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 redhat-community-ai-tools/UnifAI --skill celerygit clone --depth 1 https://github.com/redhat-community-ai-tools/UnifAIWrote 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/redhat-community-ai-tools/unifai/celery)<a href="https://agentmods.dev/skills/redhat-community-ai-tools/unifai/celery"><img src="https://agentmods.dev/badge/skills/redhat-community-ai-tools/unifai/celery/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/redhat-community-ai-tools/unifai/celery"><img src="https://agentmods.dev/badge/skills/redhat-community-ai-tools/unifai/celery.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00038 | $0.01080 |
| Opus 5.5 | $0.00015 | $0.00432 |
| Sonnet 5.5 | $0.00008 | $0.00216 |
| Haiku 4.5 | $0.00004 | $0.00108 |
Grade A, and why
celery-knowledge 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 1mo 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 — 133 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Celery Worker Knowledge System
Async task execution service for the RAG pipeline. Shares the rag/ codebase —
Celery tasks are thin wrappers that call RAG core services.
For domain logic, load the RAG domain skill (../rag/SKILL.md).
Role
- Executes document processing pipeline stages asynchronously
- Scales horizontally via additional worker instances
- Handles task retry and failure recovery
- Keeps the RAG API responsive by offloading heavy work
Entry Point
rag/entrypoint.sh with ROLE=celery runs:
celery -A infrastructure.celery.app worker -Q $CELERY_QUEUES
Three Queues
| Queue | Purpose | Tasks |
|---|---|---|
document_queue |
Document ingestion pipelines | execute_pipeline_task |
slack_queue |
Slack channel ingestion pipelines | execute_pipeline_task |
slack_events_queue |
Real-time Slack event processing | process_slack_events_task (3 retries) |
Key Files
| File | Role |
|---|---|
rag/infrastructure/celery/app.py |
Celery app configuration |
rag/infrastructure/celery/workers/pipeline_tasks.py |
execute_pipeline_task entry point |
rag/infrastructure/celery/workers/slack_event_tasks.py |
process_slack_events_task entry point |
rag/infrastructure/celery/pipeline_dispatcher.py |
Routes tasks to queues |
global_utils/celery_app/init.py |
CeleryApp singleton factory |
Task Execution Flow
CeleryPipelineDispatcher.dispatch(source_type, source_data)
→ derive queue from source_type (e.g. "document" → document_queue)
→ send_task() → RabbitMQ → Celery Worker picks up task
→ execute_pipeline_task()
→ resolve dependencies from app container
→ select handler (DocumentPipelineHandler / SlackPipelineHandler)
→ PipelineExecutor.execute(handler, source)
→ collect → process → chunk → embed → store
Worker Pool Configuration
threadspool when using remote Docling/embedding (I/O-bound)solopool for local processing (CPU-bound)- Controlled by config flags:
use_remote_docling,use_remote_embedding
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.
- 1mo ago First seen · 133 lines · 38 tokens per session scan A c72548383d0f
celery-knowledge is a skill published in the GitHub repository redhat-community-ai-tools/UnifAI (45 stars, last pushed yesterday), licensed Apache-2.0. It adds 38 tokens to every session and 1,080 once invoked, about $0.0002 per session on Opus 5.5. 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.
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