systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes
Skill by beita6969
Chalk Labs
Need help deploying systematic-debugging?
Chalk Labs sets up agents like this inside real teams: connected to your tools, tested on your data, with a human in the loop where it matters.
Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
npx agentmarket install skill/beita6969-systematic-debugging
Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.
Get the weekly diff
The weekly diff: one email a week with new agent tools, notable updates and grade changes, plus security alerts for tools you follow. Double opt-in, one-click unsubscribe.
About
Use cases: testing
Reviews
Write a reviewNo reviews yet. Installed it? Reviews come from verified installers only. How reviews work
Permissions
What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
- Tools
- No tools found
- Secrets
- None asked for
- Install scripts
- None found
- Runs as
- Node.js package
Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/systematic-debugging
What's inside
- Skill name
systematic-debugging- Description
- Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes
- License
- MIT
- Instructions
- 293 lines in SKILL.md · view on GitHub
Before installing
- Ships executable scripts
Files (11)
- CREATION-LOG.md
- SKILL.md
- condition-based-waiting-example.ts
- condition-based-waiting.md
- defense-in-depth.md
- find-polluter.sh
- root-cause-tracing.md
- test-academic.md
- test-pressure-1.md
- test-pressure-2.md
- test-pressure-3.md
SKILL.md (first lines)
# Systematic Debugging
## Overview
Random fixes waste time and create new bugs. Quick patches mask underlying issues.
**Core principle:** ALWAYS find root cause before attempting fixes. Symptom fixes are failure.
**Violating the letter of this process is violating the spirit of debugging.**
## The Iron Law
```
NO FIXES WITHOUT ROOT CAUSE INVESTIGATION FIRST
```
If you haven't completed Phase 1, you cannot propose fixes.
## When to Use
Use for ANY technical issue:
- Test failures
- Bugs in production
- Unexpected behavior
- Performance problems
- Build failures
- Integration issues
**Use this ESPECIALLY when:**
- Under time pressure (emergencies make guessing tempting)
- "Just one quick fix" seems obvious
- You've already tried multiple fixes
- Previous fix didn't work
- You don't fully understand the issue
**Don't skip when:**
- Issue seems simple (simple bugs have root causes too)
- You're in a hurry (rushing guarantees rework)
- Manager wants it fixed NOW (systematic is faster than thrashing)
## The Four Phases
You MUST complete each phase before proceeding to the next.
### Phase 1: Root Cause Investigation
**BEFORE attempting ANY fix:**
1. **Read Error Messages Carefully**
- Don't skip past errors or warnings
- They often contain the exact solution
- Read stack traces completely
- Note line numbers, file paths, error codes
2. **Reproduce Consistently**
- Can you trigger it reliably?
- What are the exact steps?
- Does it happen every time?
- If not reproducible → gather more data, don't guess
3. **Check Recent Changes**
- What changed that could cause this?
- Git diff, recent commits
- New dependencies, config changes
- Environmental differences
4. **Gather Evidence in Multi-Component Systems**
**WHEN system has multiple components (CI → build → signing, API → service → database):**
**BEFORE proposing fixes, add diagnostic instrumentation:**
```
For EACH component boundary:
- Log what data enters component
- Log what data exits component
- Verify environment/config propagation
- Check state at each layer
Run once to gather evidence showing WHERE it breaks
THEN analyze evidence to identify failing component
THEN investigate that specific component
```
**Example (multi-layer system):**
```bash
# Layer 1: Workflow
echo "=== Secrets available in workflow: ==="
echo "IDENTITY: ${IDENTITY:+SET}${IDENTITY:-UNSET}"
# Layer 2: Build script
echo "=== Env vars in build script: ==="
env | grep IDENTITY || echo "IDENTITY not in environment"
# Layer 3: Signing script
echo "=== Keychain state: ==="
security list-keychains
security find-identity -v
# Layer 4: Actual signing
codesign --sign "$IDENTITY" --verbose=4 "$APP"
```
**This reveals:** Which layer fails (secrets → workflow ✓, workflow → build ✗)
5. **Trace Data Flow**
**WHEN error is deep in call stack:**
See `root-cause-tracing.md` in this directory for the complete backward tracing technique.
