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 agentmods add commands/openshift-eng/ai-helpers/update-weekly-statusgit clone --depth 1 https://github.com/openshift-eng/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/openshift-eng/ai-helpers/update-weekly-status)<a href="https://agentmods.dev/commands/openshift-eng/ai-helpers/update-weekly-status"><img src="https://agentmods.dev/badge/commands/openshift-eng/ai-helpers/update-weekly-status.svg" alt="Measured on agentmods" 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 | $0.00012 | $0.04756 |
| Opus 5 | $0.00006 | $0.02378 |
| Sonnet 5 | $0.00002 | $0.00951 |
| Haiku 4.5 | $0.00001 | $0.00476 |
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 2d 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 — 560 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 plugins/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.
- 2d ago First seen · 560 lines · 12 tokens per session scan A cd8adfb3df93
update-weekly-status is a command published in the GitHub repository openshift-eng/ai-helpers (116 stars, last pushed today), licensed Apache-2.0. It adds 12 tokens to every session and 4,756 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.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.