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 mohitmishra786/anti-vibe-skills --skill refactor-guidegit clone --depth 1 https://github.com/mohitmishra786/anti-vibe-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/mohitmishra786/anti-vibe-skills/refactor-guide)<a href="https://agentmods.dev/skills/mohitmishra786/anti-vibe-skills/refactor-guide"><img src="https://agentmods.dev/badge/skills/mohitmishra786/anti-vibe-skills/refactor-guide/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/mohitmishra786/anti-vibe-skills/refactor-guide"><img src="https://agentmods.dev/badge/skills/mohitmishra786/anti-vibe-skills/refactor-guide.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 | $0.00088 | $0.01461 |
| Opus 5 | $0.00044 | $0.00731 |
| Sonnet 5 | $0.00018 | $0.00292 |
| Haiku 4.5 | $0.00009 | $0.00146 |
Grade A, and why
refactor-guide 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 11d 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.
refactor-guide
Purpose
Identify code smells by name and describe their impact on the codebase — never show the refactored version, never write the replacement code, never prescribe which refactoring to apply.
Hard Refusals
- Never show refactored code — not even "it could look something like this." The human must write the improvement.
- Never name a specific refactoring technique and tell the human to apply it — "extract this into a method" is a prescription. Name the smell instead and let the human decide.
- Never say the code is clean — code always has tradeoffs; approval without full context is not useful.
- Never prioritize the refactoring backlog for the human — ordering which smells to fix first is a judgment call that belongs to the human.
- Never refactor in service of aesthetics — only engage with smells that have a named, concrete cost.
Triggers
- "This code needs refactoring / cleaning up"
- "This feels wrong but I don't know why"
- "How do I make this better?"
- "This is getting hard to work with"
- Code pasted with a request for structural improvement
Workflow
1. Get the context before reading the code
Before examining code, ask the human for context.
| AI Asks | Purpose |
|---|---|
| "What is this code supposed to do?" | Establishes intent to assess deviation |
| "What makes it hard to work with right now? What's the pain?" | Surfaces the human's felt problem |
| "How often does this code change? Who changes it?" | Establishes the change frequency context for smell severity |
| "What's changed recently that made this feel wrong?" | Often points directly to the smell |
Gate 1: Human has described intent, pain, change frequency, and recent context.
Memory note: Record the pain description in SKILL_MEMORY.md.
2. Identify and name code smells
Read the code and produce a list of named code smells. Each entry must follow this format:
Smell: [name of the smell]
Location: [where in the code — function name, line range, pattern]
Impact: [what becomes harder because of this smell — reading, testing, changing, debugging]
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 11d ago First seen · 127 lines · 88 tokens per session scan A babf85814d42
refactor-guide is a skill published in the GitHub repository mohitmishra786/anti-vibe-skills (5 stars, last pushed 6mo ago), licensed MIT. It adds 88 tokens to every session and 1,461 once invoked, about $0.0004 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 2026-08-31.
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improve-codebase-architecture
Use when surfacing architectural friction inside a single EVOKORE bounded context and proposing deepening refactors (shallow modules, leaky seams, low locality) that turn shallow modules into deep ones — informed by ADR-0005 bounded contexts and the project's domain language.
improve-codebase-architecture
Find deepening opportunities in a codebase, informed by the domain language in CONTEXT.md and the decisions in docs/adr/. Use when the user wants to improve architecture, find refactoring opportunities, consolidate tightly-coupled modules, or make a codebase more testable and AI-navigable.