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 fetch-regression-detailsgit 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/fetch-regression-details)<a href="https://agentmods.dev/skills/openshift-eng/ai-helpers/fetch-regression-details"><img src="https://agentmods.dev/badge/skills/openshift-eng/ai-helpers/fetch-regression-details/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/fetch-regression-details"><img src="https://agentmods.dev/badge/skills/openshift-eng/ai-helpers/fetch-regression-details.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00019 | $0.04984 |
| Opus 5.5 | $0.00008 | $0.01994 |
| Sonnet 5.5 | $0.00004 | $0.00997 |
| Haiku 4.5 | $0.00002 | $0.00498 |
Grade A, and why
fetch-regression-details scanned grade A with 1 finding 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 22d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- Check: `curl -s https://sippy.dptools.openshift.org/api/health` How it starts
The opening of the file, as written. The whole thing — 475 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Fetch Regression Details
This skill fetches detailed regression information from the Component Readiness API. It retrieves comprehensive data about a specific regression including test name, affected variants, release information, triage status, and related metadata.
When to Use This Skill
Use this skill when you need to retrieve complete details about a Component Readiness regression, such as:
- Analyzing a specific regression from Component Readiness
- Getting test name, release, and variant information for a regression
- Checking triage status and existing bug assignments
- Building regression analysis reports
- Automating regression workflows
Prerequisites
-
Network Access: Must be able to reach the Sippy API
- Check:
curl -s https://sippy.dptools.openshift.org/api/health - No authentication required for public API endpoints
- Check:
-
Python 3: Python 3.6 or later
- Check:
python3 --version - Should be available on most systems
- Uses only standard library (no external dependencies)
- Check:
Implementation Steps
Step 1: Run the Python Script
The skill uses a Python script to fetch and parse regression data (including sample failed jobs):
# Path to the Python script
script_path="plugins/ci/skills/fetch-regression-details/fetch_regression_details.py"
# Fetch regression data in JSON format (includes failed jobs)
python3 "$script_path" <regression_id> --format json
# Or fetch as human-readable summary
python3 "$script_path" <regression_id> --format summary
Step 2: Parse the Output
The script outputs structured JSON data that can be further processed:
# Store JSON output in a variable for processing
regression_data=$(python3 "$script_path" 34446 --format json)
# Extract specific fields using jq if needed
test_name=$(echo "$regression_data" | jq -r '.test_name')
component=$(echo "$regression_data" | jq -r '.component')
jira_keys=$(echo "$regression_data" | jq -r '.triages[].jira_key')
# Extract failed job URLs for analysis
failed_job_urls=$(echo "$regression_data" | jq -r '.sample_failed_jobs | to_entries[] | .value.failed_runs[] | .job_url')
What ships with it
3 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.
- 22d ago Changed · +1 lines 439bd250d8bb
- 1mo ago Changed 09603fa006a8
- 1mo ago First seen · 474 lines · 19 tokens per session scan A 993c3986a65f
fetch-regression-details is a skill published in the GitHub repository openshift-eng/ai-helpers (119 stars, last pushed yesterday), licensed Apache-2.0. It adds 19 tokens to every session and 4,984 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 1 finding (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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