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/groominggit 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/grooming)<a href="https://agentmods.dev/commands/openshift-eng/ai-helpers/grooming"><img src="https://agentmods.dev/badge/commands/openshift-eng/ai-helpers/grooming.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 | $0.00015 | $0.02360 |
| Opus 5 | $0.00008 | $0.01180 |
| Sonnet 5 | $0.00003 | $0.00472 |
| Haiku 4.5 | $0.00002 | $0.00236 |
Grade A, and why
grooming 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- grooming — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 265 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Name
jira:grooming
Synopsis
/jira:grooming [project-filter] [time-period] [--component component-name] [--label label-name] [--type issue-type] [--status status] [--story-points]
Description
The jira:grooming command helps teams prepare for backlog grooming meetings. It automatically collects bugs and user stories created within a specified time period OR assigned to a specific sprint, analyzes their priority, complexity, and dependencies, and generates structured grooming meeting agendas.
This command is particularly useful for:
- Backlog organization before sprint planning
- Sprint-specific grooming sessions
- Sprint-specific grooming sessions with story point summaries
- Sprint retrospectives analyzing completed work
- Regular requirement grooming meetings
- Priority assessment of new bugs
- Technical debt organization and planning
Key Features
-
Automated Data Collection – Collect and categorize issues within specified time periods or sprints by type (Bug, Story, Task, Epic), extract key information (priority, components, labels), and identify unassigned or incomplete issues.
-
Story Point Analysis – When
--story-pointsflag is used, extract and analyze story points for all issues, calculate totals by status, priority, and type, and provide velocity metrics for sprint retrospectives. -
Status Filtering – Filter issues by status (e.g., Closed, Done, In Progress, Open) using the
--statusflag to focus on specific workflow states for sprint reviews or retrospectives. -
Intelligent Analysis – Evaluate issue complexity based on historical data, identify related or duplicate issues, analyze business value and technical impact, and detect potential dependencies.
-
Agenda Generation – Build a structured, actionable meeting outline organized by priority and type, with discussion points, decision recommendations, estimation references, and risk alerts.
Implementation
The jira:grooming command runs in three main phases:
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 · 265 lines · 15 tokens per session scan A fc2593e0afcf
grooming is a command published in the GitHub repository openshift-eng/ai-helpers (116 stars, last pushed today), licensed Apache-2.0. It adds 15 tokens to every session and 2,360 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.