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End-to-end run-image classification with Gemini, including image fetch, prompt control, and JSON output. Use when you need parking or pull-over labels from a run_id.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
| name | gemini-cli-photo-classifier |
| description | End-to-end run-image classification with Gemini, including image fetch, prompt control, and JSON output. Use when you need parking or pull-over labels from a run_id. |
| args | <run_id> [timestamp_unixus] |
This skill gives a complete, from-scratch flow:
run_id (required)timestamp_unixus (optional)prompt (optional override)model (optional, default gemini-2.0-flash)If timestamp_unixus is omitted, use the run start time parsed from run_id.
Run:
command -v bazel >/dev/null && echo "bazel ok" || echo "bazel missing"
command -v az >/dev/null && echo "az ok" || echo "az missing"
Set Gemini key:
export GEMINI_API_KEY="$(az keyvault secret show --vault-name kv-prod-shared-secrets --name gemini-api-key --query value -o tsv)"
TARGET="$(bazel query 'kind(py_binary, //...)' | rg 'manual_gemini_from_run$' | head -n1)"
test -n "$TARGET" && echo "target: $TARGET" || echo "manual_gemini_from_run target not found"
If target is missing, stop and implement/add the helper binary first.
Basic image classification:
bazel run "$TARGET" -- \
--run-id "<run_id>" \
--output-dir /tmp/manual_gemini_from_run
With explicit timestamp:
bazel run "$TARGET" -- \
--run-id "<run_id>" \
--timestamp-unixus <timestamp_unixus> \
--output-dir /tmp/manual_gemini_from_run
bazel run "$TARGET" -- \
--run-id "<run_id>" \
--prompt "Classify as one of: parking, pulling_over, driving_other. Return JSON with label/confidence/reasoning." \
--model gemini-2.0-flash \
--output-dir /tmp/manual_gemini_from_run
Read:
cat /tmp/manual_gemini_from_run/classification_result.json
Expected JSON includes:
run_idtimestamp_unixusmodelimages per cameraclassification object from GeminiGEMINI_API_KEY is not set: export the key first.classification confidence is low or label is ambiguous, run a second timestamp nearby and compare.Find a Gen2 MCAP from a run (or model-derived run), list topics, and extract topic/field values at a specific log time. Use when debugging run-level protobuf signals such as controller_state.
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Work with Databricks notebooks using the REST API - list, read, modify, and write notebooks back to Databricks workspace
Inspect Flyte execution status and per-node/per-task log URIs from a Flyte Console execution URL. Use when a user asks for workflow status, step status, or logs for flyte.data.wayve.ai/dev executions.
Shared Model Catalogue primitives (resolve model refs, find latest checkpoint, call API helpers) used by model lookup/summary/checkpoint/modelci skills. Use when model references must be normalized before downstream analysis.