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/wangke19/gemini-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/commands/wangke19/gemini-ai-helpers/update-weekly-status)<a href="https://agentmods.dev/commands/wangke19/gemini-ai-helpers/update-weekly-status"><img src="https://agentmods.dev/badge/commands/wangke19/gemini-ai-helpers/update-weekly-status/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/commands/wangke19/gemini-ai-helpers/update-weekly-status"><img src="https://agentmods.dev/badge/commands/wangke19/gemini-ai-helpers/update-weekly-status.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 | $0.00012 | $0.04276 |
| Opus 5 | $0.00006 | $0.02138 |
| Sonnet 5 | $0.00002 | $0.00855 |
| Haiku 4.5 | $0.00001 | $0.00428 |
Grade A, and why
update-weekly-status 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 9d 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 — 550 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Name
jira:update-weekly-status
Synopsis
/jira:update-weekly-status [project-key] [--component <component-name>] [--label <label-name>] [user-filters...]
Description
The jira:update-weekly-status command automates the process of updating weekly status summaries for Jira issues in a specified project. It analyzes recent activity across tickets, GitHub PRs, and GitLab MRs to draft color-coded status updates (Red/Yellow/Green), then allows you to review and modify them before updating Jira.
This command is particularly useful for:
- Weekly status updates on strategic issues
- Team lead status reporting workflows
- Consistent formatting across status updates
- Reducing manual effort in gathering context from multiple sources
Key capabilities:
- Efficient batch data gathering using async Python script
- Interactive component selection from available project components
- User filtering by email or display name (with auto-resolution)
- Intelligent activity analysis using
parent = KEYJQL for full hierarchy traversal (Atlassian Cloud compatible) - GitHub PR and GitLab MR integration via external links
- Recent update warnings to prevent duplicate updates
- Batch processing with selective skip options
- Formatted status summaries with color-coded health indicators (Red/Yellow/Green)
This command uses the Status Analysis Engine skill for core analysis logic. See extensions/jira/skills/status-analysis/SKILL.md for detailed implementation.
[Extended thinking: This command streamlines weekly status update workflows by first gathering all data efficiently using an async Python script, then processing each issue with focused LLM context. This two-phase approach minimizes API calls and ensures consistent, high-quality analysis.]
Implementation
The command executes in two phases:
Phase 1: Data Gathering
Step 1. Parse Arguments and Determine Target Project
- Parse command-line arguments:
- Extract project key from first positional argument (e.g.,
OCPSTRAT,OCPBUGS) - Parse optional
--component <component-name>parameter - Parse optional
--label <label-name>parameter - Parse user filter parameters (space-separated emails or names)
- User filters support exclusion by prefixing with an exclamation mark (example: ![email protected])
- Extract project key from first positional argument (e.g.,
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.
- 9d ago First seen · 550 lines · 12 tokens per session scan A 919203c52009
update-weekly-status is a command published in the GitHub repository wangke19/gemini-ai-helpers (2 stars, last pushed 5mo ago), licensed Apache-2.0. It adds 12 tokens to every session and 4,276 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 2026-09-03.
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