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 openshift-eng/ai-helpers --skill payload-experimental-revertsgit 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/skills/openshift-eng/ai-helpers/payload-experimental-reverts)<a href="https://agentmods.dev/skills/openshift-eng/ai-helpers/payload-experimental-reverts"><img src="https://agentmods.dev/badge/skills/openshift-eng/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/openshift-eng/ai-helpers/payload-experimental-reverts"><img src="https://agentmods.dev/badge/skills/openshift-eng/ai-helpers/payload-experimental-reverts.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 5 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 75 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
- medium Agent Snooping · line 81 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
- medium Agent Snooping · line 199 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
- medium Agent Snooping · line 198 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
- medium Agent Snooping · line 200 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
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 | $0.00023 | $0.02346 |
| Opus 5 | $0.00012 | $0.01173 |
| Sonnet 5 | $0.00005 | $0.00469 |
| Haiku 4.5 | $0.00002 | $0.00235 |
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 8d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- Payload Experimental Reverts — 97% identical, 12 lines differ
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.
- 8d ago First seen · 202 lines · 23 tokens per session scan A 91b1f92e49e5
payload-experimental-reverts is a skill published in the GitHub repository openshift-eng/ai-helpers (116 stars, last pushed today), licensed Apache-2.0. It adds 23 tokens to every session and 2,346 once invoked, about $0.0001 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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adversarial-reviewer
Adversarial code review that assumes bugs exist and hunts for them. Use when asked to review code, find bugs, audit for correctness, stress-test a PR, or when someone says "tear this apart" or "what's wrong with this". Give no benefit of the doubt — every line is guilty until proven innocent.
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work
Deliver one maintainer-approved EmDash issue, choosing the bug-fix path for a defect and the direct implementation path for an enhancement or task.