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/dmzoneill/redhat-ai-workflow/check-feedbackgit clone --depth 1 https://github.com/dmzoneill/redhat-ai-workflowWrote 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/dmzoneill/redhat-ai-workflow/check-feedback)<a href="https://agentmods.dev/commands/dmzoneill/redhat-ai-workflow/check-feedback"><img src="https://agentmods.dev/badge/commands/dmzoneill/redhat-ai-workflow/check-feedback.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 | $0.00000 | $0.00733 |
| Opus 5 | $0.00000 | $0.00367 |
| Sonnet 5 | $0.00000 | $0.00147 |
| Haiku 4.5 | $0.00000 | $0.00073 |
Grade A, and why
check-feedback 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 4d 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 — 107 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/check-feedback
Check your open Merge Requests for comments awaiting your response.
Overview
Check your open Merge Requests for comments awaiting your response.
Underlying Skill: check_mr_feedback
This command is a wrapper that calls the check_mr_feedback skill. For detailed process information, see skills/check_mr_feedback.md.
Arguments
No arguments required.
Usage
Examples
skill_run("check_mr_feedback", '{}')
cd ~/src/automation-analytics-backend
for mr in $(glab mr list --author=@me -R automation-analytics/automation-analytics-backend | grep -oP '!\d+' | tr -d '!'); do
echo "=== MR !$mr ==="
glab mr view $mr --comments | grep -A5 -E "^[a-zA-Z].*commented" | grep -v "group_10571_bot\|Konflux\|Starting Pipelinerun"
done
google_calendar_quick_meeting(
title="MR !1445 Race Condition Discussion",
attendee_email="[email protected]",
when="tomorrow 10:00",
duration_minutes=30
)
Process Flow
This command invokes the check_mr_feedback skill. The process flow is:
flowchart LR
START([User runs /check-feedback]) --> VALIDATE[Validate Arguments]
VALIDATE --> CALL[Call check_mr_feedback skill]
CALL --> EXECUTE[Execute Skill Steps]
EXECUTE --> RESULT[Return Result]
RESULT --> END([Complete])
style START fill:#6366f1,stroke:#4f46e5,color:#fff
style END fill:#10b981,stroke:#059669,color:#fff
style CALL fill:#3b82f6,stroke:#2563eb,color:#fff
```text
For detailed step-by-step process, see the [check_mr_feedback skill documentation](../skills/check_mr_feedback.md).
## Details
## Instructions
Run the check_mr_feedback skill to scan your open MRs for:
- Human reviewer comments (filters out bot/CI comments)
- Meeting requests
- Code change requests
- Questions requiring answers
```text
skill_run("check_mr_feedback", '{}')
Or run manually with glab:
cd ~/src/automation-analytics-backend
for mr in $(glab mr list --author=@me -R automation-analytics/automation-analytics-backend | grep -oP '!\d+' | tr -d '!'); do
echo "=== MR !$mr ==="
glab mr view $mr --comments | grep -A5 -E "^[a-zA-Z].*commented" | grep -v "group_10571_bot\|Konflux\|Starting Pipelinerun"
done
```text
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.
- 4d ago First seen · 107 lines · 0 tokens per session scan A b63c165fb535
check-feedback is a command published in the GitHub repository dmzoneill/redhat-ai-workflow (5 stars, last pushed yesterday), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 733 tokens. 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.
Other commands, from other repositories
pr-address
Address PR review comments on current branch.
enforce_standards
CRITICAL: Before running the code standards enforcer, we must prepare the stack properly and abort if there are merge conflicts.
OPSX: Verify
Verify implementation matches change artifacts before archiving.
OPSX: Continue
Continue working on a change - create the next artifact (Experimental).
spec-research
需求 → 约束集(并行探索 + OPSX 提案).
OPSX: Fast Forward
Create a change and generate all artifacts needed for implementation in one go.