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 skills add bmbouter/redhat-agents --skill grooming-prepgit clone --depth 1 https://github.com/bmbouter/redhat-agentsWrote 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/skills/bmbouter/redhat-agents/grooming-prep)<a href="https://agentmods.dev/skills/bmbouter/redhat-agents/grooming-prep"><img src="https://agentmods.dev/badge/skills/bmbouter/redhat-agents/grooming-prep/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/skills/bmbouter/redhat-agents/grooming-prep"><img src="https://agentmods.dev/badge/skills/bmbouter/redhat-agents/grooming-prep.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 | $0.00032 | $0.00425 |
| Opus 5.5 | $0.00013 | $0.00170 |
| Sonnet 5.5 | $0.00006 | $0.00085 |
| Haiku 4.5 | $0.00003 | $0.00042 |
Grade A, and why
grooming-prep 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.
What it actually says
When to Use
Before a backlog grooming or refinement session.
Instructions
Read local/jira-workflow.md to understand the project's conventions.
1. Gather tickets
Ask the PO what to groom:
- A specific epic and its sub-issues?
- All issues in "New" status?
- A specific set of ticket keys?
Query the relevant issues from Jira.
2. Assess readiness
For each ticket, evaluate:
- Description quality: Is there enough detail to understand the work?
- Acceptance criteria: Present and testable?
- Dependencies: Are blockers or related issues identified?
- Estimation: If story points are used, is it estimated?
- Type correctness: Is the issue type right per the workflow config's decision rules?
Classify each ticket as:
- Ready — can be discussed and moved to Refinement
- Needs work — has gaps that should be filled before or during grooming
- Blocked — has unresolved dependencies or questions
3. Produce agenda
## Grooming Agenda — <date>
### Context
[Epic name or theme being groomed]
### Ready for Refinement (X tickets)
1. ISSUE-123 — "Summary" — priority: Major
2. ISSUE-456 — "Summary" — priority: Normal
### Needs Discussion (Y tickets)
1. ISSUE-789 — "Summary"
- Missing: acceptance criteria
- Question: Is this blocked by ISSUE-012?
### Blocked (Z tickets)
1. ISSUE-345 — "Summary"
- Blocked by: ISSUE-678 (still in progress)
### Suggested Discussion Order
[Order tickets by dependency chain, then priority]
### Open Questions
- [Questions that came up during analysis]
Present to the PO for review. Adjust order or grouping as requested.
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 · 67 lines · 32 tokens per session scan A 435b2b8d6b4b
grooming-prep is a skill published in the GitHub repository bmbouter/redhat-agents (7 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 32 tokens to every session and 425 once invoked, about $0.0001 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 2026-08-31.
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