Analyze oversized logs with natural-language queries using Gemini. Extract matching original lines with line numbers, produce a Markdown summary, or combine both steps to focus a report on relevant errors and events.
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Use scripts/logscan.py only when explicitly requested to scan an oversized log or to perform Gemini-backed log analysis.
安装前可以查看交给 AI 代理的指令内容。
grep finds matching original lines; summarize produces a Markdown report.
They can run independently or as a pipeline.
GEMINI_API_KEY must be set. The log is sent to an external Gemini API and
incurs model usage; do not send secrets or sensitive logs without approval.
Returns line-numbered original lines matching a natural-language query.
uv run scripts/logscan.py grep <logfile> "<query>"
Summarizes the log around the query, hierarchically reducing large inputs.
uv run scripts/logscan.py summarize <logfile> "<query>"
Output is a markdown report on stdout.
Narrow first, then summarize through --stdin:
uv run scripts/logscan.py grep log.txt "errors" \
| uv run scripts/logscan.py summarize --stdin "summarize these errors"
Behavior-changing options are --chunk-tokens, --concurrency, --model,
--stdin, and --verbose. The defaults are 5,000-token grep chunks,
50,000-token hierarchical summarize chunks, concurrency 16, and
gemini-2.5-flash-lite. Smaller grep chunks favor precise line matching;
large logs need no manual pre-split. Inspect --help before overriding them.
Both modes report progress and token usage to stderr. Read the matched source lines before accepting a generated diagnosis.
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Compare Zephyr changes using the same prepared dataset and matching execution settings. Analyze Finelog metrics for each processing stage to distinguish CPU efficiency, elapsed time, and peak memory changes, then report improvements, regressions, or tradeoffs.
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Register Hugging Face datasets for Marin by inspecting their structure without downloading the full dataset. Identify text fields, configurations, and data splits, then add a dataset module and validate the mapping with a sample.
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Turn a specified GCP access request into a reviewable Pulumi change and pull request. Select the narrowest suitable role and encrypt personal email identities, covering resource permissions and Cloud Run viewer access through IAP, an access gateway.
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Add or update Pallas/Mosaic kernels, specialized computation routines for JAX. The skill covers reference implementations, output and gradient checks, public wrappers, fallback behavior, performance benchmarks, and hardware-specific tuning when requested.
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Preserve old experiment scripts with a dated Git tag, a named snapshot of the code, and add comments linking related GitHub issues to the archived files and cleanup pull request.
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Launch and continuously monitor a specified Zephyr pipeline on Iris when asked. It checks coordinator and worker health, tracks stage progress, identifies stalled or repeated failures, and requires explicit approval before stopping or restarting jobs.
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