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jfriisj AI Skills

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Browse published AI skills maintained by jfriisj. Compare skill descriptions and follow each source repository for setup instructions.

Author statistics

Indexed skills

9

Repository stars across indexed skills

0

Repository forks

0

Average repository stars per indexed skill

0

Repository stars may repeat across skills from the same repository.

9 skills

Browse skills

GitHub

docker-logs-snapshot

jfriisj

Capture a quick, reproducible snapshot of Docker/Compose logs and container state for debugging. Use when smoke tests fail or you need attachable evidence.

GitHub

compose-bringup

jfriisj

Bring up a Docker Compose stack reliably and verify basic service/container status. Use for local smoke tests, live testing, and staging-like bring-up.

GitHub

http-health-smoke

jfriisj

Run quick HTTP health/readiness smoke checks against one or more endpoints. Use to validate a service is up before running deeper E2E tests.

GitHub

multipart-audio-smoke

jfriisj

Smoke test an audio upload endpoint using multipart/form-data. Use for quick end-to-end validation of transcription pipelines.

GitHub

synthetic-wav-sample

jfriisj

Generate a tiny non-sensitive WAV file using only the Python standard library. Use for deterministic smoke tests without committing real user audio.

GitHub

cpu-mode-assertions-torch

jfriisj

Assert CPU-only runtime inside a container using PyTorch (torch.cuda.is_available()==False) and optional env var checks. Use to prevent accidental GPU execution during CPU smoke tests.

GitHub

version-audit-quickscan

jfriisj

Quick scan for version strings across common files (README, pyproject, requirements, package manifests) to avoid release blockers from inconsistent versions. Use before tagging or publishing.

GitHub

python-pytest-run-capture

jfriisj

Run Python pytest suites and capture reproducible evidence (command, environment, output) into agent-output artifacts. Use for QA verification, debugging, and pre-release confidence.

The default order uses GitHub repository stars from our indexed data. Stars belong to the source repository and may be shared by multiple skills. They indicate repository interest, not skill installs or an independent quality score.