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 payload-experimental-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/payload-experimental-reverts)<a href="https://agentmods.dev/skills/wangke19/gemini-ai-helpers/payload-experimental-reverts"><img src="https://agentmods.dev/badge/skills/wangke19/gemini-ai-helpers/payload-experimental-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/payload-experimental-reverts"><img src="https://agentmods.dev/badge/skills/wangke19/gemini-ai-helpers/payload-experimental-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 | $0.00022 | $0.02345 |
| Opus 5 | $0.00011 | $0.01172 |
| Sonnet 5 | $0.00004 | $0.00469 |
| Haiku 4.5 | $0.00002 | $0.00234 |
Grade A, and why
Payload Experimental 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
97% identical to payload-experimental-reverts — 12 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 — 202 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Payload Experimental Reverts
This skill experimentally tests medium-confidence candidate PRs by opening draft revert PRs, triggering payload jobs, and evaluating results. It operates in two phases separated by a CI wait period. All state is tracked in the payload results YAML file via the payload-results-yaml skill — no separate tracking file is created.
When to Use This Skill
Use this skill when the /ci:payload-experiment command identifies candidate PRs with medium confidence (score 60-84) that cannot be conclusively attributed to a failure through static analysis alone. The experiment creates real tests to determine causality.
Inputs (passed in-context by the caller):
results_yaml_path: Path to the payload results YAML file (e.g.,./payload-results-{tag}.yaml)candidates: List of medium-confidence PRs to test experimentally, each with:pr_url,pr_number,component,title,confidence_scorefailing_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 (needed in Phase 2 for confirmed causes)
- Repository Access: User must have push access to their fork of each target repository
Implementation Steps
Phase 1: Set Up Experiments
For each medium-confidence candidate, launch a parallel subagent (do NOT set the model parameter):
1.1: Check for Merge Conflicts
Before opening a revert PR, preemptively check whether the revert will have merge conflicts:
# Clone the repo (shallow for speed)
git clone -b <base_branch> --depth 50 "https://github.com/<org>/<repo>.git" /tmp/experiment-check-<pr_number>
cd /tmp/experiment-check-<pr_number>
# Attempt the revert without committing
git revert -m1 --no-commit <merge_sha>
# Check for conflicts
git status --porcelain
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 · 202 lines · 22 tokens per session scan A 7b09cdbb8835
Payload Experimental 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 22 tokens to every session and 2,345 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 97% identical to payload-experimental-reverts, differing in 12 lines, and is treated as a copy.
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