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/v1npx skills add redhat-et/rhdp-rca-plugin --skill v1git 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/v1)<a href="https://agentmods.dev/skills/redhat-et/rhdp-rca-plugin/v1"><img src="https://agentmods.dev/badge/skills/redhat-et/rhdp-rca-plugin/v1.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 5.1 | $0.00063 | $0.04162 |
| Opus 5 | $0.00032 | $0.02081 |
| Sonnet 5 | $0.00013 | $0.00832 |
| Haiku 4.5 | $0.00006 | $0.00416 |
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 5d 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 — 447 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.
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 1-3: 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 skill automatically searches for job logs in the configured JOB_LOGS_DIR (set in .env).
Step 4: Read outputs and analyze with AgnosticD/V context
After running the CLI, read the generated files from the .analysis/ folder in this skill directory:
.analysis/<job-id>/step1_job_context.json
.analysis/<job-id>/step2_splunk_logs.json
.analysis/<job-id>/step3_correlation.json
Then perform enhanced analysis:
- Parse job metadata to identify platform/demo/env from job_name pattern
- Fetch AgnosticV configuration hierarchy from rhpds/agnosticv
- Fetch AgnosticD workload code from redhat-cop/agnosticd
- Apply investigation rules to identify root cause
- Generate summary with actionable recommendations
Provide a summary to the user with:
- Job Details: ID, status, GUID, namespace, platform, demo
- Failed Task(s): IMPORTANT - Preserve ALL fields from step1 failed_tasks:
- Task: task name
- Play: play name
- Role: role name
- Task Action: task_action (Ansible module)
- Error: error_message
- Task Duration: duration seconds
- Location: Both original task_path AND derived GitHub path
- Original:
/home/runner/.ansible/collections/...(from task_path field) - GitHub:
repository:file_path:line(parsed from original) - Example:
agnosticd/core_workloads:roles/ocp4_workload_gitops_bootstrap/tasks/workload.yml:74
- Original:
- Configuration Context: Relevant variables, missing vars, secrets references
- Correlation: How AAP logs link to Splunk pod logs (GUID, namespace, time overlap)
- Correlated Pods: Pods found in Splunk during the job window
- Root Cause: Analysis of why the job failed (configuration vs. infrastructure vs. workload bug)
- Recommendations: Specific file changes with paths and suggested values
What ships with it
10 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 3.6 KB
- 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 11 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/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.
- 5d ago First seen · 447 lines · 63 tokens per session scan A cfffc73fdf8d
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 4,162 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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