Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add openshift-eng/ai-helpers/plugin install ciWrote 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-analysis)<a href="https://agentmods.dev/skills/openshift-eng/ai-helpers/payload-analysis"><img src="https://agentmods.dev/badge/skills/openshift-eng/ai-helpers/payload-analysis/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-analysis"><img src="https://agentmods.dev/badge/skills/openshift-eng/ai-helpers/payload-analysis.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 | $0.00029 | $0.13621 |
| Opus 5.5 | $0.00012 | $0.05448 |
| Sonnet 5.5 | $0.00006 | $0.02724 |
| Haiku 4.5 | $0.00003 | $0.01362 |
Grade B, and why
payload-analysis scanned grade B with 2 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 16d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Reads agent configuration directoriesmediumAgent snooping
.claude/, .codex/, .gemini/ hold keys, settings and other credentials a mod has no legitimate need for.
SNAPSHOT_SCRIPT=$(find ~/.claude/plugins -type f -path "*/ci/skills/payload-snapshot/scripts/payload_snapshot.py" 2>/dev/null | sort | head -1) Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
**Triage the results using failure context from Steps 3.4 and 3.5.** Extract the key signals from the failure: error messages, failing URLs/domains, exit codes, failing script names, and affected subsystems. Use commit m How it starts
The opening of the file, as written. The whole thing — 617 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Payload Analysis
This skill analyzes a payload using a local snapshot (produced by payload-snapshot) to identify root causes of blocking job failures and produce a comprehensive HTML report. The snapshot pre-gathers all release controller, GitHub, and CI data so this skill can focus purely on analysis — no live API orchestration required.
It supports Rejected payloads (full analysis of all failed blocking jobs), Ready payloads (early analysis of blocking jobs that have already failed), and Accepted payloads (which may have been force-accepted despite blocking failures).
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
- Assess whether an in-progress ("Ready") payload is likely to be rejected
- Determine whether failures are new or persistent
- Identify which PRs likely caused new failures
- Get a comprehensive overview of payload health with actionable root cause analysis
- Re-analyze a historical payload against its original snapshot data
Examples
-
Analyze an amd64 nightly payload (auto-creates snapshot if needed):
/ci:payload-analysis 4.22.0-0.nightly-2026-02-25-152806 -
Analyze using an existing snapshot directory:
/ci:payload-analysis 4.22.0-0.nightly-2026-02-25-152806 --snapshot-dir payload/4.22/nightly -
Analyze an arm64 payload (architecture inferred from tag):
/ci:payload-analysis 4.22.0-0.nightly-arm64-2026-02-25-152806
Prerequisites
- Python 3 (3.10 or later) — for running the snapshot script if needed
- gcloud CLI — for subagent artifact download (must-gather, pod logs)
- GitHub CLI (
gh) — for step-registry change detection (Step 3.6) and checking existing revert PRs (Step 6.3)
Bundled Resources
Load these only at the step that needs them — not up front:
references/investigation-subagent.md— the verbatim per-job subagent prompt andANALYSIS_RESULTformat (Step 4)references/report-guide.md— per-section content rules for the HTML report (Step 7)references/completeness-review.md— the completeness-reviewer prompt and response handling (Step 9)assets/report-template.html— the fill-in-the-blanks HTML report template (Step 7)references/ship-status-component-map.md— SHIP Status slug mapping (Step 6.5)
What ships with it
5 files 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.
- 16d ago Changed 61f74d80534c
- 1mo ago Changed 5256432ca50e
- 1mo ago First seen · 617 lines · 29 tokens per session scan B 057443f072d6
payload-analysis is a skill published in the GitHub repository openshift-eng/ai-helpers (119 stars, last pushed yesterday), licensed Apache-2.0. It adds 29 tokens to every session and 13,621 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it B with 2 findings (reads agent configuration directories, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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