Skill systematic-debugging
Enforces a four-phase root cause analysis process to reproduce bugs, test hypotheses, and apply hardened fixes with regression testing.
- Estrellas en GitHub
- 44
- Archivos
- 1
- Último commit
- 12 abr 2026
- Licencia
- MIT
- Herramientas permitidas
- —
¿Qué hace la skill systematic-debugging?
systematic-debugging es una agent skill. Enforces a four-phase root cause analysis process to reproduce bugs, test hypotheses, and apply hardened fixes with regression testing.
Resumen generado a partir del SKILL.md de la skill.
Sobre la skill systematic-debugging
Replacing trial-and-error fixes with scientific rigor is what this skill enables for developers facing complex software issues. It provides a structured four-phase root cause analysis process that forces an agent to reproduce a bug reliably before attempting any code changes. By following a strict progression of reproduction, hypothesis ranking, testing, and hardening, the agent avoids shotgun debugging where multiple variables are changed at once.
An agent should load this skill when a test fails unexpectedly, a user reports a bug with a stack trace, or code behaves differently in production. The process produces a targeted fix, a new regression test, and defense-in-depth measures like input validation or assertions. It relies on tools like the shell, debugger, and git log to examine recent changes and instrument code. This approach ensures that every fix is accompanied by a post-mortem note documenting the root cause and preventive measures.
Lo que un agente puede hacer con ella
- 1Resolve intermittent test failuresAn agent can use this skill when a test fails unexpectedly or intermittently to find the true cause.
- 2Fix bugs from user reportsDevelopers can hand a stack trace or reproduction steps from a user to the agent to begin a formal analysis.
- 3Prevent bug recurrenceThe agent applies hardening measures like assertions and monitoring to ensure the same class of bug does not return.
- 4Investigate production discrepanciesUse this skill when code behaves differently in a production environment compared to development.
Escrito a partir del SKILL.md de la skill. Cada caso de uso se verifica con una cita del mismo.
Cómo instalar la skill systematic-debugging
Cada comando copia la carpeta de la skill al directorio de skills del cliente. Reinicia el cliente o inicia una nueva sesión para cargarla.Instalar la skill systematic-debugging en Plugin
/plugin marketplace add itallstartedwithaidea/agent-skills /plugin install software-dev-skills@googleadsagent-skills
Instalar la skill systematic-debugging en Claude Code
~/.claude/skills/systematic-debugging/SKILL.md
$ npx degit itallstartedwithaidea/agent-skills/skills/software-dev/systematic-debugging ~/.claude/skills/systematic-debuggingInstalar la skill systematic-debugging en Claude Code (project)
.claude/skills/systematic-debugging/SKILL.md
$ npx degit itallstartedwithaidea/agent-skills/skills/software-dev/systematic-debugging .claude/skills/systematic-debuggingInstalar la skill systematic-debugging en Codex
~/.agents/skills/systematic-debugging/SKILL.md
$ npx degit itallstartedwithaidea/agent-skills/skills/software-dev/systematic-debugging ~/.agents/skills/systematic-debuggingInstalar la skill systematic-debugging en Gemini CLI
~/.gemini/skills/systematic-debugging/SKILL.md
$ npx degit itallstartedwithaidea/agent-skills/skills/software-dev/systematic-debugging ~/.gemini/skills/systematic-debuggingInstalar la skill systematic-debugging en Claude.ai
npx degit itallstartedwithaidea/agent-skills/skills/software-dev/systematic-debugging systematic-debugging && zip -r systematic-debugging.zip systematic-debugging # claude.ai → Customize → Skills → + → Create skill → Upload a skill → systematic-debugging.zip
SKILL.md
Ver en GitHubSystematic Debugging
Part of Agent Skills™ by googleadsagent.ai™
Description
Systematic Debugging replaces trial-and-error fixes with a disciplined four-phase root cause analysis process. The agent reproduces the bug reliably, generates ranked hypotheses, tests each hypothesis with minimal instrumentation, and applies a targeted fix with defense-in-depth hardening. No fix is applied without first understanding why the bug exists.
Agents are prone to "shotgun debugging"—changing multiple things simultaneously and hoping the problem disappears. This skill enforces scientific rigor: one variable at a time, observable evidence at each step, and a clear causal chain from root cause to fix. The agent must articulate the root cause in plain language before writing any corrective code.
After the immediate fix, the agent applies defense-in-depth: adding assertions, input validation, or monitoring that would catch the same class of bug in the future. The debugging session concludes with a post-mortem note documenting the root cause, the fix, and the preventive measures added.
