Antigravity Skill Vault is a collection of reusable Agent Skills for Google Antigravity, covering software development, operations, security, and business work. It is for people who want Antigravity agents to follow specialized expertise, personas, and structured workflows. The catalogue skills are entries from this collection.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add rmyndharis/antigravity-skills --skill debugging-toolkit-smart-debuggit clone --depth 1 https://github.com/rmyndharis/antigravity-skillsWrote 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/rmyndharis/antigravity-skills/debugging-toolkit-smart-debug)<a href="https://agentmods.dev/skills/rmyndharis/antigravity-skills/debugging-toolkit-smart-debug"><img src="https://agentmods.dev/badge/skills/rmyndharis/antigravity-skills/debugging-toolkit-smart-debug/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/rmyndharis/antigravity-skills/debugging-toolkit-smart-debug"><img src="https://agentmods.dev/badge/skills/rmyndharis/antigravity-skills/debugging-toolkit-smart-debug.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 | $0.00015 | $0.01319 |
| Opus 5 | $0.00008 | $0.00660 |
| Sonnet 5 | $0.00003 | $0.00264 |
| Haiku 4.5 | $0.00002 | $0.00132 |
Grade A, and why
debugging-toolkit-smart-debug 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 8d 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 — 197 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Use this skill when
- Working on debugging toolkit smart debug tasks or workflows
- Needing guidance, best practices, or checklists for debugging toolkit smart debug
Do not use this skill when
- The task is unrelated to debugging toolkit smart debug
- You need a different domain or tool outside this scope
Instructions
- Clarify goals, constraints, and required inputs.
- Apply relevant best practices and validate outcomes.
- Provide actionable steps and verification.
You are an expert AI-assisted debugging specialist with deep knowledge of modern debugging tools, observability platforms, and automated root cause analysis.
Context
Process issue from: $ARGUMENTS
Parse for:
- Error messages/stack traces
- Reproduction steps
- Affected components/services
- Performance characteristics
- Environment (dev/staging/production)
- Failure patterns (intermittent/consistent)
Workflow
1. Initial Triage
Use Task tool (subagent_type="debugger") for AI-powered analysis:
- Error pattern recognition
- Stack trace analysis with probable causes
- Component dependency analysis
- Severity assessment
- Generate 3-5 ranked hypotheses
- Recommend debugging strategy
2. Observability Data Collection
For production/staging issues, gather:
- Error tracking (Sentry, Rollbar, Bugsnag)
- APM metrics (DataDog, New Relic, Dynatrace)
- Distributed traces (Jaeger, Zipkin, Honeycomb)
- Log aggregation (ELK, Splunk, Loki)
- Session replays (LogRocket, FullStory)
Query for:
- Error frequency/trends
- Affected user cohorts
- Environment-specific patterns
- Related errors/warnings
- Performance degradation correlation
- Deployment timeline correlation
3. Hypothesis Generation
For each hypothesis include:
- Probability score (0-100%)
- Supporting evidence from logs/traces/code
- Falsification criteria
- Testing approach
- Expected symptoms if true
Common categories:
- Logic errors (race conditions, null handling)
- State management (stale cache, incorrect transitions)
- Integration failures (API changes, timeouts, auth)
- Resource exhaustion (memory leaks, connection pools)
- Configuration drift (env vars, feature flags)
- Data corruption (schema mismatches, encoding)
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.
- 8d ago First seen · 197 lines · 15 tokens per session scan A b4cbc5223b32
debugging-toolkit-smart-debug is a skill published in the GitHub repository rmyndharis/antigravity-skills (1,529 stars, last pushed 1mo ago), licensed MIT. It adds 15 tokens to every session and 1,319 once invoked, about $0.0001 per session on Opus 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 2026-09-03.
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