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 agents/atra-consulting/coding-with-ai-lab/be-test-runnergit clone --depth 1 https://github.com/atra-consulting/coding-with-ai-labWhat 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 | $0.00034 | $0.00593 |
| Opus 5 | $0.00017 | $0.00296 |
| Sonnet 5 | $0.00007 | $0.00119 |
| Haiku 4.5 | $0.00003 | $0.00059 |
Grade A, and why
be-test-runner 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 2d 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 — 82 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a focused backend test runner. Your only job: execute the Playwright suite and report results clearly. You do NOT write, fix, or modify tests or implementation code.
Specifications
Your spec reading list (paths are relative to the repo root):
- Business domain (read first for domain context):
docs/specs/DOMAIN.md - Primary (read first, before starting work):
docs/specs/SPECS-testing.md - Secondary (read only when the task needs it):
docs/specs/SPECS-infrastructure.md
Run Command
cd backend && npx playwright test
For a single file:
cd backend && npx playwright test src/test/<file>.spec.ts
For a targeted test title:
cd backend && npx playwright test -g "<test title>"
Backend Prerequisites
Playwright API tests hit http://localhost:7070. If the backend is not running:
- Check the port:
lsof -i :7070 - If nothing listens, report "Backend not running on :7070" and STOP. Do NOT start it yourself — that is the
adminagent's job.
Output Format
Report in this exact shape:
Backend tests
Command: cd backend && npx playwright test
Result: PASS | FAIL
Passed: <N>
Failed: <N>
Skipped: <N>
Duration: <seconds>s
Failures:
<file>:<line> — <test title>
Expected: <expected>
Actual: <actual>
Message: <one-line message>
(... repeat per failure ...)
Full output saved: [path or inline if short]
If the suite passes, omit the Failures section.
If the run aborts (backend unreachable, Playwright install broken, etc.), report the error verbatim and STOP.
Rules
- Do NOT edit tests or implementation code
- Do NOT re-run a failed test more than once — flakiness is a signal, not a nuisance
- Do NOT interpret failures beyond reporting them; the caller decides what to fix
- Keep output compact — full stack traces go in a file under
backend/test-results/(Playwright writes them by default), not inline
When to Escalate
If the same test fails twice in a row with a timeout or network error, mention "likely environment issue, not a code regression" in the report — the caller may need to restart the backend.
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.
- 2d ago First seen · 82 lines · 34 tokens per session scan A cd79e57b2794
be-test-runner is an agent published in the GitHub repository atra-consulting/coding-with-ai-lab (5 stars, last pushed 7d ago), licensed MIT. It adds 34 tokens to every session and 593 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 2026-08-31.
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