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 GoogilyBoogily/googilyboogily-claude-power-tools --skill jira-ticketsgit clone --depth 1 https://github.com/GoogilyBoogily/googilyboogily-claude-power-toolsWrote 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/googilyboogily/googilyboogily-claude-power-tools/jira-tickets)<a href="https://agentmods.dev/skills/googilyboogily/googilyboogily-claude-power-tools/jira-tickets"><img src="https://agentmods.dev/badge/skills/googilyboogily/googilyboogily-claude-power-tools/jira-tickets/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/googilyboogily/googilyboogily-claude-power-tools/jira-tickets"><img src="https://agentmods.dev/badge/skills/googilyboogily/googilyboogily-claude-power-tools/jira-tickets.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 | $0.00066 | $0.03128 |
| Opus 5.5 | $0.00026 | $0.01251 |
| Sonnet 5.5 | $0.00013 | $0.00626 |
| Haiku 4.5 | $0.00007 | $0.00313 |
Grade A, and why
jira-tickets 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 — 297 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Jira Ticket Extraction from Architecture Documents
Extract actionable items from a finalized ADR, HLD, or LLD document and create Jira tickets under a user-specified epic. All tickets are created unassigned — assignment is a sprint planning concern, not a ticket creation concern.
When to Use
- After an HLD or LLD has been generated and audited — ready for implementation planning
- After an ADR when you want to track consequences and required changes
- When you need to bridge architecture documentation with project management
- As an optional step after the
arch-pipelinecompletes
Prerequisites: A finalized architecture document (ADR, HLD, or LLD) and an existing Jira epic to create tickets under.
Source Integrity Rules
Every factual claim about the document must be verified through tool calls in this session.
- Cite your work. When extracting items, cite the exact section and line from the document.
- Never fabricate tickets. Only create tickets for items that are explicitly present in the document. Do not infer or add items that aren't in the source sections.
- Preserve source language. Ticket titles and scope descriptions should use language from the document, not paraphrased rewrites.
Process
Human-in-the-loop: Never create Jira tickets without explicit user approval. Every ticket must be reviewed before creation.
Phase 1: Parse Input and Detect Document Type
-
Parse
$ARGUMENTSto extract:- Document path — the architecture document to extract from
--epic PROJ-123(optional) — the epic key to create tickets under
-
If no document path is provided, ask the user:
- "Which architecture document should I extract tickets from? Provide the file path."
-
Read the document in full.
-
Detect document type by structural markers:
Marker Document Type MADR frontmatter ( status:,date:,decision-makers:), "Considered Options", "Decision Outcome"ADR "Implementation Phases" table, "Codebase Impact", "Proposed Solution" with Architecture subsection HLD "File-Level Implementation Plan", "Testing Specifications", method signature tables LLD
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 297 lines · 66 tokens per session scan A 0565682b0a2d
jira-tickets is a skill published in the GitHub repository GoogilyBoogily/googilyboogily-claude-power-tools (2 stars, last pushed 5mo ago), licensed MIT. It adds 66 tokens to every session and 3,128 once invoked, about $0.0003 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 2026-08-31.
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