Debug Code Systematically
Debug code with a scientific method instead of guessing. Use when fixing bugs, regressions, crashes, stack traces, failing tests, unexpected behavior, or "not working" reports. Produces a reproduced failure, tested hypotheses, isolated root cause, fix, and regression check.
Install
$ agentstack add skill-ariaxhan-the-agent-library-debug-code-systematically ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Links to your public security report.[](https://agentstack.voostack.com/security/report/skill-ariaxhan-the-agent-library-debug-code-systematically) Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
Debug Code Systematically
Debugging is theory work. Reproduce, hypothesize, isolate, fix root cause, and prove the original failure is gone.
1. Reproduce
Capture:
- Exact input or action
- Expected behavior
- Actual behavior
- Error output or stack trace
- Environment
- Frequency
If reproduction is not possible, add targeted logging or a minimal observation plan before changing code.
2. List hypotheses
Write at least three plausible causes before pursuing one. Read all error output first.
For each hypothesis, name what evidence would disprove it. Do not chase the first explanation that feels right.
3. Isolate
Use binary search:
- Call chain: inspect the midpoint, then recurse into the failing half
- Git history: bisect between known-good and known-bad
- Input: cut the failing input until the minimal case remains
- Boundaries: log inputs/outputs at module or service edges
Stop exploring when the failure is localized to a specific function, commit, input shape, or external dependency.
4. Find root cause
State the violated invariant in one sentence.
Do not patch only the visible failure site unless that is the actual defect. A null check at a crash site is usually not enough; ask why null arrived there.
5. Fix with a regression check
Make the smallest root-cause fix. Add or update a test/check that fails before the fix and passes after.
Run:
- The original failing case
- The regression check
- Nearby edge cases
- The nearest relevant test suite
6. Report evidence
Return:
- Reproduction
- Root cause
- Files changed
- Regression check
- Commands run and results
- Any residual risk
If you could not reproduce or fully verify, say that directly.
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: ariaxhan
- Source: ariaxhan/the-agent-library
- License: MIT
Install and usage instructions live in the source repository linked above.
Reviews
No reviews yet, be the first.
Write a review
Versions
- v0.1.0 Imported from the upstream source.