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 analyze-payloadgit 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/analyze-payload)<a href="https://agentmods.dev/skills/wangke19/gemini-ai-helpers/analyze-payload"><img src="https://agentmods.dev/badge/skills/wangke19/gemini-ai-helpers/analyze-payload/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/analyze-payload"><img src="https://agentmods.dev/badge/skills/wangke19/gemini-ai-helpers/analyze-payload.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 | $0.00033 | $0.08670 |
| Opus 5 | $0.00016 | $0.04335 |
| Sonnet 5 | $0.00007 | $0.01734 |
| Haiku 4.5 | $0.00003 | $0.00867 |
Grade D, and why
Analyze Payload scanned grade D with 2 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Hidden instructionshighPrompt injection
Directives inside HTML comments, invisible characters or bidirectional overrides are read by the model and not by the person reviewing the file.
<!-- Automated revert instructions --> Reads agent configuration directoriesmediumAgent snooping
.claude/, .codex/, .gemini/ hold keys, settings and other credentials a mod has no legitimate need for.
FETCH_PAYLOADS=$(find ~/.claude/plugins -type f -path "*/ci/skills/fetch-payloads/fetch_payloads.py" 2>/dev/null | sort | head -1) How it starts
The opening of the file, as written. The whole thing — 542 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analyze Payload
This skill analyzes a payload for a given OCP version, walks back through consecutive rejected payloads to determine when each failure started, correlates failures with newly introduced PRs, investigates each failed job in parallel, and produces a comprehensive HTML report.
It supports Rejected payloads (full analysis of all failed blocking jobs), Ready payloads (early analysis of blocking jobs that have already failed, with a determination of whether the payload is on track for rejection), and Accepted payloads (payloads can be force-accepted despite blocking failures, so any failed blocking jobs are still analyzed).
When to Use This Skill
Use this skill when you need to:
- Understand why a payload was rejected
- Investigate failures in a force-accepted payload (Accepted payloads may still have failed blocking jobs)
- Assess whether an in-progress ("Ready") payload is likely to be rejected based on already-failed blocking jobs
- Determine whether failures are new or persistent (permafailing)
- Identify which PRs likely caused new failures
- Get a comprehensive overview of payload health with actionable root cause analysis
Required Skills
Before starting, you MUST load the following skills (they define output schemas used in Steps 6, 8, and 9):
payload-results-yaml— schema for the payload results YAML filepayload-autodl-json— schema for the autodl JSON data file
Prerequisites
- Network Access: Must be able to reach:
- OpenShift release controller (
amd64.ocp.releases.ci.openshift.org) - Sippy API (
sippy.dptools.openshift.org) - Prow (
prow.ci.openshift.org)
- OpenShift release controller (
- Python 3: For running fetch scripts
- gcloud CLI: For downloading Prow job artifacts
Implementation Steps
Step 1: Parse Arguments
The first argument is a full payload tag (e.g., 4.22.0-0.nightly-2026-02-25-152806). Parse from it:
tag: The specific payload tag to analyzeversion: Extract from the tag (e.g.,4.22from4.22.0-0.nightly-...)stream: Extract from the tag (e.g.,nightlyfrom4.22.0-0.nightly-...)architecture: Inferred from the tag. The tag format is<version>-0.<stream>[-<arch>]-<timestamp>. If no architecture is present between the stream and timestamp, it isamd64. Otherwise, the architecture is the segment between the stream and timestamp. Examples:4.22.0-0.nightly-2026-02-25-152806→amd644.22.0-0.nightly-arm64-2026-02-25-152806→arm644.22.0-0.nightly-ppc64le-2026-02-25-152806→ppc64le4.22.0-0.nightly-s390x-2026-02-25-152806→s390x4.22.0-0.nightly-multi-2026-02-25-152806→multi
lookback: From--lookback N(default:10)
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.
- 10d ago First seen · 542 lines · 33 tokens per session scan D d2e5cb0480df
Analyze Payload is a skill published in the GitHub repository wangke19/gemini-ai-helpers (2 stars, last pushed 5mo ago), licensed Apache-2.0. It adds 33 tokens to every session and 8,670 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it D with 2 findings (hidden instructions, reads agent configuration directories). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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