Borrowing it
Nothing to install: this file belongs to cardstack/boxel. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/cardstack/boxel/main/.claude/skills/flaky-test-fix/SKILL.mdgit clone --depth 1 https://github.com/cardstack/boxelWrote 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/cardstack/boxel/flaky-test-fix)<a href="https://agentmods.dev/skills/cardstack/boxel/flaky-test-fix"><img src="https://agentmods.dev/badge/skills/cardstack/boxel/flaky-test-fix/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/cardstack/boxel/flaky-test-fix"><img src="https://agentmods.dev/badge/skills/cardstack/boxel/flaky-test-fix.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 | $0.00000 | $0.00993 |
| Opus 5.5 | $0.00000 | $0.00397 |
| Sonnet 5.5 | $0.00000 | $0.00199 |
| Haiku 4.5 | $0.00000 | $0.00099 |
Grade A, and why
flaky-test-fix 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 3d 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 — 64 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Flaky Test Fix
A flaky test reported by a CI URL is a request for five things, done in order: prior-art search → Linear issue → worktree → root-cause fix → diagnostic logging. Do not paper over the failure with a retry, an increased timeout, or a skip/todo unless the failure genuinely cannot be diagnosed without more data — and if so, lead with the diagnostics and say so explicitly.
Why this protocol
Flaky tests are uncertain by nature. A fix that looks correct in local repro often does not address the real race / ordering / resource issue that triggers it in CI. Two safeguards counter that:
- Prior-art search catches the common case where the same test has been "fixed" before. Without that context you risk re-doing a fix that already proved insufficient, or undoing an intentional accommodation.
- Diagnostic logging bundled with the fix means that if the fix misses the real root cause, the next CI failure produces enough signal to diagnose it — instead of burning another CI cycle staring at the same opaque output.
Procedure
1. Search for prior fix attempts first
Before touching anything, find out whether this test has been worked on before. Search by test name, file path, and failure signature:
gh search prs "<test name or file>" # searches open + closed by default
gh pr list --state=all --search "<test name or failure signature>"
git log --oneline -- <path/to/test/file>
If you find a prior PR, read its description, diff, and review thread before designing your fix. You may be looking at a regression, a partial fix, or a class of flakiness someone has already characterized. Surface any prior attempts early so the reporter can weigh in if the new fix should look materially different from the last.
2. Create a Linear issue for the flaky test
Unless the reporter already names a ticket, recommend a Linear issue that tracks the flake, and create it once they agree. Search Linear for the test name first: an existing issue for the same flake is updated, not duplicated. Give a new issue these settings, and set the state of an existing issue too:
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.
- 3d ago Changed · +13 lines db351fde4c66
- 5d ago First seen · 51 lines · 0 tokens per session scan A 6a5fc9a436f5
flaky-test-fix is a skill published in the GitHub repository cardstack/boxel (27 stars, last pushed today), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 993 tokens. 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 2026-10-21.
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