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/openshift-eng/ai-helpers/ci-data-analystnpx skills add openshift-eng/ai-helpers --skill ci-data-analystgit 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/ci-data-analyst)<a href="https://agentmods.dev/skills/openshift-eng/ai-helpers/ci-data-analyst"><img src="https://agentmods.dev/badge/skills/openshift-eng/ai-helpers/ci-data-analyst.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 | $0.00036 | $0.05135 |
| Opus 5 | $0.00018 | $0.02567 |
| Sonnet 5 | $0.00007 | $0.01027 |
| Haiku 4.5 | $0.00004 | $0.00513 |
Grade A, and why
ci-data-analyst 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 — 409 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CI Data Analyst
An agent for querying and analyzing OpenShift CI data stored in BigQuery. Covers prow job runs, junit test results, job variants, and related tables. Prioritizes cost safety: every query is dry-run first, costs are estimated, and the user confirms before execution.
When to Use This Skill
- Investigating test failures, flakes, or regressions across CI jobs
- Querying prow job pass/fail rates over time
- Analyzing test results by variant (platform, architecture, network, upgrade, etc.)
- Exploring CI data patterns (e.g. which jobs run a test, how often a test fails)
- Any ad-hoc BigQuery analysis against OpenShift CI datasets
Prerequisites
bqCLI installed and authenticated (gcloud auth login)- BigQuery read access to the
openshift-gce-develproject
Core Principles
1. Cost Safety Is Non-Negotiable
The junit table alone is massive. Every query MUST go through this flow:
- Show the query to the user before doing anything
- Dry-run to get bytes scanned:
bq query --project_id=openshift-gce-devel --dry_run --use_legacy_sql=false '<query>' - Calculate cost: bytes / 10^12 * $6.25 (on-demand pricing)
- If cost > $1.00: show the estimated cost and bytes scanned, ask user to confirm before executing
- If cost <= $1.00: proceed, but still report the cost in results
- Never loop or repeat queries. Run once, cache locally, analyze from the cached data.
2. Always Use Partition and Clustering Filters
Both key tables are partitioned by day:
junit: partitioned onmodified_time, clustered onreleasejobs: partitioned onprowjob_start
Every query MUST include a filter on the partition column to avoid full-table scans. Use tight date ranges.
Every junit query targeting a specific release MUST filter on release to activate clustering pruning. This reduces bytes scanned by 60-70%:
WHERE modified_time >= DATETIME("2026-05-01")
AND modified_time < DATETIME("2026-05-08")
AND release = '4.22'
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 · 409 lines · 36 tokens per session scan A c9ceb5e35ea1
ci-data-analyst is a skill published in the GitHub repository openshift-eng/ai-helpers (116 stars, last pushed today), licensed Apache-2.0. It adds 36 tokens to every session and 5,135 once invoked, about $0.0002 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 2026-08-30.
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