**Quick version:**
- Where does bad value originate?
- What called this with bad value?
- Keep tracing up until you find the source
- Fix at source, not at symptom
### Phase 2: Pattern Analysis
**Find the pattern before fixing:**From beita6969/ScienceClaw, MIT.
More from beita6969/ScienceClaw
- 0.608score
scienceclaw-benchmark-for51
beita6969
Use when a task asks to predict a phonon property of inorganic crystals from their structure, for example the frequency of the last peak of the phonon density…
SkillAFreshness: A★ 1KClaude Code +9 - 0.608score
scienceclaw-benchmark-for44
beita6969
Use when a task gives a covariate table plus, for each of many simulated observational datasets, a binary treatment vector and an outcome vector, and asks for…
SkillAFreshness: A★ 1KClaude Code +9 - 0.608score
scienceclaw-benchmark-for43
beita6969
Use when a task gives noisy OCR text of historical newspapers, periodicals or books (several languages, with metadata and a set of labelled OCR/ground-truth…
SkillAFreshness: A★ 1KClaude Code +9 - 0.608score
scienceclaw-benchmark-for34
beita6969
Use when predicting a binary molecular property such as HIV replication inhibition (OGB ogbg-molhiv, MoleculeNet-style activity) from SMILES strings and…
SkillAFreshness: A★ 1KClaude Code +9 - 0.608score
scienceclaw-benchmark-for42
beita6969
Use when a task gives hourly ICU records (vitals, labs, demographics, one stay per patient) with sepsis labels and asks for causal hour-by-hour sepsis alarms…
SkillAFreshness: A★ 1KClaude Code +9 - 0.608score
scienceclaw-benchmark-for33
beita6969
Use when forecasting day-ahead hourly electricity load of individual buildings (residential and commercial smart-meter data, BuildingsBench-style) from the…
SkillAFreshness: A★ 1KClaude Code +9 - 0.608score
scienceclaw-benchmark-for32
beita6969
Use when segmenting the hippocampus into anterior and posterior parts in small 3-D T1-weighted brain MRI crops (Medical Segmentation Decathlon Task04) from a…
SkillAFreshness: A★ 1KClaude Code +9 - 0.608score
scienceclaw-benchmark-for35
beita6969
Use when forecasting monthly tourism, visitor or other seasonal business and service series (Monash Tourism Monthly, 24-month horizon, period 12) from each…
SkillAFreshness: A★ 1KClaude Code +9 - 0.608score
scienceclaw-benchmark
beita6969
Index of the 23 discipline skills (FoR30-FoR52) that mirror the disciplines of the companion ScienceClaw-Eval benchmark: find which discipline skill fits a…
SkillAFreshness: A★ 1KClaude Code +9 - 0.608score
scienceclaw-benchmark-for30
beita6969
Use when segmenting top-down field or UAV RGB images of crops (sugar beet, PhenoBench-style) into soil / crop / weed semantics plus crop-plant and crop-leaf…
SkillAFreshness: A★ 1KClaude Code +9 - 0.608score
scienceclaw-benchmark-for31
beita6969
Use when ranking or predicting the fitness effect of single amino-acid substitutions in a protein (deep mutational scanning, ProteinGym-style assays) from…
SkillAFreshness: A★ 1KClaude Code +9 - 0.608score
scienceclaw-benchmark-for46
beita6969
Use when a task gives Python programming problems (a function stub with docstring or a task description, an entry-point name, a few public assert tests) and…
SkillAFreshness: A★ 1KClaude Code +9
Signals
- Stars
- 1,019
- Forks
- 109
- Open issues
- 1
- Last commit (this folder)
- Mar 12, 2026 (7 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- Repository
- github.com/beita6969/ScienceClaw
- Website
- scienceclaw.science
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
Same skill elsewhere
Copies of this SKILL.md published by others; search shows only one of them.
