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 cenconq25/claude-code-app-studio --skill test-flakinessgit clone --depth 1 https://github.com/cenconq25/claude-code-app-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/cenconq25/claude-code-app-studio/test-flakiness)<a href="https://agentmods.dev/skills/cenconq25/claude-code-app-studio/test-flakiness"><img src="https://agentmods.dev/badge/skills/cenconq25/claude-code-app-studio/test-flakiness/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/cenconq25/claude-code-app-studio/test-flakiness"><img src="https://agentmods.dev/badge/skills/cenconq25/claude-code-app-studio/test-flakiness.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 | $0.00052 | $0.01558 |
| Opus 5 | $0.00026 | $0.00779 |
| Sonnet 5 | $0.00010 | $0.00312 |
| Haiku 4.5 | $0.00005 | $0.00156 |
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 6d 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 — 212 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Test Flakiness
Locate tests whose result depends on timing, ordering, or hidden state rather than on the system under test. Maintain a registry of known flakes with quarantine status and fix targets.
Phase 1: Source CI History
Determine where test history lives. Try in order:
.test-history/directory — JSON or JUnit XML reports stored locally.ghCLI — fetch recent CI runs:gh run list --workflow=test.yml --limit=N --json conclusion,databaseId- Bitrise API — if configured, fetch recent builds.
- CircleCI API — if configured.
- Manual import — if none of the above, ask the user to drop JUnit XML
files into
.test-history/and rerun.
Default window: last 30 days OR last 50 runs, whichever yields more data.
Override via --days or --runs.
If history is unreachable, stop and tell the user how to enable it.
Phase 2: Parse Results
For each retrieved run, extract:
- Run ID
- Date
- Branch
- Commit SHA
- Per-test result (
pass/fail/error/skipped) - Per-test duration
Aggregate per test:
- Total runs
- Pass count
- Fail count
- Pass rate
- Standard deviation of duration
- First failure date, last failure date
Phase 3: Classify
Apply these thresholds:
| Class | Criterion |
|---|---|
| HEALTHY | Pass rate 100% over the window. |
| FLAKY | Pass rate >= 50% but < 100%, with at least one fail and one pass on the same SHA. |
| BROKEN | Pass rate < 50% — likely a real regression, not flakiness. |
| SLOW | Duration std-dev > 2x median, even if pass rate is 100%. |
| DEAD | Skipped on every run in the window. |
Surface FLAKY first; that is the target of this skill.
Phase 4: Gather Context for Each Flaky Test
For each FLAKY entry, read the test file and look for:
- Direct timing dependencies —
setTimeout,Thread.sleep,Future.delayed, hardcoded animation duration waits. - Real network or file I/O.
- Order-dependence — module-scope mutable state, missing teardown, reliance on previous test's side effects.
- Real device clock —
Date(),DateTime.now(),Date.now()without injection. - Real randomness — unseeded
Math.random,Random(). - Concurrency — uncontrolled
await Promise.all(...), async/await without explicit synchronization. - E2E specific — fixed waits, hardcoded element coordinates, missing retry on flakey selectors.
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.
- 6d ago First seen · 212 lines · 52 tokens per session scan A d056b49d026c
test-flakiness is a skill published in the GitHub repository cenconq25/claude-code-app-studio (40 stars, last pushed 4mo ago), licensed MIT. It adds 52 tokens to every session and 1,558 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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