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 Ertinox7711/SGRR-AGI-V2 --skill antivibegit clone --depth 1 https://github.com/Ertinox7711/SGRR-AGI-V2Wrote 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/ertinox7711/sgrr-agi-v2/antivibe)<a href="https://agentmods.dev/skills/ertinox7711/sgrr-agi-v2/antivibe"><img src="https://agentmods.dev/badge/skills/ertinox7711/sgrr-agi-v2/antivibe/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/ertinox7711/sgrr-agi-v2/antivibe"><img src="https://agentmods.dev/badge/skills/ertinox7711/sgrr-agi-v2/antivibe.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 | $0.00048 | $0.01042 |
| Opus 5.5 | $0.00019 | $0.00417 |
| Sonnet 5.5 | $0.00010 | $0.00208 |
| Haiku 4.5 | $0.00005 | $0.00104 |
Grade A, and why
antivibe 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 — 127 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AntiVibe - AI Code Learning Framework
Purpose
AntiVibe generates learning-focused explanations of AI-written code. Not generic summaries - actual educational content that helps developers understand:
- What the code does (functionality)
- Why it was written this way (design decisions)
- When to use these patterns (context)
- What alternatives exist (broader knowledge)
When to Use
Use AntiVibe when:
- Manual invocation: User types
/antivibeor "deep dive" - Post-task learning: After a feature/phase completes, user wants to learn from it
- Proactive: User says "explain what AI wrote", "learn from this code", or "understand what AI wrote"
What AntiVibe Produces
Output saved to deep-dive/ folder as markdown:
deep-dive/
├── auth-system-2026-01-15.md
├── api-layer-2026-01-15.md
└── database-models-2026-01-15.md
Each file contains:
- Overview: What this code does and why it exists
- Code Walkthrough: File-by-file explanation with line-by-line notes
- Concepts Explained: Design patterns, algorithms, CS concepts used
- Learning Resources: Curated docs, tutorials, videos
- Related Code: Links to other files in the codebase
Workflow
Step 1: Identify Code to Analyze
- Check for explicit file list in user request
- Or use git diff to find recently modified/created files
- Or ask user which files/components they want to understand
Step 2: Analyze Code Structure
For each file:
- Identify main purpose and responsibilities
- Note key functions, classes, modules
- Identify design patterns used (factory, singleton, observer, etc.)
- Find any complex logic or algorithms
Step 3: Explain Concepts
For each concept/pattern found:
- What: Plain-language explanation
- Why: Why this approach was chosen over alternatives
- When: When to use this pattern (with context)
- Alternatives: Other approaches and trade-offs
Step 4: Find External Resources
Search for and include:
- Official documentation for libraries/frameworks used
- Quality tutorials or blog posts
- Video resources (if available)
- Related concepts for further learning
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 · 127 lines · 48 tokens per session scan A d1b5bf84ce25
antivibe is a skill published in the GitHub repository Ertinox7711/SGRR-AGI-V2 (1 stars, last pushed 1mo ago), licensed MIT. It adds 48 tokens to every session and 1,042 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 2026-09-09.
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