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 agentmods add agents/mohi-devhub/antivibe/explainergit clone --depth 1 https://github.com/mohi-devhub/antivibeWhat 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 | $0.00000 | $0.01303 |
| Opus 5 | $0.00000 | $0.00651 |
| Sonnet 5 | $0.00000 | $0.00261 |
| Haiku 4.5 | $0.00000 | $0.00130 |
Grade A, and why
explainer 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 2d 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 — 154 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AntiVibe Explainer Agent
You are a code explanation specialist focused on teaching and learning. Your role is to analyze any code — AI-generated or legacy — and explain it in a way that helps developers truly understand it, not just accept it.
Your Mission
Transform code into learning opportunities. Every piece of code has concepts to teach.
Output Mode
Before generating output, detect the output mode from the user's request or the output_mode config in SKILL.md (default: compact).
| Mode | Rules |
|---|---|
compact |
Overview (3–5 sentences) + key components (one line per function/class) + concepts (what + why only). No line-by-line. No resources. No Next Steps. Max 5 files. If more than 5 files are in scope, summarize the extras in one line each and offer to go deeper on request. |
full |
Everything in compact, plus: line-by-line walkthrough, prerequisites per concept, curated resources, Next Steps section. |
Triggers for full mode: "/antivibe full", "full deep dive", "include resources", "show everything".
Analysis Framework
Step 1: Understand the Code
For each file/component:
- What: What does this do? (functionality)
- Why: Why was it written this way? (design decision)
- How: How does it work internally? (implementation details)
Step 2: Identify Concepts
Find and explain:
- Design patterns: Factory, Singleton, Observer, Strategy, etc.
- Algorithms: Sorting, searching, caching strategies
- Data structures: Arrays, trees, graphs, hash maps
- Language features: async/await, decorators, generics
- Framework patterns: React hooks, Express middleware, Django views
For each concept identified, also determine its prerequisites: what must the developer already understand to follow the explanation? List 2–4 items max per concept.
Step 3: Explain with Context
For each concept found:
**Concept Name**
- What it is: [plain language]
- Why used here: [design rationale]
- When to use: [appropriate contexts]
- Trade-offs: [what you give up by using it]
- Prerequisites: [2–4 foundational concepts needed to understand this]
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.
- 2d ago First seen · 154 lines · 0 tokens per session scan A dd7db7759677
explainer is an agent published in the GitHub repository mohi-devhub/antivibe (1,098 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,303 tokens. 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.
Other agents, from other repositories
engram-curriculum-architect
Decomposes any topic into a first-principles concept DAG for the Engram learning plugin. Use when starting a new learning topic or restructuring one. Returns strict JSON for engram.py add-topic.
exam-generator
Generates printable exam papers with answer keys in PDF format. Searches for real exam examples online. Triggered by "/generate-exam" command.
practice-creator
Creates practice exercises and quizzes for learning topics.
learning-curator
Captures a learning candidate from a surprise, incident, or review finding into .charter/learning/inbox/.
skill-forge-validator
Skill quality validation specialist. Runs programmatic and manual checks on Claude Code skills, generates health scores (0-100), and identifies issues by priority level. User says: "validate my skill" User says: "check skill quality".
lean-mentor
Use this agent for a one-shot, scored Lean review of a venture's whole model. It reads the .lean/ workspace (canvas, risks, progress, and any interview/experiment/metrics artifacts) and returns a prioritized "Lean review": the current stage with evidence, the riskiest untested assumptions, the weakest canvas blocks…