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 LuuOW/meridian-mcp --skill env-managementgit clone --depth 1 https://github.com/LuuOW/meridian-mcpWrote 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/luuow/meridian-mcp/env-management)<a href="https://agentmods.dev/skills/luuow/meridian-mcp/env-management"><img src="https://agentmods.dev/badge/skills/luuow/meridian-mcp/env-management/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/luuow/meridian-mcp/env-management"><img src="https://agentmods.dev/badge/skills/luuow/meridian-mcp/env-management.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 | $0.00061 | $0.02254 |
| Opus 5 | $0.00030 | $0.01127 |
| Sonnet 5 | $0.00012 | $0.00451 |
| Haiku 4.5 | $0.00006 | $0.00225 |
Grade A, and why
env-management 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 9d 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 — 291 lines — stays where its author put it; the contents beside it link to each section on GitHub.
env-management
Patterns for managing environment variables across Python/FastAPI backends and TypeScript/Next.js or Vite frontends. Synthesized from lead-gen-engine (python-dotenv + manual validation) and seo-geo-aeo-engine (Pydantic BaseSettings + LRU cache).
1) Python: Pydantic BaseSettings (preferred — seo-geo-aeo pattern)
# core/config.py
from functools import lru_cache
from pydantic_settings import BaseSettings, SettingsConfigDict
class Settings(BaseSettings):
model_config = SettingsConfigDict(
env_file=".env",
env_file_encoding="utf-8",
case_sensitive=False,
extra="ignore",
)
# Database
database_url: str
postgres_user: str = "app"
postgres_password: str = ""
# Redis
redis_url: str = "redis://localhost:6379/0"
redis_password: str = ""
# LLM providers — all optional (feature-flagged)
openai_api_key: str = ""
anthropic_api_key: str = ""
moonshot_api_key: str = ""
gemini_api_key: str = ""
# Generic OpenAI-compatible provider (overrides specific providers when set)
use_generic_llm: bool = False
generic_llm_base_url: str = ""
generic_llm_api_key: str = ""
generic_llm_model: str = "gpt-4o"
# External APIs
serp_api_key: str = ""
firecrawl_api_key: str = ""
# App
environment: str = "development"
log_level: str = "INFO"
debug: bool = False
# Auth
jwt_secret: str = "change-me-in-production"
dashboard_username: str = "admin"
dashboard_password: str = ""
@property
def is_production(self) -> bool:
return self.environment == "production"
@lru_cache
def get_settings() -> Settings:
return Settings()
Usage everywhere:
from core.config import get_settings
def my_func():
s = get_settings()
# s.database_url, s.openai_api_key, etc.
2) Python: python-dotenv + Manual Validation (lead-gen pattern)
# shared/cli.py
from pathlib import Path
from dotenv import load_dotenv
def get_project_root() -> Path:
return Path(__file__).parent.parent
def load_env() -> None:
env_path = get_project_root() / ".env"
if env_path.exists():
load_dotenv(env_path)
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.
- 9d ago First seen · 291 lines · 61 tokens per session scan A 59f51148ac9f
env-management is a skill published in the GitHub repository LuuOW/meridian-mcp (0 stars, last pushed today), licensed MIT. It adds 61 tokens to every session and 2,254 once invoked, about $0.0003 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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