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-reportgit 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-report)<a href="https://agentmods.dev/skills/bmbouter/redhat-agents/grooming-report"><img src="https://agentmods.dev/badge/skills/bmbouter/redhat-agents/grooming-report/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-report"><img src="https://agentmods.dev/badge/skills/bmbouter/redhat-agents/grooming-report.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 | $0.00027 | $0.00978 |
| Opus 5.5 | $0.00011 | $0.00391 |
| Sonnet 5.5 | $0.00005 | $0.00196 |
| Haiku 4.5 | $0.00003 | $0.00098 |
Grade A, and why
grooming-report 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 — 96 lines — stays where its author put it; the contents beside it link to each section on GitHub.
When to Use
During and after a backlog grooming session. Use this alongside grooming-prep
and backlog-health-check: those decide what to do, this one keeps a durable
record of what was done so sessions are auditable and reviewable.
Why
Grooming touches many tickets across a session. Without a record, there is no way to answer "what changed last Tuesday?", to review automated actions after the fact, or to hand a summary to the team. This skill maintains a machine-readable audit trail and turns it into a report the PO and team can read.
Where the Files Live
Keep the audit trail in the gitignored local/ directory so it stays with the
user's workflow config and out of version control:
local/grooming_log.jsonl— append-only audit trail (one JSON object per line)local/grooming_report.md— generated report (do not edit by hand)
Instructions
Read local/jira-workflow.md first to understand the project's conventions
(issue types, hierarchy, required fields, status names).
1. Mark the start of the session
Before making any changes, append a session marker to
local/grooming_log.jsonl:
{"session_start": "YYYY-MM-DD", "groomer": "Name", "total_issues": N, "analyzed": M, "note": "what this session focused on"}
total_issues— how many issues matched the grooming queryanalyzed— how many you actually reviewed this session
2. Log every action as you go
Append one line per action. Use a small, consistent set of action values so
the report stays scannable. Common actions:
{"issue": "KEY-123", "action": "updated", "changes": ["added_label_X", "assigned_to_parent_KEY-100"], "notes": "context", "timestamp": "ISO8601"}
{"issue": "KEY-456", "action": "postponed_for_team_review", "reason": "needs_team_input_on_priority", "timestamp": "ISO8601"}
{"action": "created_epic", "issue": "KEY-789", "summary": "Epic title", "parent": "PARENT-KEY", "timestamp": "ISO8601"}
{"action": "created_feature", "issue": "KEY-790", "summary": "Feature title", "parent": "PARENT-KEY", "timestamp": "ISO8601"}
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 96 lines · 27 tokens per session scan A 06fa1a0d288c
grooming-report is a skill published in the GitHub repository bmbouter/redhat-agents (7 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 27 tokens to every session and 978 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 2026-09-05.
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