Borrowing it
Nothing to install: this file belongs to Piyush8296/claude-workspace. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Piyush8296/claude-workspace/main/.claude/skills/systematic-debugging/SKILL.mdgit clone --depth 1 https://github.com/Piyush8296/claude-workspaceWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/piyush8296/claude-workspace/systematic-debugging)<a href="https://agentmods.dev/skills/piyush8296/claude-workspace/systematic-debugging"><img src="https://agentmods.dev/badge/skills/piyush8296/claude-workspace/systematic-debugging/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/piyush8296/claude-workspace/systematic-debugging"><img src="https://agentmods.dev/badge/skills/piyush8296/claude-workspace/systematic-debugging.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00044 | $0.01288 |
| Opus 5.5 | $0.00018 | $0.00515 |
| Sonnet 5.5 | $0.00009 | $0.00258 |
| Haiku 4.5 | $0.00004 | $0.00129 |
Grade A, and why
systematic-debugging scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 177 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Systematic Debugging
Core Principle
NO FIXES WITHOUT ROOT CAUSE INVESTIGATION FIRST.
Never apply symptom-focused patches that mask underlying problems. Understand WHY something fails before writing a single line of fix code.
The Four-Phase Framework
Phase 1: Reproduce & Gather Evidence
Before touching any code:
- Read the full error — every word matters, including the stack trace
- Reproduce consistently — define exact steps. If you can't reproduce, you can't verify a fix
- Check recent changes —
git log --oneline -10, what changed before this broke? - Gather diagnostics — console output, network tab, React DevTools, state snapshots
- Define expected vs actual — write it down explicitly
# Find the breaking change
git log --oneline -20
git bisect start
git bisect bad HEAD
git bisect good <last-known-good-commit>
Phase 2: Trace to Root Cause
Follow the data, not your assumptions:
1. Where does the error appear? (symptom location)
2. What value is wrong at that point?
3. Where does that value come from? (trace upstream)
4. Is the value wrong at the source, or corrupted in transit?
5. What assumption was violated?
Key insight: The bug is rarely where the error appears. Trace upstream until you find where correct data becomes incorrect.
Phase 3: Hypothesize & Test
Scientific method, not shotgun debugging:
- One clear hypothesis — "The error occurs because X causes Y"
- Predict the outcome — "If my hypothesis is correct, then Z should happen when I..."
- Change ONE variable — isolate the test
- Observe result — did it match prediction?
- Iterate or proceed — revise hypothesis if wrong, implement if right
Phase 4: Fix & Verify
- Write a failing test that captures the exact bug
- Implement the fix — address root cause, not symptoms
- Verify the test passes
- Run full test suite — no regressions
- Verify in browser — test the actual user flow
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 177 lines · 44 tokens per session scan A 7d2b91ab2ce0
systematic-debugging is a skill published in the GitHub repository Piyush8296/claude-workspace (2 stars, last pushed 6mo ago), licensed MIT. It adds 44 tokens to every session and 1,288 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
monitoring-observability
OpenTelemetry, structured logging, distributed tracing, alerting, and dashboards. Use when instrumenting services, designing logs/traces/metrics, defining SLOs and alerts, or debugging production behavior from telemetry.
thinking-scientific-method
When a symptom has several plausible causes, rank falsifiable hypotheses and run the cheapest discriminating observation first; prefer least-assumptive survivors only after evidence fit.
debug-systematic
Systematic 4-phase debugging methodology for complex, intermittent, or mysterious issues. Use when investigating bugs, race conditions, or unexplained failures.
thinking-five-whys-plus
When a fault is localized and the proximate cause is known but the systemic root is not, chain evidence-linked whys with a counterfactual stop and a countermeasure.
thinking-map-territory
When a claim, doc, test, metric, or assumption conflicts with observed behavior, stop theorizing from the map and verify the live code or data; let territory overrule.
windows-ops
Comprehensive Windows workstation operations - diagnose slow boot, failing drives, BSOD crashes, startup bloat, runaway processes, event logs. Use for: Windows is slow, slow bootup, won't boot, blue screen, BSOD, kernel crash, drive failing, SMART errors, disk errors, Event 41, Event 129, storahci reset, BugCheck…