Use When
- A test fails unexpectedly or intermittently
- A user reports a bug with a stack trace or reproduction steps
- Code behaves differently in production than in development
- An error message is unclear or misleading
- Multiple potential causes exist and guessing would waste time
- A previous fix attempt did not resolve the issue
How It Works
graph TD
A[Bug Report] --> B[Phase 1: Reproduce]
B --> C{Reproducible?}
C -->|No| D[Gather More Context]
D --> B
C -->|Yes| E[Phase 2: Hypothesize]
E --> F[Rank Hypotheses by Likelihood]
F --> G[Phase 3: Test Top Hypothesis]
G --> H{Root Cause Found?}
H -->|No| I[Eliminate Hypothesis]
I --> F
H -->|Yes| J[Phase 4: Fix + Harden]
J --> K[Write Regression Test]
K --> L[Apply Defense-in-Depth]
L --> M[Document Post-Mortem]
The four phases enforce a strict progression: you cannot fix what you cannot reproduce, you should not fix what you do not understand, and you must not close a bug without preventing its recurrence.
Implementation
class DebuggingSession:
def __init__(self, bug_report):
self.report = bug_report
self.hypotheses = []
self.evidence = []
self.root_cause = None
def phase_reproduce(self):
"""Create a minimal, reliable reproduction."""
minimal_input = self.minimize_reproduction(self.report.steps)
result = self.execute(minimal_input)
assert result.matches(self.report.expected_failure), \
"Cannot proceed without reliable reproduction"
return ReproductionCase(minimal_input, result)
def phase_hypothesize(self, repro):
"""Generate ranked hypotheses from evidence."""
self.hypotheses = [
Hypothesis("Race condition in async handler", likelihood=0.7),
Hypothesis("Null reference from cache miss", likelihood=0.5),
Hypothesis("Stale closure over mutable state", likelihood=0.3),
]
return sorted(self.hypotheses, key=lambda h: -h.likelihood)
def phase_test(self, hypothesis, repro):
"""Test one hypothesis with minimal instrumentation."""
probe = self.instrument(hypothesis.target_area)
result = self.execute_with_probe(repro, probe)
if result.confirms(hypothesis):
self.root_cause = hypothesis
else:
hypothesis.eliminated = True
self.evidence.append(result)
def phase_fix(self):
"""Apply targeted fix with defense-in-depth."""
fix = self.root_cause.generate_fix()
regression_test = self.root_cause.generate_regression_test()
hardening = self.apply_defense_in_depth([
InputValidation(self.root_cause.entry_point),
AssertionGuard(self.root_cause.invariant),
MonitoringAlert(self.root_cause.symptom),
])
return DebugResult(fix, regression_test, hardening)
Root-Cause Tracing Checklist
- Read the actual error, not just the message—examine the full stack trace
- Identify the first divergence from expected behavior, not the crash site
- Check recent changes via
git log --oneline -20andgit diff - Verify assumptions about input data, environment state, and dependencies
- Instrument, don't guess—add logging at the divergence point
Best Practices
- Always reproduce before hypothesizing; never skip Phase 1
- Limit active hypotheses to 3-5 ranked by likelihood and testability
- Change one variable per test cycle to maintain causal clarity
- Write the regression test before applying the fix
- Apply defense-in-depth: validation, assertions, and monitoring beyond the fix
- Document the root cause even for "obvious" bugs—patterns emerge over time
Platform Compatibility
| Platform | Support | Notes |
|---|---|---|
| Cursor | Full | Debugger + Shell for reproduction |
| VS Code | Full | Integrated debugger support |
| Windsurf | Full | Terminal-based debugging |
| Claude Code | Full | Shell access for instrumentation |
| Cline | Full | Step-through debugging |
| aider | Partial | Limited to log-based debugging |
Related Skills
- Test-Driven Development - Write the failing test that exposes the bug before applying any fix
- Code Review - Review the fix and regression test for quality before merging
- Agent Security Scanning - Security-focused analysis that may identify vulnerability root causes during debugging
Keywords
debugging root-cause-analysis systematic-debugging reproduce-hypothesize-test-fix defense-in-depth regression-test post-mortem bug-fix
© 2026 googleadsagent.ai™ | Agent Skills™ | MIT License
Archivos de esta skill
1Preguntas sobre la skill systematic-debugging
¿Cómo instalo la skill systematic-debugging en Claude Code?
Ejecuta npx degit itallstartedwithaidea/agent-skills/skills/software-dev/systematic-debugging ~/.claude/skills/systematic-debugging. Claude Code carga las skills desde ~/.claude/skills (todos los proyectos) o .claude/skills (un proyecto) y usa una skill cuando su descripción coincide con la tarea.
¿Qué clientes pueden usar la skill systematic-debugging?
Cualquier cliente que lea el formato Agent Skills: Claude Code, Codex, Gemini CLI y claude.ai (subir como ZIP). Los scripts de la skill se ejecutan solo donde el cliente permite la ejecución de código.
What are the phases of the systematic debugging process?
The process follows four strict phases: Reproduce, Hypothesize, Test, and Fix + Harden.
How does this skill prevent bugs from recurring?
It uses a defense-in-depth approach by adding assertions, input validation, or monitoring, and requires writing a regression test.