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 fetch-test-runsgit 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/fetch-test-runs)<a href="https://agentmods.dev/skills/wangke19/gemini-ai-helpers/fetch-test-runs"><img src="https://agentmods.dev/badge/skills/wangke19/gemini-ai-helpers/fetch-test-runs/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/fetch-test-runs"><img src="https://agentmods.dev/badge/skills/wangke19/gemini-ai-helpers/fetch-test-runs.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 | $0.00018 | $0.04036 |
| Opus 5 | $0.00009 | $0.02018 |
| Sonnet 5 | $0.00004 | $0.00807 |
| Haiku 4.5 | $0.00002 | $0.00404 |
Grade A, and why
Fetch Test Runs 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 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.
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/tests/v2/runs?test_id=test` This is a copy
92% identical to fetch-test-runs — 18 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 438 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Fetch Test Runs
This skill fetches test runs from the Sippy API. It can return both failed and successful test runs, including JUnit output for AI-based analysis.
When to Use This Skill
Use this skill when you need to:
- Fetch test run data for a specific test across all jobs
- Get raw test failure outputs for AI-based similarity analysis
- Compare error messages across runs to determine if they share the same root cause
- Include successful runs in addition to failures (optional)
- Filter runs by job name substrings (e.g., only GCP techpreview jobs)
- Access JUnit test output for debugging and investigation
Prerequisites
-
Network Access: Must be able to reach the Sippy test runs API
- No authentication required
- Check:
curl -s https://sippy.dptools.openshift.org/api/tests/v2/runs?test_id=test
-
Python 3: Python 3.6 or later
- Check:
python3 --version - Uses only standard library (no external dependencies)
- Check:
-
Input Data: Requires test_id (job_run_ids are optional)
- Get from
fetch-regression-detailsskill output test_id: Found in regression data (e.g., "openshift-tests:71c053c318c11cfc47717b9cf711c326")job_run_ids: Optional - extracted fromsample_failed_jobs[].failed_runs[].job_run_id
- Get from
Implementation Steps
Step 1: Run the Python Script
# Path to the Python script
script_path="extensions/ci/skills/fetch-test-runs/fetch_test_runs.py"
# Fetch all test runs (failures only, by default)
python3 "$script_path" "$test_id" --format json
# Include successful runs as well
python3 "$script_path" "$test_id" --include-success --format json
# Filter to a specific Prow job (exact name works as substring of itself)
python3 "$script_path" "$test_id" --job-contains "periodic-ci-openshift-release-..." --format json
# Filter by multiple substrings (AND logic, case-insensitive, server-side)
python3 "$script_path" "$test_id" --job-contains gcp --job-contains techpreview --format json
# Filter to specific job run IDs (backward compatible with analyze-regression)
python3 "$script_path" "$test_id" "$job_run_ids" --format json
# Get human-readable summary
python3 "$script_path" "$test_id" --format summary
What ships with it
2 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.
- 10d ago First seen · 438 lines · 18 tokens per session scan A 7ea136e6c4a4
Fetch Test Runs is a skill published in the GitHub repository wangke19/gemini-ai-helpers (2 stars, last pushed 5mo ago), licensed Apache-2.0. It adds 18 tokens to every session and 4,036 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). It is 92% identical to fetch-test-runs, differing in 18 lines, and is treated as a copy.
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