- 0.363score
systematic-debugging
chinkan
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes
SkillAFreshness: A★ 10Claude Code +9 - 0.339score
systematic-debugging
CraftOS-dev
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes
SkillAFreshness: A★ 400Claude Code +9 - 0.337score
systematic-debugging
IvanSkainet
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes
SkillBFreshness: B★ 9Claude Code +9 - 0.310score
systematic-debugging
mihazs
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes
SkillCFreshness: C★ 11Claude Code +9 - 0.307score
systematic-debugging
anubhavg-icpl
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes
SkillCFreshness: C★ 11Claude Code +9 - 0.283score
systematic-debugging
roman01la
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes
SkillDFreshness: D★ 47Claude Code +9 - 0.280score
systematic-debugging
KingYeon-Zoo
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes
SkillCFreshness: C★ 1Claude Code +9 - 0.277score
systematic-debugging
idchain-world
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes
SkillDFreshness: D★ 18Claude Code +9 - 0.277score
systematic-debugging
idchain-world
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes
SkillDFreshness: D★ 18Claude Code +9 - 0.269score
systematic-debugging
kyr0
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes
SkillCFreshness: CClaude Code +9 - 0.268score
systematic-debugging
zhaoxinyi02
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes
SkillDFreshness: D★ 9Claude Code +9 - 0.266score
systematic-debugging
mrtimberme-bot
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes
SkillCFreshness: CClaude Code +9
More from beita6969
- 0.608score
scienceclaw-benchmark-for33
beita6969
Use when forecasting day-ahead hourly electricity load of individual buildings (residential and commercial smart-meter data, BuildingsBench-style) from the…
SkillAFreshness: A★ 1KClaude Code +9 - 0.608score
scienceclaw-benchmark-for34
beita6969
Use when predicting a binary molecular property such as HIV replication inhibition (OGB ogbg-molhiv, MoleculeNet-style activity) from SMILES strings and…
SkillAFreshness: A★ 1KClaude Code +9 - 0.608score
scienceclaw-benchmark-for30
beita6969
Use when segmenting top-down field or UAV RGB images of crops (sugar beet, PhenoBench-style) into soil / crop / weed semantics plus crop-plant and crop-leaf…
SkillAFreshness: A★ 1KClaude Code +9 - 0.608score
scienceclaw-benchmark-for32
beita6969
Use when segmenting the hippocampus into anterior and posterior parts in small 3-D T1-weighted brain MRI crops (Medical Segmentation Decathlon Task04) from a…
SkillAFreshness: A★ 1KClaude Code +9 - 0.608score
scienceclaw-benchmark
beita6969
Index of the 23 discipline skills (FoR30-FoR52) that mirror the disciplines of the companion ScienceClaw-Eval benchmark: find which discipline skill fits a…
SkillAFreshness: A★ 1KClaude Code +9 - 0.608score
scienceclaw-benchmark-for35
beita6969
Use when forecasting monthly tourism, visitor or other seasonal business and service series (Monash Tourism Monthly, 24-month horizon, period 12) from each…
SkillAFreshness: A★ 1KClaude Code +9
Done for you
Rather not set it up yourself?
Chalk Labs scopes, builds and runs agents for your team: the task audit, the eval harness, the integrations and the weekly ops report. Fixed scope, priced before we start.
See how we build AI agents →Read the case studies
Chalk Labs is a two-founder AI and growth studio. We build AI agents, CRMs and MVPs for Web3 and AI companies, and have shipped 50+ product and token launches.
- launches shipped
- 50+
- launches shipped
- media placements
- 500+
- media placements
- founders on every account
- 2
- founders on every account
Press we've placed: Forbes · CoinDesk · Decrypt · Cointelegraph · The Block
Rahil Jain, Co-founder, Chalk Labs. Ex-Goldman Sachs engineer. You talk to the person who builds it.
Get systematic-debugging running in your business
Tell us what you want to automate. A founder replies within one working day with a plan, not a pitch deck.
Cite this page
systematic-debugging skill. Agent Mart. https://agentmart.dev/skills/beita6969-systematic-debugging (last updated 2026-10-24)