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/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/generate-feature-updates)<a href="https://agentmods.dev/commands/openshift-eng/ai-helpers/generate-feature-updates"><img src="https://agentmods.dev/badge/commands/openshift-eng/ai-helpers/generate-feature-updates/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/openshift-eng/ai-helpers/generate-feature-updates"><img src="https://agentmods.dev/badge/commands/openshift-eng/ai-helpers/generate-feature-updates.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 | $0.00008 | $0.04390 |
| Opus 5.5 | $0.00003 | $0.01756 |
| Sonnet 5.5 | $0.00002 | $0.00878 |
| Haiku 4.5 | $0.00001 | $0.00439 |
Grade A, and why
generate-feature-updates 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 — 464 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Name
jira:generate-feature-updates
Synopsis
/jira:generate-feature-updates [project-key] [--component <component-name>] [--label <label-name>] [user-filters...]
Description
The jira:generate-feature-updates command generates concise executive-level feature summaries for weekly status documents. It analyzes recent activity across Jira issues and their descendants to produce prose-style updates suitable for the "Key Strategic Feature Updates" section of weekly reports.
This command is particularly useful for:
- Weekly executive status documents
- Strategic feature progress summaries
- Stakeholder communication on feature delivery
- Identifying blocked or at-risk features
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
childIssuesOf()for full hierarchy traversal - GitHub PR and GitLab MR integration via external links
- Automatic filtering: skips issues with no significant activity
- Batch processing with full-section review
- Nested list output: feature link as top-level bullet, update prose as nested bullet
This command uses the Status Analysis Engine skill for core analysis logic. See plugins/jira/skills/status-analysis/SKILL.md for detailed implementation.
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.,
-
If project key is NOT provided:
- Use
mcp__atlassian-mcp__jira_get_all_projectsto list all accessible projects - Present projects in a numbered list with keys and names
- Ask: "Please enter the number of the project you want to generate updates for:"
- Parse response and extract project key
- Use
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 · 464 lines · 8 tokens per session scan A 74ae8bf893bb
generate-feature-updates is a command published in the GitHub repository openshift-eng/ai-helpers (121 stars, last pushed today), licensed Apache-2.0. It adds 8 tokens to every session and 4,390 once invoked, about $0.0000 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 2026-09-03.
Other commands, from other repositories
template
Manage issue templates for streamlined issue creation.
fest-show
Show festival progression (in-progress tasks, roadmap, and dependency view).
add-note
Add an internal or external note to a ConnectWise PSA ticket.
sync-linear
Sync current work with Linear ticket status.
dispatcher
Use when you want the orchestrator to pick the next repo to work on across your whole portfolio — it enumerates candidate repos below the confinement root, resolves free/busy from each repo's session.lock lease, ranks the FREE ones by backlog priority × staleness × readiness, recommends the single most worthwhile one…
workpm
A project-management workflow for coordinating multiple AI workers through five stages. It includes task assignment, shared activity logs, worker replacement, and final checks.