Summarizes application log files by severity, time range and the most frequent error messages. Use when the user shares a log file or pasted log output and asks what went wrong, which errors repeat, or how noisy the log is.
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Reads plain text logs and produces a structured summary so the agent can reason about the incident instead of scrolling through thousands of lines.
python scripts/analyze_log.py --top 5 app.log
cat app.log | python scripts/analyze_log.py
The script reads the file given as the last argument, or standard input when no file is given.
A JSON object:
total_lines and levels: a count per severity (CRITICAL, ERROR, WARN, INFO, DEBUG).first_timestamp and last_timestamp: the earliest and latest ISO style timestamps found, or null.top_errors: the most frequent error messages as {"message", "count"}. Numbers, hex ids and quoted
values are replaced with placeholders so that similar errors group together.levels. A high ratio of ERROR to INFO points to a sustained failure, not a one-off.first_timestamp with the time the user reports the problem.top_errors and search the original log for its raw form to get the stack trace.Input line: 2024-05-01 10:00:02 ERROR db timeout after 30s on host 10.0.0.5
The message is grouped as db timeout after <n>s on host <n>.<n>.<n>.<n>.
A missing file exits with code 2 and file not found on stderr. Empty input returns zero counts.
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Runs a list of HTTP checks against a base URL from a JSON file and reports status codes, response time and body assertions. Use when the user asks whether an API is up, wants a smoke test after a deploy, or needs to verify that endpoints return what the docs promise.
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Turns git log output into a grouped markdown changelog using conventional commit prefixes such as feat, fix and docs. Use when the user prepares a release, asks for release notes, or wants a summary of what changed between two tags.
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Writes clear git commit messages in the conventional commits style from a diff or a description of the change. Use when the user asks for a commit message, wants to tidy up a message before pushing, or has staged changes and no wording yet.
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Profiles a CSV file column by column, reporting inferred type, missing values, unique counts and min, max and mean for numeric columns. Use when the user hands over a CSV and asks what is in it, whether the data is clean, or what to check before analysis.
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Checks a Dockerfile for common problems such as unpinned base images, running as root, ADD instead of COPY, secrets in ENV or ARG and apt-get without cleanup. Use when the user asks to review, harden or slim down a Dockerfile.
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Compares a .env file with its .env.example template and reports missing keys, undocumented extra keys, empty values and example files that contain real looking secrets. Use when the user asks why an app fails on startup with missing configuration, or wants to check environment files before committing.
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