit, bisection is the fastest path.
5. Environment — Python version, OS, dependency versions, configuration differences between working and broken environments.

Phase 2: Build a Feedback Loop

Create a fast, deterministic pass/fail signal for the reported bug before ranking hypotheses or changing production code. The loop must reproduce the user's symptom, not a nearby failure.

Try these seams in order:

1. Failing test at the smallest public interface that reaches the bug.
2. CLI or script invocation with fixture input and asserted output.
3. Curl or HTTP request against a local server with asserted response, logs, or state.
4. Browser automation for UI bugs with DOM, console, and network assertions.
5. Replayed trace, event payload, HAR, or log fixture through the real code path.
6. Throwaway harness that boots the minimal subsystem needed to trigger the path.
7. Property, fuzz, or stress loop for intermittent failures.
8. git bisect run harness when the bug appeared between known good and bad revisions.

Improve the loop before moving on:

• Make it faster by narrowing setup and caching expensive fixtures.
• Make it sharper by asserting the exact symptom.
• Make it more deterministic by pinning time, seeds, filesystem paths, and network access.
• For intermittent bugs, raise reproduction rate with repeated runs, concurrency, stress, or timing probes until the failure is frequent enough to debug.

If no credible loop can be built, stop and state what was tried. Request the missing artifact: environment access, captured payloads, logs, screen recording with timestamps, or permission for temporary instrumentation. Do not proceed to speculative fixes.

Phase 3: Evidence Analysis

Examine all available evidence before forming hypotheses:

1. Stacktrace interpretation — If a traceback exists, read it bottom-up. The last frame is where the error manifested, but the cause is often several frames up. Identify:

   • Exception type and message
   • The frame where the error originated vs. where it was raised
   • Any familiar patterns (see references/stacktrace-patterns.md)

2. Log pattern extraction — Search logs for:

   • Temporal anomalies (timestamps out of sequence, gaps)
   • Repeated errors (same error appearing in bursts)
   • State transitions that didn't complete
   • Correlation with external events (deploys, config changes)

3. State inspection — If the system is running, inspect:

   • Variable values at the failure point
   • Database state (missing rows, unexpected values)
   • Configuration values (environment variables, config files)
   • External dependency status (API availability, DB connectivity)

4. Code diff analysis — If the bug is recent:

   • git diff HEAD~5 — what changed?
   • Focus on files touched by the error's call chain
   • Look for typos, wrong variable names, missing null checks

Phase 4: Hypothesis Generation

Generate ranked hypotheses — never start fixing without a hypothesis:

1. List 3-5 hypotheses ranked by likelihood. Each hypothesis must include:

   • A concrete claim about what is wrong
   • What evidence supports it
   • What evidence would confirm it (a test you can run)
   • What evidence would refute it

2. Rank by likelihood using:

   • Proximity to recent changes (most bugs are in new code)
   • Simplicity (typos before race conditions)
   • Evidence fit (does the hypothesis explain ALL symptoms?)

3. Common bug categories (see references/hypothesis-templates.md):

   • State bugs: wrong value, missing initialization, stale cache
   • Logic bugs: off-by-one, wrong operator, inverted condition
   • Integration bugs: API contract mismatch, serialization error
   • Concurrency bugs: race condition, deadlock, resource starvation
   • Environment bugs: missing dependency, wrong config, version mismatch

Phase 5: Investigation Plan

Design specific steps to test each hypothesis:

1. Test H1 first — Always test the most likely hypothesis first. Design a single action that will confirm or refute it.
2. Bisection — If the bug appeared after a change and H1 fails:
   • Identify the known-good and known-bad commits
   • Run git bisect start <bad> <good>
   • Define the test command for each commit
   • See references/bisection-guide.md for workflow
3. Isolation — Remove variables one at a time:
   • Simplify input data
   • Disable features/plugins
   • Replace external calls with hardcoded values
   • Run in a clean environment
4. Instrumentation — Add targeted logging/breakpoints:
   • At function entry/exit points in the call chain
   • Before and after state mutations
   • At decision points (if/else branches)
   • See references/instrumentation-points.md

Phase 6: Execution

Execute the investigation plan, updating hypotheses as evidence arrives:

1. Test one variable at a time — Changing multiple things simultaneously makes results uninterpretable.
2. Record results — Document what each test revealed, even negative results. Dead-end documentation prevents revisiting failed paths.
3. Update probabilities — After each test, re-rank hypotheses. If H1 is refuted, H2 becomes the new priority.
4. Know when to escalate — If all hypotheses are exhausted, the bug is in a category you haven't considered. Step back and re-examine assumptions.

Phase 7: Resolution Documentation

After finding the root cause:

1. Root cause — What was actually wrong, precisely.
2. Fix — What was changed and why.
3. Prevention — How to prevent recurrence (test, lint rule, type check, etc.).
4. Lessons — What was learned that applies beyond this specific bug.

