Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add giggsoinc/raven/plugin install ravenWrote 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/giggsoinc/raven/workflow-specialist)<a href="https://agentmods.dev/skills/giggsoinc/raven/workflow-specialist"><img src="https://agentmods.dev/badge/skills/giggsoinc/raven/workflow-specialist/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/giggsoinc/raven/workflow-specialist"><img src="https://agentmods.dev/badge/skills/giggsoinc/raven/workflow-specialist.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 | $0.01554 |
| Opus 5.5 | $0.00020 | $0.00622 |
| Sonnet 5.5 | $0.00010 | $0.00311 |
| Haiku 4.5 | $0.00005 | $0.00155 |
Grade A, and why
workflow-specialist 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 — 142 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Workflow Orchestration Specialist — Ben Stopford (Distributed systems engineer)
Assumed Expert
Ben Stopford (Distributed systems engineer) Explaining as a senior engineer teaching someone who knows backend services but is new to workflow orchestration.
Core Focus
Workflow engines, deterministic flows, DAG pipelines, durable execution, event-driven orchestration, state machines
Sub-Modes
Event-Driven
- N8N: Visual workflow builder, self-hosted, 400+ integrations
- Webhook triggers, conditional branching, error handling nodes
- Best for: business automation, API orchestration, no-code/low-code teams
- Limitations: single-threaded execution per workflow, no native distributed mode
- Docker:
n8nio/n8n, port 5678, volume for/home/node/.n8n
- AWS Step Functions: Serverless state machine, pay-per-transition
- ASL (Amazon States Language) — JSON state machine definition
- Standard (exactly-once, up to 1yr) vs Express (at-least-once, up to 5min)
- Best for: AWS-native, microservice coordination, human approval flows
- Gotcha: 256KB payload limit, state transitions cost $0.025/1000
- Azure Logic Apps / Durable Functions: Event-driven orchestration in Azure
- Fan-out/fan-in, human interaction, monitoring patterns
- Best for: Azure-native, enterprise integration
DAG-Based
- Airflow: The standard for batch data pipelines
- DAGs in Python, XCom for task data passing, connection/hook model
- Executors: Local, Celery, Kubernetes, CeleryKubernetes
- Best for: scheduled batch ETL, data engineering, ML training pipelines
- Gotcha: not for real-time, DAG parsing overhead at scale, XCom size limits
- Docker-compose: webserver + scheduler + worker + postgres + redis
- Dagster: Software-defined assets, type-checked, testable
- Assets > Tasks mental model — declare what exists, not what to do
- Best for: data teams wanting testability and lineage
- Gotcha: smaller ecosystem than Airflow, steeper learning curve
- Prefect: Python-native, dynamic workflows, hybrid execution
- Flows and tasks with retries, caching, concurrency limits
- Best for: ML pipelines with dynamic branching, Python-heavy teams
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 · 142 lines · 51 tokens per session scan A 640fb2c210a7
workflow-specialist is a skill published in the GitHub repository giggsoinc/raven (5 stars, last pushed 13d ago), licensed MIT. It adds 51 tokens to every session and 1,554 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 2026-09-03.
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