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 wangke19/gemini-ai-helpers --skill stage-payload-revertsgit clone --depth 1 https://github.com/wangke19/gemini-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/wangke19/gemini-ai-helpers/stage-payload-reverts)<a href="https://agentmods.dev/skills/wangke19/gemini-ai-helpers/stage-payload-reverts"><img src="https://agentmods.dev/badge/skills/wangke19/gemini-ai-helpers/stage-payload-reverts/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/wangke19/gemini-ai-helpers/stage-payload-reverts"><img src="https://agentmods.dev/badge/skills/wangke19/gemini-ai-helpers/stage-payload-reverts.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 | $0.00025 | $0.02217 |
| Opus 5 | $0.00013 | $0.01108 |
| Sonnet 5 | $0.00005 | $0.00443 |
| Haiku 4.5 | $0.00003 | $0.00222 |
Grade A, and why
Stage Payload Reverts 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 11d 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.
This is a copy
92% identical to stage-payload-reverts — 20 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 176 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Stage Payload Reverts
This skill automates the full revert-staging workflow for payload regressions: creating TRT JIRA bugs, opening revert PRs, and triggering payload validation jobs.
When to Use This Skill
Use this skill when revert candidates have already been identified with high confidence by the analyze-payload skill. The caller passes all required context in-memory — this skill does not perform its own analysis.
Inputs (passed in-context by the caller):
results_yaml_path: Path to the payload results YAML file (e.g.,./payload-results-{tag}.yaml)payload_tag: The full payload tag being analyzedversion,stream,architecture: Parsed from the payload tagrelease_controller_url: URL to the payload on the release controllerrevert_candidates: List of PRs to revert, each with:pr_url,pr_number,component,confidence_score,rationaleoriginating_payload_tag: The payload where this candidate PR first caused failuresfailing_jobs: List of{job_name, prow_url, is_aggregated, underlying_job_name}
Required Skills
Before starting, you MUST load the following skills (they define output schemas used when updating results):
payload-results-yaml— schema for the payload results YAML filepayload-autodl-json— schema for the autodl JSON data file
Prerequisites
- GitHub CLI (
gh): Installed and authenticated - JIRA MCP: Configured for creating TRT issues (validated in Step 1)
- Repository Access: User must have push access to their fork of each target repository
Implementation Steps
Step 1: Check Jira MCP Availability
Before launching subagents, verify the Jira MCP server is working by attempting a lightweight read-only Jira MCP call (e.g., a simple JQL search or fetching the current user profile — use whichever Jira MCP tool is available).
If the call fails (tool not found, connection error, authentication error, or any other error), stop and inform the user. Present two options:
- Fix and retry: "Fix your Jira MCP configuration and tell me when it's working. I'll pick up where I left off."
- Continue without Jira: "Continue without creating Jira issues. I'll open the revert PRs and trigger payload jobs, and give you the details to create Jira issues yourself afterward."
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.
- 11d ago First seen · 176 lines · 25 tokens per session scan A ade00a63910e
Stage Payload Reverts is a skill published in the GitHub repository wangke19/gemini-ai-helpers (2 stars, last pushed 5mo ago), licensed Apache-2.0. It adds 25 tokens to every session and 2,217 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 92% identical to stage-payload-reverts, differing in 20 lines, and is treated as a copy.
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