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 background-tasksgit 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/background-tasks)<a href="https://agentmods.dev/skills/luuow/meridian-mcp/background-tasks"><img src="https://agentmods.dev/badge/skills/luuow/meridian-mcp/background-tasks/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/background-tasks"><img src="https://agentmods.dev/badge/skills/luuow/meridian-mcp/background-tasks.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 | $0.00051 | $0.01619 |
| Opus 5 | $0.00026 | $0.00809 |
| Sonnet 5 | $0.00010 | $0.00324 |
| Haiku 4.5 | $0.00005 | $0.00162 |
Grade A, and why
background-tasks 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 8d 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 — 219 lines — stays where its author put it; the contents beside it link to each section on GitHub.
background-tasks
Covers async task execution: Celery + beat for scheduled/queued work, APScheduler for in-process cron, and bare Redis queues for lightweight pipelines.
1) Celery setup (Python)
# celery_app.py
from celery import Celery
celery = Celery(
"tasks",
broker="redis://localhost:6379/1", # DB 1 for queues (never evicted)
backend="redis://localhost:6379/2", # DB 2 for results
include=["app.tasks"],
)
celery.conf.update(
task_serializer="json",
result_expires=3600,
timezone="UTC",
enable_utc=True,
worker_prefetch_multiplier=1, # process one task at a time (prevents memory spike)
task_acks_late=True, # ack after success, not on receive (safe retry on crash)
)
2) Defining tasks
from celery_app import celery
@celery.task(bind=True, max_retries=3, default_retry_delay=60)
def generate_article(self, domain: str, slug: str) -> dict:
try:
result = run_pipeline(domain, slug)
return result
except TemporaryError as exc:
raise self.retry(exc=exc) # exponential back-off via default_retry_delay
except PermanentError:
# Don't retry — log and fail cleanly
logger.error("permanent_failure", domain=domain, slug=slug)
return {"status": "failed"}
# Enqueue
task = generate_article.delay(domain="keto", slug="keto-diet-guide")
print(task.id) # track this ID
3) Celery Beat (scheduled tasks)
from celery.schedules import crontab
celery.conf.beat_schedule = {
"serp-delta-check": {
"task": "app.tasks.run_serp_delta",
"schedule": crontab(hour="*/6"), # every 6h
},
"link-score-refresh": {
"task": "app.tasks.refresh_link_scores",
"schedule": crontab(hour=3, minute=0), # 3am UTC
},
}
4) Docker Compose: worker + beat
services:
worker-default:
build: .
command: celery -A celery_app worker -Q default -c 2 --loglevel=info
environment:
- REDIS_URL=redis://redis:6379/1
depends_on: [redis]
restart: unless-stopped
worker-scraping:
build: .
command: celery -A celery_app worker -Q scraping -c 1 --loglevel=info
depends_on: [redis]
restart: unless-stopped
beat:
build: .
command: celery -A celery_app beat --loglevel=info --scheduler celery.beat.PersistentScheduler
depends_on: [redis]
restart: unless-stopped
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.
- 8d ago First seen · 219 lines · 51 tokens per session scan A 9cd03718426c
background-tasks is a skill published in the GitHub repository LuuOW/meridian-mcp (0 stars, last pushed 4d ago), licensed MIT. It adds 51 tokens to every session and 1,619 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.
Other skills, from other repositories
sqlalchemy-models
Create or modify SQLAlchemy models, queries, and Alembic migrations. Use when: defining new database tables, writing queries, creating migrations, checking model conventions, or understanding the database layer.
alembic-migration
Create, review, and apply database schema changes with Alembic. Use whenever a SQLAlchemy model is added or changed, a column/index/constraint needs to change, or a data backfill is required — anything that alters the PostgreSQL schema.
asyncio
Python asyncio - Modern concurrent programming with async/await, event loops, tasks, coroutines, primitives, aiohttp, and FastAPI async patterns.
aiocache
Configure or use the aiocache caching layer. Use when: adding cache reads/writes, configuring cache backends, working with TTLs, enabling/disabling caching, or understanding the NoOpCache fallback pattern.
fastapi-routes
Create or modify FastAPI routes. Use when: adding new API endpoints, creating Pydantic request/response models, registering routers, designing REST APIs, or following route conventions for this project.
typer-cli
Add or modify CLI commands using Typer. Use when: adding new CLI subcommands, wrapping async functions for CLI use, understanding the CLI entrypoint structure, or following the @syncify pattern for async commands.