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/analyze-regressiongit 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/analyze-regression)<a href="https://agentmods.dev/commands/openshift-eng/ai-helpers/analyze-regression"><img src="https://agentmods.dev/badge/commands/openshift-eng/ai-helpers/analyze-regression.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 | $0.00012 | $0.19046 |
| Opus 5 | $0.00006 | $0.09523 |
| Sonnet 5 | $0.00002 | $0.03809 |
| Haiku 4.5 | $0.00001 | $0.01905 |
Grade C, and why
analyze-regression scanned grade C with 1 finding 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.
Reaches for credential fileshighPrivilege escalation
SSH keys, cloud credentials, git-credentials, .npmrc, /etc/shadow: reading these is how a config file becomes a credential leak.
This works because `oc` reads from `~/.kube/config` which is bind-mounted from the host. The token stored in the kubeconfig was obtained when the user previously ran `oc login` to the DPCR cluster on the host. If the tok How it starts
The opening of the file, as written. The whole thing — 1,363 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Name
ci:analyze-regression
Synopsis
/ci:analyze-regression <regression id>
Description
The ci:analyze-regression command analyzes details for a specific Component Readiness regression and suggests next steps for investigation.
The command performs a full analysis regardless of whether the regression has been triaged. For triaged regressions, it also fetches the linked JIRA issue to analyze whether someone is actively working on the fix or if the issue needs attention.
This command is useful for:
- Understanding regression patterns and failure modes
- Checking if a triaged regression is being actively worked on or needs attention
- Identifying related regressions that might be caused by the same issue
- Getting pointers on where to investigate next
Implementation
Important: Avoiding user permission prompts when running scripts
When calling Python skill scripts via the Bash tool, always run the script directly without piping the output through inline Python (python3 -c "..."). Complex piped commands trigger user permission prompts, while simple python3 script.py args calls are auto-approved.
- Do:
python3 script.py args --format json 2>/dev/null— run the script directly and process the JSON output in your reasoning - Don't:
python3 script.py args | python3 -c "import json; ..."— piped inline Python triggers permission prompts
Parse and analyze the JSON output from scripts using your own reasoning capabilities rather than shell pipelines.
Obtaining the DPCR authentication token from mounted kubeconfig
The triage and bug filing steps (step 14) require a Bearer token from the DPCR cluster (api.cr.j7t7.p1.openshiftapps.com:6443). When running in a container with ~/.kube mounted (read-only), the token is extracted directly from the mounted kubeconfig using oc:
# Find the oc context for the DPCR cluster from the mounted kubeconfig
DPCR_CONTEXT=$(oc config get-contexts -o name 2>/dev/null | while read -r ctx; do
server=$(oc config view -o jsonpath="{.clusters[?(@.name=='$(oc config view -o jsonpath="{.contexts[?(@.name=='$ctx')].context.cluster}" 2>/dev/null)')].cluster.server}" 2>/dev/null || echo "")
server_clean=$(echo "$server" | sed -E 's|^https?://||')
if [ "$server_clean" = "api.cr.j7t7.p1.openshiftapps.com:6443" ]; then
echo "$ctx"
break
fi
done)
# Extract the token from the DPCR context
if [ -z "$DPCR_CONTEXT" ]; then
echo "ERROR: Could not find a DPCR cluster context in kubeconfig. Set DPCR_CONTEXT manually or run: oc login https://api.cr.j7t7.p1.openshiftapps.com:6443"
exit 1
fi
TOKEN=$(oc whoami -t --context="$DPCR_CONTEXT" 2>/dev/null)
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 · 1,363 lines · 12 tokens per session scan C 8b56677102a6
analyze-regression is a command published in the GitHub repository openshift-eng/ai-helpers (116 stars, last pushed today), licensed Apache-2.0. It adds 12 tokens to every session and 19,046 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it C with 1 finding (reaches for credential files). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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checklist
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clarify
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specify
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analyze
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converge
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implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.