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 skills/changoo89/claude-pilot/vibe-codingnpx skills add changoo89/claude-pilot --skill vibe-codinggit clone --depth 1 https://github.com/changoo89/claude-pilotWrote 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/changoo89/claude-pilot/vibe-coding)<a href="https://agentmods.dev/skills/changoo89/claude-pilot/vibe-coding"><img src="https://agentmods.dev/badge/skills/changoo89/claude-pilot/vibe-coding.svg" alt="Measured on agentmods" 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 | $0.00038 | $0.00432 |
| Opus 5 | $0.00019 | $0.00216 |
| Sonnet 5 | $0.00008 | $0.00086 |
| Haiku 4.5 | $0.00004 | $0.00043 |
Grade A, and why
vibe-coding 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 5d 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.
What it actually says
SKILL: Vibe Coding (Code Quality Standards)
Purpose: Enforce LLM-readable code standards for maintainable code Target: Coder Agent during Green/Refactor phases
Quick Reference
| Target | Limit | Action |
|---|---|---|
| Function | ≤50 lines | Split functions |
| File | ≤200 lines | Extract modules |
| Nesting | ≤3 levels | Early return |
Principles
- SRP: One function = one responsibility
- DRY: No duplicate code blocks, extract common logic
- KISS: Simplest solution that works, avoid over-engineering
- Early Return: Return early to reduce nesting, keep happy path at top
- Self-Documenting Code: Names reveal intent, no unnecessary comments (see REFERENCE.md)
- Single Abstraction Level: Functions operate at one conceptual level (see REFERENCE.md)
- Principle of Least Surprise: Predictable behavior, no hidden side effects (see REFERENCE.md)
AI Rules
Enforce during ALL code generation:
- Functions ≤50 lines, Files ≤200 lines, Nesting ≤3 levels
- SRP, DRY, KISS, Early Return pattern
Further Reading
Internal: @.claude/skills/vibe-coding/REFERENCE.md - SOLID principles, refactoring patterns | @.claude/skills/tdd/SKILL.md - Red-Green-Refactor
External: Clean Code | Refactoring
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.
- 5d ago First seen · 40 lines · 38 tokens per session scan A b8413d8e1213
vibe-coding is a skill published in the GitHub repository changoo89/claude-pilot (20 stars, last pushed 6mo ago), licensed MIT. It adds 38 tokens to every session and 432 once invoked, about $0.0002 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 2026-08-30.
Other skills, from other repositories
ai-ml-development
AI and machine learning development with PyTorch, TensorFlow, and LLM integration. Use when building ML models, training pipelines, fine-tuning LLMs, or implementing AI features.
ai-policy-generator
AI governance policy creation for nonprofits and enterprises with frameworks, risk assessment, ethical guidelines, and compliance templates. Use when drafting AI usage policies, responsible AI frameworks, or organizational AI governance documents.
data-science
Data science and analytics expertise for statistical analysis, machine learning pipelines, data governance, business intelligence, predictive modeling, and analytics strategy. Use when building ML models, analyzing data, creating dashboards, or designing data architectures.
data-engineering
ETL/ELT pipelines, data warehousing (BigQuery, Snowflake, Redshift), stream processing (Kafka, Spark Streaming), orchestration (Airflow, Dagster, Prefect), dbt transformations, and data lake architecture. Use when building data pipelines, designing warehouse schemas, or implementing real-time data processing.
data-pipeline
ETL/ELT pipeline design. Trigger when the user wants to create data flows, transformations, or orchestration.
evaluating-llms-harness
Evaluates LLMs across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag). Use when benchmarking model quality, comparing models, reporting academic results, or tracking training progress. Industry standard used by EleutherAI, HuggingFace, and major labs. Supports HuggingFace, vLLM, APIs.