Output Format

## Debug Investigation: {Brief Description}

### Symptom
**Observed:** {What is happening — precise description}
**Expected:** {What should happen}
**Reproducibility:** {Always | Intermittent (~N% of attempts) | Once}
**First noticed:** {Date/time or triggering event}
**Environment:** {Relevant versions and configuration}

### Evidence Analysis

#### Stacktrace
- **Exception:** {type}: {message}
- **Origin:** {file}:{line} in {function}
- **Call chain:** {caller} → {caller} → {failure point}
- **Key insight:** {What the traceback reveals about the cause}

#### Logs
- **Anomaly:** {What is unusual}
- **Timeline:** {When the anomaly started}
- **Correlation:** {Related events}

#### Code Changes
- **Recent commits:** {relevant commits since last known-good state}
- **Files in error path:** {which changed files appear in the traceback}

### Hypotheses

| # | Hypothesis | Likelihood | Confirming Test | Refuting Test |
|---|------------|------------|-----------------|---------------|
| H1 | {Specific claim} | High | {What to check} | {What would disprove} |
| H2 | {Specific claim} | Medium | {What to check} | {What would disprove} |
| H3 | {Specific claim} | Low | {What to check} | {What would disprove} |

### Investigation Plan

#### Step 1: Test H1 — {action}
- **Command/action:** {specific step}
- **If confirmed:** {next action — fix}
- **If refuted:** proceed to Step 2

#### Step 2: Bisection
- **Good commit:** {hash}
- **Bad commit:** {hash}
- **Test:** {command to verify each commit}
- **Command:** `git bisect start {bad} {good}`

#### Step 3: Isolation
- **Remove:** {variable to eliminate}
- **Expected change:** {what should happen}

### Instrumentation Points
1. {file}:{line} — log {variable/state} to observe {what}
2. {file}:{line} — breakpoint to inspect {what}

### Minimal Reproduction
```{language}
# Minimal code that triggers the bug
{code}

Resolution

Root cause: {What was wrong} Fix: {What was changed — file:line, diff summary} Prevention: {Test added, lint rule, type annotation, etc.} Lessons: {What generalizes beyond this bug}

## Configuring Scope

| Mode | Scope | Depth | When to Use |
|------|-------|-------|-------------|
| `quick` | Single error | H1 test + fix | Clear stacktrace, obvious cause |
| `standard` | Full investigation | 3 hypotheses + bisection plan | Default for non-obvious bugs |
| `deep` | Systemic analysis | 5+ hypotheses + instrumentation + reproduction | Intermittent bugs, no stacktrace, production issues |

## Calibration Rules

1. **Hypotheses before code changes.** Never start modifying code without at least one
   explicit hypothesis. "Let me try this" is not debugging — it's guessing.
2. **One variable at a time.** Each investigation step should change exactly one thing.
   If you change two things and the bug disappears, you don't know which fixed it.
3. **Document dead ends.** Failed hypotheses are valuable — they narrow the search space.
   Record what was tested and what was learned.
4. **Simplest explanation first.** Test typos, wrong variable names, and missing imports
   before considering race conditions, compiler bugs, or cosmic rays.
5. **Feedback loop before hypotheses.** If you cannot reproduce the bug with a controlled
   pass/fail signal, any fix is speculative. Invest in the loop first.
6. **Root cause, not symptoms.** A fix that addresses the symptom (adding a null check)
   without understanding the root cause (why was it null?) leaves the real bug alive.

## Error Handling

| Problem | Resolution |
|---------|------------|
| No stacktrace available | Focus on log analysis and state inspection. Use instrumentation to generate diagnostic output. |
| Bug is intermittent | Add persistent logging at key decision points. Run under stress (high load, concurrent requests) to increase reproduction rate. |
| Cannot reproduce locally | Compare environments systematically: versions, config, data, timing. Use `docker` or VM to mirror production. |
| Multiple hypotheses equally likely | Design a single test that distinguishes between them. Binary decision: "If X, then H1; if Y, then H2." |
| Fix attempted but bug persists | The hypothesis was wrong. Revert the fix, update hypothesis rankings, and proceed to the next hypothesis. Do not stack fixes. |
| Bug is in a dependency | Confirm with a minimal reproduction that uses only the dependency. Check issue trackers. Pin to last known-good version while awaiting upstream fix. |

## When NOT to Investigate

Push back if:
- The error message already contains the fix ("missing module X" → install X)
- The issue is a known environment setup problem (wrong Python version, missing env var)
- The "bug" is actually a feature request or design disagreement — redirect to ADR or discussion
- The code is not under the user's control (third-party SaaS, managed service) — file a support ticket instead
- The user wants to debug generated/minified code — debug the source, not the output