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 agentmods add skills/redhat-et/rhdp-rca-plugin/v2npx skills add redhat-et/rhdp-rca-plugin --skill v2git clone --depth 1 https://github.com/redhat-et/rhdp-rca-pluginWrote 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/redhat-et/rhdp-rca-plugin/v2)<a href="https://agentmods.dev/skills/redhat-et/rhdp-rca-plugin/v2"><img src="https://agentmods.dev/badge/skills/redhat-et/rhdp-rca-plugin/v2.svg" alt="Measured on agentmods" 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 | $0.00063 | $0.03392 |
| Opus 5 | $0.00032 | $0.01696 |
| Sonnet 5 | $0.00013 | $0.00678 |
| Haiku 4.5 | $0.00006 | $0.00339 |
Grade A, and why
root_cause_analysis 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 4d 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.
How it starts
The opening of the file, as written. The whole thing — 391 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Root Cause Analysis
Investigate failed jobs by correlating Ansible Automation Platform (AAP) job logs with Splunk OCP pod logs and analyzing AgnosticD/AgnosticV configuration to identify root causes.
This version uses Python scripts to parse job metadata and build GitHub investigation paths (Steps 1-4a). Then, use GitHub MCP tools to fetch configuration and workload files from GitHub repositories.
Automatic Execution
When a user asks to analyze a failed job, execute these steps automatically.
The skill's base path is provided when this skill is invoked. Run scripts relative to this folder.
Setup (run once per session if .venv doesn't exist)
# Create virtual environment and install dependencies
python3 -m venv .venv && .venv/bin/pip install -q -r requirements.txt
Step 1-4a: Run the analysis CLI
# Option 1: By job ID (searches JOB_LOGS_DIR automatically)
.venv/bin/python scripts/cli.py analyze --job-id <JOB_ID>
# Option 2: By explicit path
.venv/bin/python scripts/cli.py analyze --job-log <path-to-job-log>
The CLI automatically runs Steps 1-4a:
- Step 1: Parse job log → extract identifiers, failed tasks
- Step 2: Query Splunk → fetch correlated OCP pod logs
- Step 3: Build correlation timeline
- Step 4a: Parse GitHub paths → ONLY generates candidate paths (does NOT fetch files)
Output files:
.analysis/<job-id>/step1_job_context.json.analysis/<job-id>/step2_splunk_logs.json.analysis/<job-id>/step3_correlation.json.analysis/<job-id>/step4a_github_paths.json(candidate paths only - files NOT fetched yet)
Step 4b-4e: Analyze with AgnosticD/V context (You do this)
Input: Read the following files in order (optimized to avoid redundancy):
- REQUIRED:
step1_job_context.json- Job metadata and failed task details - REQUIRED:
step4a_github_paths.json- Parsed GitHub paths and investigation targets - REQUIRED:
step3_correlation.json- Correlated timeline with relevant pod logs (DO NOT read step2 unless needed) - CONDITIONAL:
step2_splunk_logs.json- Only read if step3 indicates errors needing deeper investigation
What ships with it
12 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.
- README.md 7.1 KB
- requirements.txt 21 B
- schemas/correlation.schema.json 2.6 KB
- schemas/job_context.schema.json 2.4 KB
- schemas/summary.schema.json 6.7 KB
- scripts/__init__.py 30 B runs code
- scripts/cli.py 12 KB runs code
- scripts/config.py 3.2 KB runs code
- scripts/correlator.py 10.0 KB runs code
- scripts/job_parser.py 6.2 KB runs code
- scripts/parse_github_paths.py 10.0 KB runs code
- scripts/splunk_client.py 5.5 KB runs code
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.
- 4d ago First seen · 391 lines · 63 tokens per session scan A b66025819a8a
root_cause_analysis is a skill published in the GitHub repository redhat-et/rhdp-rca-plugin (10 stars, last pushed 10d ago), licensed Apache-2.0. It adds 63 tokens to every session and 3,392 once invoked, about $0.0003 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 2026-08-31.
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