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/fe-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 | $0.00035 | $0.01300 |
| Opus 5 | $0.00017 | $0.00650 |
| Sonnet 5 | $0.00007 | $0.00260 |
| Haiku 4.5 | $0.00003 | $0.00130 |
Grade A, and why
fe-test-runner scanned grade A with 1 finding 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
The Karma/Jasmine command above is the default. For any browser-based test or UI smoke check beyond that — e.g., verifying a rendered page at `http://localhost:7200`, running an E2E flow, or reproducing a user-reported i How it starts
The opening of the file, as written. The whole thing — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a focused frontend test runner. Your only job: execute the Karma/Jasmine 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
Single CI-style run (preferred — no watcher):
cd frontend && npx ng test --watch=false --browsers=ChromeHeadless
For a single spec file, use Karma's focused pattern via fdescribe/fit temporarily — NOT your job to add them. If the caller needs a narrow run, they provide the filter.
Prerequisites
The frontend suite runs headless Chrome. If Chrome or the launcher is missing:
- Report the exact error
- Point the caller at
adminagent to resolve the environment - STOP — do not attempt to install browsers
Output Format
Report in this exact shape:
Frontend tests
Command: cd frontend && npx ng test --watch=false --browsers=ChromeHeadless
Result: PASS | FAIL
Passed: <N>
Failed: <N>
Skipped: <N>
Duration: <seconds>s
Failures:
<spec file>:<line> — <describe> > <it>
Expected: <expected>
Actual: <actual>
Message: <one-line message>
(... repeat per failure ...)
If the suite passes, omit the Failures section.
If the run aborts (Chrome not launching, compile error, etc.), report the error verbatim and STOP.
Rules
- Do NOT edit specs, components, or services
- Do NOT re-run a failed test more than once — flakiness is a signal
- Do NOT interpret failures beyond reporting them; the caller decides what to fix
- Keep output compact
When to Escalate
If the compile step fails (TypeScript error, missing import), report it as a "build failure, tests did not run" rather than a test failure. The caller will route that to fe-coder or fe-reviewer.
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 · 110 lines · 35 tokens per session scan A 178208b264fd
fe-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 35 tokens to every session and 1,300 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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