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 frabcd/codex-ai-game-studio --skill test-flakinessgit clone --depth 1 https://github.com/frabcd/codex-ai-game-studioWrote 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/frabcd/codex-ai-game-studio/test-flakiness)<a href="https://agentmods.dev/skills/frabcd/codex-ai-game-studio/test-flakiness"><img src="https://agentmods.dev/badge/skills/frabcd/codex-ai-game-studio/test-flakiness.svg" alt="Measured on agentmods" 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 | $0.00059 | $0.02081 |
| Opus 5 | $0.00030 | $0.01040 |
| Sonnet 5 | $0.00012 | $0.00416 |
| Haiku 4.5 | $0.00006 | $0.00208 |
Grade A, and why
test-flakiness 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 — 214 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Port provenance: adapted from the pinned upstream source at
984023ddac0d5e27624f2baacde6105e45de375funder MIT; see the repository parity ledger for the exact path and blob.
Test Flakiness Detection
A flaky test is one that sometimes passes and sometimes fails without any code change. Flaky tests are worse than no tests in some ways — they train the team to ignore red CI runs, masking genuine failures. This skill identifies them, explains likely causes, and recommends whether to quarantine or fix each one.
Output: Updated tests/regression-suite.md quarantine section + optional
production/qa/flakiness-report-[date].md
When to run:
- Polish phase (tests have had many runs; statistical signal is reliable)
- When developers start dismissing CI failures as "probably flaky"
- After
$ai-game-studio:regression-suiteidentifies quarantined tests that need diagnosis
1. Parse Arguments
Modes:
$ai-game-studio:test-flakiness [ci-log-path]— analyse a specific CI run log file$ai-game-studio:test-flakiness scan— scan all available CI logs in.github/or standard log output directories$ai-game-studio:test-flakiness registry— read existing regression-suite.md quarantine section and provide remediation guidance for already-known flaky tests- No argument — auto-detect: run
scanif CI logs are accessible, elseregistry
2. Locate CI Log Data
Option A — GitHub Actions (preferred)
Check for test result artifacts:
ls -t .github/ 2>/dev/null
ls -t test-results/ 2>/dev/null
For Godot projects: GdUnit4 outputs XML results compatible with JUnit format.
Check test-results/ for .xml files.
For Unity projects: game-ci test runner outputs NUnit XML to test-results/
by default.
For Unreal projects: automation logs go to Saved/Logs/. text search for
Result: Success and Result: Fail patterns.
Option B — Local log files
If a path argument is provided, read that file directly.
Option C — No log data available
What ships with it
1 file 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.
- 4d ago First seen · 214 lines · 59 tokens per session scan A da599475709b
test-flakiness is a skill published in the GitHub repository frabcd/codex-ai-game-studio (9 stars, last pushed yesterday), licensed MIT. It adds 59 tokens to every session and 2,081 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 2026-09-03.
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Investigates a specific CI failure to a verdict: whose fault, which commit, who wrote it, and whether it's fixed. Use for "who broke master", "why did this test fail in CI", "is this failure my PR's fault or everyone's", "is this test flaky or actually broken", "when did this failure start". Works from the…
capture-failure
Turn a captured Codex tool failure into reviewed .tape evidence and an offline regression test. Use when the user asks to inspect a failed run, fork captured evidence, save a regression, or prepare AgentTape evidence for CI.
investigating-bugs
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
systematic-debugging
4-phase root cause debugging: understand bugs before fixing.
experimental-code-coverage-local-debugger
Runs code coverage locally via Universal Test Runner (UTR) or helper scripts, mimicking LUCI trybots. Activate when CQ tryjobs fail or underreport coverage, to test local GN/recipe repairs before uploading, or to debug hermetic crashes.