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 berkcangumusisik/claude-code-practices --skill env-checkgit clone --depth 1 https://github.com/berkcangumusisik/claude-code-practicesWrote 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/berkcangumusisik/claude-code-practices/env-check)<a href="https://agentmods.dev/skills/berkcangumusisik/claude-code-practices/env-check"><img src="https://agentmods.dev/badge/skills/berkcangumusisik/claude-code-practices/env-check.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 5.1 | $0.00013 | $0.00272 |
| Opus 5 | $0.00006 | $0.00136 |
| Sonnet 5 | $0.00003 | $0.00054 |
| Haiku 4.5 | $0.00001 | $0.00027 |
Grade A, and why
env-check scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Environment Variable Check
- Read requirements:
cat .env.example 2>/dev/null || cat .env.template 2>/dev/null
- Check which are set in the current environment:
while IFS='=' read -r key _; do
[[ "$key" =~ ^#|^$ ]] && continue
if [[ -z "${!key}" ]]; then
echo "MISSING: $key"
else
echo " SET: $key"
fi
done < .env.example
-
Additional checks:
- DATABASE_URL: can it actually connect?
- API keys: correct format (prefix check)?
- URLs: valid format?
-
Output:
Environment Check:
✅ DATABASE_URL
✅ JWT_SECRET
❌ STRIPE_SECRET_KEY ← MISSING (required for payments)
⚠️ REDIS_URL ← Missing (optional, caching disabled)
Result: 1 required var missing - app will fail to start.
- Suggest how to obtain missing variables.
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.
- 6d ago First seen · 45 lines · 13 tokens per session scan A 3221181adc63
env-check is a skill published in the GitHub repository berkcangumusisik/claude-code-practices (10 stars, last pushed 4mo ago), licensed MIT. It adds 13 tokens to every session and 272 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 2026-08-31.
Other skills, from other repositories
open-source
Documentation reference for writing Python code using the browser-use open-source library. Use this skill whenever the user needs help with Agent, Browser, or Tools configuration, is writing code that imports from browseruse, asks about @sandbox deployment, supported LLM models, Actor API, custom tools, lifecycle…
skill-builder
Automatically detect source types and build AI skills using Skill Seekers. Use when the user wants to create skills from documentation, repos, PDFs, videos, or other knowledge sources.
prompt-master
Generates optimized prompts for AI tools. Activates only when the user explicitly asks to write, fix, improve, or adapt a prompt for a specific AI tool (LLM, Cursor, Midjourney, image AI, video AI, coding agents, etc.). Does not activate for general conversation, coding tasks, document writing, or other…
snip
You are an expert at writing declarative YAML filters for snip, a CLI proxy that reduces LLM token consumption by filtering shell output.
goai
GoAI is a Go SDK for AI applications. One unified API across 25+ LLM providers. Inspired by the Vercel AI SDK, adapted to Go idioms (generics, interfaces, channels).
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.