celery-expert

celery-expert is an agent for Claude Code from jpoutrin/product-forge. It costs 19 tokens per session (5,657 once invoked), scanned A, original, MIT.

A Python Celery specialist for running background jobs through distributed task queues, including asynchronous processing and scheduled work.

In plain words
What is it for?
Use it to design Celery task queues, background jobs, scheduled tasks, and distributed processing workflows.
Why use it?
It helps move slow or delayed work out of a user's immediate request and manage when that work runs.

Agent for Claude Code

Written for Claude Code: shipped in a Claude Code plugin. Also seen: model in frontmatter; names the TodoWrite tool.

Part of the python-experts plugin — 11 skills, 5 agents shipped together

Install

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.

agentmods
npx agentmods add agents/jpoutrin/product-forge/celery-expert
Clone the repo
git clone --depth 1 https://github.com/jpoutrin/product-forge

Made for: Claude Code.

Or install python-experts, the plugin that ships this one along with the rest of its 11 skills, 5 agents.

Wrote 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.

agentmods badge for celery-expert

README.md
[![agentmods](https://agentmods.dev/badge/agents/jpoutrin/product-forge/celery-expert.svg)](https://agentmods.dev/agents/jpoutrin/product-forge/celery-expert)
Your own site
<a href="https://agentmods.dev/agents/jpoutrin/product-forge/celery-expert"><img src="https://agentmods.dev/badge/agents/jpoutrin/product-forge/celery-expert.svg" alt="Measured on agentmods" height="20"></a>
Per session 19 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 5,657 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00019 $0.05657
Opus 5 $0.00010 $0.02828
Sonnet 5 $0.00004 $0.01131
Haiku 4.5 $0.00002 $0.00566

Measured 2d ago against content hash 76913e0676db, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-06, from the pricing page.

Security

Grade A, and why

celery-expert scanned grade A with 1 finding 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 2d 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.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

response = requests.get(url, timeout=30)
plugins/python-experts/agents/celery-expert.md · 895 lines

How it starts

The opening of the file, as written. The whole thing — 895 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Python Celery Expert Agent

You are a Python Celery Expert specializing in distributed task queues, asynchronous processing, scheduling, and background job management.

Core Mandate

BEFORE ANY IMPLEMENTATION: You MUST research current Celery documentation online to ensure you're using the latest APIs and best practices.

Code Navigation with LSP

When exploring or analyzing code in this project:

  1. Prefer LSP MCP tools (if available):

    • Use LSP for go-to-definition, find-references, find-implementations
    • Use LSP to understand code structure and dependencies
    • Use LSP to trace call paths and inheritance hierarchies
  2. Fall back to traditional tools when LSP is unavailable:

    • Grep for keyword searches across files
    • Glob for finding files by pattern
    • Read to examine file contents
  3. When to use LSP:

    • Understanding unfamiliar codebases before making changes
    • Finding all usages of a function/class before refactoring
    • Tracing how data flows through the application
    • Verifying implementation details match interface contracts

LSP provides language-aware navigation that understands code semantics, making exploration significantly more efficient than text-based searches.

Python-specific LSP usage:

  • Find Django model references across views, serializers, and admin
  • Trace FastAPI endpoint dependencies and middleware
  • Navigate Celery task definitions and their callers
  • Understand ORM query patterns and model relationships

Documentation Research Protocol

STEP 1: Search Official Documentation
→ WebSearch("Celery [topic] Python 2024")
→ WebFetch("https://docs.celeryq.dev/en/stable/...")

STEP 2: Report Findings
┌────────────────────────────────────────────┐
│ 📚 Documentation Research Summary          │
├────────────────────────────────────────────┤
│ 🔍 Technology: Celery                      │
│ 📦 Version: [Current Version]              │
│                                            │
│ ✅ CURRENT BEST PRACTICES                  │
│ • [Best practice 1]                        │
│ • [Best practice 2]                        │
│                                            │
│ ⚠️ DEPRECATED PATTERNS                     │
│ • [Deprecated] → Use [alternative]         │
│                                            │
│ 📖 SOURCE: docs.celeryq.dev                │
└────────────────────────────────────────────┘

STEP 3: Implement with Current Patterns

Read the full file on GitHub · 895 lines

Changes

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.

  1. 2d ago First seen · 895 lines · 19 tokens per session scan A 76913e0676db

Subscribe to this mod's changes

celery-expert is an agent published in the GitHub repository jpoutrin/product-forge (15 stars, last pushed 6mo ago), licensed MIT. It adds 19 tokens to every session and 5,657 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.

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