AG Kit is a toolkit that gives Google Antigravity coding agents a structured workspace with rules, skills, specialist agents, workflows, persistent memory, orchestration, MCP guidance, and a safety hook. It is for building and operating agent workflows in Antigravity, and its catalogue entries provide parts of that workspace contract.
Borrowing it
Nothing to install: this file belongs to vudovn/ag-kit. 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/vudovn/ag-kit/main/.agents/skills/systematic-debugging/SKILL.mdgit clone --depth 1 https://github.com/vudovn/ag-kitWrote 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/vudovn/ag-kit/systematic-debugging)<a href="https://agentmods.dev/skills/vudovn/ag-kit/systematic-debugging"><img src="https://agentmods.dev/badge/skills/vudovn/ag-kit/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/vudovn/ag-kit/systematic-debugging"><img src="https://agentmods.dev/badge/skills/vudovn/ag-kit/systematic-debugging.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00025 | $0.00647 |
| Opus 5.5 | $0.00010 | $0.00259 |
| Sonnet 5.5 | $0.00005 | $0.00129 |
| Haiku 4.5 | $0.00003 | $0.00065 |
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.
Copies of this mod
4 near-identical copies found in the catalogue:
- systematic-debugging — 89% identical, 2 lines differ
- systematic-debugging — 89% identical, 2 lines differ
- systematic-debugging — 89% identical, 2 lines differ
- systematic-debugging — 89% identical, 2 lines differ
How it starts
The opening of the file, as written. The whole thing — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Systematic Debugging
Source: obra/superpowers
Overview
This skill provides a structured approach to debugging that prevents random guessing and ensures problems are properly understood before solving.
4-Phase Debugging Process
Phase 1: Reproduce
Before fixing, reliably reproduce the issue.
## Reproduction Steps
1. [Exact step to reproduce]
2. [Next step]
3. [Expected vs actual result]
## Reproduction Rate
- [ ] Always (100%)
- [ ] Often (50-90%)
- [ ] Sometimes (10-50%)
- [ ] Rare (<10%)
Phase 2: Isolate
Narrow down the source.
## Isolation Questions
- When did this start happening?
- What changed recently?
- Does it happen in all environments?
- Can we reproduce with minimal code?
- What's the smallest change that triggers it?
Phase 3: Understand
Find the root cause, not just symptoms.
## Root Cause Analysis
### The 5 Whys
1. Why: [First observation]
2. Why: [Deeper reason]
3. Why: [Still deeper]
4. Why: [Getting closer]
5. Why: [Root cause]
Phase 4: Fix & Verify
Fix and verify it's truly fixed.
## Fix Verification
- [ ] Bug no longer reproduces
- [ ] Related functionality still works
- [ ] No new issues introduced
- [ ] Test added to prevent regression
Debugging Checklist
## Before Starting
- [ ] Can reproduce consistently
- [ ] Have minimal reproduction case
- [ ] Understand expected behavior
## During Investigation
- [ ] Check recent changes (git log)
- [ ] Check logs for errors
- [ ] Add logging if needed
- [ ] Use debugger/breakpoints
## After Fix
- [ ] Root cause documented
- [ ] Fix verified
- [ ] Regression test added
- [ ] Similar code checked
Common Debugging Commands
# Recent changes
git log --oneline -20
git diff HEAD~5
# Search for pattern
grep -r "errorPattern" --include="*.ts"
# Check logs
pm2 logs app-name --err --lines 100
Anti-Patterns
❌ Random changes - "Maybe if I change this..." ❌ Ignoring evidence - "That can't be the cause" ❌ Assuming - "It must be X" without proof ❌ Not reproducing first - Fixing blindly ❌ Stopping at symptoms - Not finding root cause
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 · 112 lines · 25 tokens per session scan A b41274eb9a63
systematic-debugging is a skill published in the GitHub repository vudovn/ag-kit (8,199 stars, last pushed 3d ago), licensed MIT. It adds 25 tokens to every session and 647 once invoked, about $0.0001 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 2026-08-30.
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