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 opendatahub-io/ai-helpers --skill vllm-backport-cherry-pickgit clone --depth 1 https://github.com/opendatahub-io/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/skills/opendatahub-io/ai-helpers/vllm-backport-cherry-pick)<a href="https://agentmods.dev/skills/opendatahub-io/ai-helpers/vllm-backport-cherry-pick"><img src="https://agentmods.dev/badge/skills/opendatahub-io/ai-helpers/vllm-backport-cherry-pick/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/opendatahub-io/ai-helpers/vllm-backport-cherry-pick"><img src="https://agentmods.dev/badge/skills/opendatahub-io/ai-helpers/vllm-backport-cherry-pick.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00054 | $0.00484 |
| Opus 5 | $0.00027 | $0.00242 |
| Sonnet 5 | $0.00011 | $0.00097 |
| Haiku 4.5 | $0.00005 | $0.00048 |
Grade A, and why
vllm-backport-cherry-pick 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 9d 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
Cherry Pick
Selects eligible candidates from ranked.json, attempts cherry-pick on each, and creates a draft PR if any succeed.
Candidate Selection
A PR is eligible if ALL of:
backport_ease == "ai-fixable"score >= 50already_backported == falseverdictismust_backportorlikely_relevant
Usage
bash scripts/cherry-pick.sh \
--input artifacts/backport-triage/ranked.json \
--downstream /path/to/downstream-repo \
--branch rhai/0.13.0 \
--jira-url "https://redhat.atlassian.net/browse/..." \
--report-url "https://github.com/..." \
--output artifacts/backport-triage/cherry-pick-result.json
Output
cherry-pick-result.json:
{
"status": "created|skipped",
"pr_url": "https://...",
"succeeded": 3,
"conflicts": 1,
"results": [{"number": 12345, "title": "...", "score": 85, "status": "success|conflict"}]
}
Agent Follow-up (Required)
After this skill runs, the agent MUST:
- Semantic validation — review the cherry-picked diff, check imports reference modules that exist at the target tag, check for calls to post-release functions
- If issues found, add a comment on the draft PR
- Update the Jira ticket with the PR link
- For conflict candidates with score >= 70, add label
ai-autofix-candidate
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.
- 9d ago First seen · 61 lines · 54 tokens per session scan A 4b6473b8929e
vllm-backport-cherry-pick is a skill published in the GitHub repository opendatahub-io/ai-helpers (37 stars, last pushed 5d ago), licensed Apache-2.0. It adds 54 tokens to every session and 484 once invoked, about $0.0003 per session on Opus 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 2026-09-03.
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