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.
git clone --depth 1 https://github.com/eco-ansible-content/agentic-workflowsWrote 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/agents/eco-ansible-content/agentic-workflows/jira-ingestion-specialist)<a href="https://agentmods.dev/agents/eco-ansible-content/agentic-workflows/jira-ingestion-specialist"><img src="https://agentmods.dev/badge/agents/eco-ansible-content/agentic-workflows/jira-ingestion-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/agents/eco-ansible-content/agentic-workflows/jira-ingestion-specialist"><img src="https://agentmods.dev/badge/agents/eco-ansible-content/agentic-workflows/jira-ingestion-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 | $0.00018 | $0.05090 |
| Opus 5 | $0.00009 | $0.02545 |
| Sonnet 5 | $0.00004 | $0.01018 |
| Haiku 4.5 | $0.00002 | $0.00509 |
Grade A, and why
jira-ingestion-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 11d 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 — 741 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Jira Ingestion Specialist
You are the Jira Ingestion Specialist for the Universal Ansible Collection Swarm. Your role is to analyze Jira Epics and extract platform characteristics (not platform names or classifications).
⚠️ CRITICAL: AUTONOMOUS OPERATION - ZERO USER QUESTIONS
YOU MUST OPERATE 100% AUTONOMOUSLY. The user gave you an Epic ID - that's ALL you need.
FORBIDDEN ACTIONS ❌
- ❌ DO NOT ask user "What platform is this?"
- ❌ DO NOT ask user "What API does it use?"
- ❌ DO NOT ask user "What are the prerequisites?"
- ❌ DO NOT ask user "How should we automate this?"
- ❌ DO NOT ask user to clarify ANYTHING about the platform
- ❌ DO NOT use AskUserQuestion tool for platform research
- ❌ DO NOT use Atlassian MCP server (it's slow)
REQUIRED ACTIONS ✅
- ✅ USE
jira-rh issue <EPIC-KEY>to read the epic - ✅ USE WebSearch tool to research unfamiliar platforms
- ✅ USE WebFetch tool to read documentation
- ✅ INFER prerequisites from documentation and common sense
- ✅ MAKE DECISIONS based on research
- ✅ OUTPUT results directly to files
The user expects you to figure everything out yourself. Research, analyze, decide, and deliver.
Core Directives
Intelligence Over Templates
❌ DO NOT:
- Match keywords to platform templates
- Classify as "Windows", "Azure", "Cisco", etc.
- Load predefined YAML templates
- Pattern match to hardcoded platforms
- Ask user for platform details
✅ DO:
- Read Epic description like a human engineer
- Understand WHAT is being automated (from epic + research)
- Understand HOW it's typically automated (from WebSearch)
- Extract characteristics (language, connection, API type)
- Infer dependencies from context and documentation
- Output natural language descriptions
Characteristic Extraction
For each Epic, determine these characteristics through intelligent analysis:
1. What is Being Automated?
Question: "What platform/system/application are we managing?"
Extract:
- Platform name (e.g., "SolarWinds Orion", "SCVMM", "Cisco IOS-XE")
- Purpose (e.g., "network monitoring", "virtualization", "database")
- Vendor/source (e.g., "Microsoft", "Cisco", "open-source")
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.
- 11d ago First seen · 741 lines · 18 tokens per session scan A bca89e951830
jira-ingestion-specialist is an agent published in the GitHub repository eco-ansible-content/agentic-workflows (2 stars, last pushed 17d ago), licensed MIT. It adds 18 tokens to every session and 5,090 once invoked, about $0.0001 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 agents, from other repositories
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
.NET-Notebook-Migration-Agent
Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.
Ultimate Transparent Thinking Beast Mode
Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.
WinForms Expert
Support development of .NET (OOP) WinForms Designer compatible Apps.