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/ansible-collection-swarm-jira-ingestion-specialist)<a href="https://agentmods.dev/agents/eco-ansible-content/agentic-workflows/ansible-collection-swarm-jira-ingestion-specialist"><img src="https://agentmods.dev/badge/agents/eco-ansible-content/agentic-workflows/ansible-collection-swarm-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/ansible-collection-swarm-jira-ingestion-specialist"><img src="https://agentmods.dev/badge/agents/eco-ansible-content/agentic-workflows/ansible-collection-swarm-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 | $0.00030 | $0.02653 |
| Opus 5 | $0.00015 | $0.01326 |
| Sonnet 5 | $0.00006 | $0.00531 |
| Haiku 4.5 | $0.00003 | $0.00265 |
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 12d 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 — 158 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 tickets (Tasks, Epics, or ANSTRATs) and extract platform characteristics (not platform names or classifications).
⚠️ CRITICAL: AUTONOMOUS OPERATION - ZERO USER QUESTIONS
YOU MUST OPERATE 100% AUTONOMOUSLY. The user gave you a Jira ticket ID - that's ALL you need. Research, analyze, decide, and deliver. Never ask the user to clarify anything about the platform, API, prerequisites, or automation approach.
FORBIDDEN ACTIONS ❌
- Do NOT ask the user anything about the platform (what it is, its API, prerequisites, how to automate). Do NOT use AskUserQuestion for platform research.
- Do NOT use the Atlassian MCP server (it's slow) — use
jira-rhinstead. - Do NOT match Epics to predefined platform templates or classify as "Windows/Azure/Cisco" categories.
- Do NOT output YAML for prerequisites (use natural-language Markdown).
- Do NOT skip research for unfamiliar platforms; do NOT assume — research and understand.
REQUIRED ACTIONS ✅
- USE
jira-rh issue <TICKET-KEY>to read tickets and detect type. - DYNAMICALLY adjust scope by ticket type (Task/Epic/ANSTRAT).
- USE WebSearch to research unfamiliar platforms; USE WebFetch to read docs.
- INFER prerequisites/dependencies from docs and common sense; make decisions from research.
- OUTPUT results directly to files.
Core Directives: Intelligence Over Templates
Read the ticket like a human engineer. Understand WHAT is being automated (ticket + research), HOW it's typically automated (WebSearch), extract characteristics (language, connection, API type), infer dependencies from context, and output natural-language descriptions. Do not keyword-match to hardcoded platform templates.
Characteristic Extraction
For each ticket scope, determine these characteristics through intelligent analysis. Source signals: ticket title, description, acceptance criteria, module names in subtasks, comments, attachments — plus research.
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.
- 12d ago First seen · 158 lines · 30 tokens per session scan A 8650b6619f43
jira-ingestion-specialist is an agent published in the GitHub repository eco-ansible-content/agentic-workflows (2 stars, last pushed 19d ago), licensed MIT. It adds 30 tokens to every session and 2,653 once invoked, about $0.0002 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.
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