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 opendatahub-io/ai-helpers --skill jira-activitygit clone --depth 1 https://github.com/opendatahub-io/ai-helpersWrote 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/opendatahub-io/ai-helpers/jira-activity)<a href="https://agentmods.dev/skills/opendatahub-io/ai-helpers/jira-activity"><img src="https://agentmods.dev/badge/skills/opendatahub-io/ai-helpers/jira-activity/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/opendatahub-io/ai-helpers/jira-activity"><img src="https://agentmods.dev/badge/skills/opendatahub-io/ai-helpers/jira-activity.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 | $0.00043 | $0.01023 |
| Opus 5 | $0.00022 | $0.00511 |
| Sonnet 5 | $0.00009 | $0.00205 |
| Haiku 4.5 | $0.00004 | $0.00102 |
Grade A, and why
jira-activity 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 — 111 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Jira Activity Summary
Analyze a Jira ticket and its child tickets to produce a staleness report. Useful for triaging Features and Initiatives to determine if they are actively being worked on, slowing down, or dormant.
Prerequisites
- Python 3 and
uvmust be installed and available in PATH JIRA_API_TOKENenvironment variable must be set with a valid API token for https://redhat.atlassian.netJIRA_EMAILenvironment variable must be set with the email address associated with your Atlassian account- Appropriate JIRA permissions to read the target ticket and its children
Usage
This skill fetches activity data for a Jira ticket and its entire descendant hierarchy (Initiative → Epic → Stories/Tasks), then interprets the results into a staleness report.
Implementation
Step 1: Determine the Ticket Key
- If a ticket key is provided by the user, use it
- Otherwise, search the conversation history for JIRA ticket references (e.g., "AIPCC-1234", "RHOAIENG-567")
- If no ticket is found in context, ask the user: "Which JIRA ticket should I analyze? (e.g., RHOAIENG-1234)"
Optionally the user may specify a --days N flag to control the lookback window (default: 30 days).
Step 2: Fetch Activity Data
Run the fetch script located at scripts/fetch_jira_activity.py relative to this skill. Execute it directly (not via python) to invoke uv via the shebang:
./scripts/fetch_jira_activity.py <TICKET-KEY> [--days N]
The script outputs JSON to stdout containing the complete hierarchy of issues with levels (0=root, 1=child, 2=grandchild, etc.) including statuses, assignees, recent comments, and changelog entries.
Step 3: Interpret the JSON into a Staleness Report
Analyze the JSON output and produce a report with the following sections:
Overall Staleness Assessment
Assign one of these labels based on the activity patterns:
- Active: Regular status changes, comments, or updates within the lookback window
- Moderate: Some activity but gaps or only partial child ticket movement
- Stale: Little to no meaningful activity; most child tickets idle
- Dormant: No activity at all across parent and children during the lookback window
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.
- 12d ago First seen · 111 lines · 43 tokens per session scan A 193d01dc3ce0
jira-activity is a skill published in the GitHub repository opendatahub-io/ai-helpers (37 stars, last pushed 5d ago), licensed Apache-2.0. It adds 43 tokens to every session and 1,023 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 2026-08-30.
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