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/grooming)<a href="https://agentmods.dev/commands/wangke19/gemini-ai-helpers/grooming"><img src="https://agentmods.dev/badge/commands/wangke19/gemini-ai-helpers/grooming/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/grooming"><img src="https://agentmods.dev/badge/commands/wangke19/gemini-ai-helpers/grooming.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 | $0.00015 | $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 5d 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.
This is a copy
100% identical to grooming — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
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.
- 5d ago First seen · 265 lines · 15 tokens per session scan A fc2593e0afcf
grooming is a command published in the GitHub repository wangke19/gemini-ai-helpers (2 stars, last pushed 5mo ago), 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. It is 100% identical to grooming, differing in 0 lines, and is treated as a copy.
Other commands, from other repositories
projects
Provides tools for creating and managing your Supabase projects.
session-list
List recent sessions from the project ledger and offer to view one.
7a_stakeholder_comms
Generate stakeholder-facing communications: release notes, demo scripts, and change briefs.
convert-to-plan
Convert planning artifacts (lite-plan, workflow session, markdown) to issue solutions.
execute
Execute queue with DAG-based parallel orchestration (one commit per solution).
queue
Form execution queue from bound solutions using issue-queue-agent (solution-level).