Automate Your Workflow with DirFind Scripts### Introduction
DirFind is a powerful, lightweight utility designed to quickly locate directories and files across large codebases and file systems. While many developers rely on built-in tools like find, ripgrep, or fd, DirFind offers a focused set of features tailored for directory-centric searches — making it ideal for tasks such as repository maintenance, build automation, deployment scripts, and developer tooling. This article explores how you can harness DirFind scripts to automate repetitive tasks, integrate with CI/CD pipelines, and create maintainable workflows.
Why automate with DirFind?
Automation reduces human error, speeds up repetitive tasks, and enforces consistency. DirFind excels in scenarios where directory structure matters more than individual files — for example, locating module folders, identifying stale build directories, or aggregating project subcomponents. Using DirFind within scripts provides:
- Faster directory discovery compared to manual searches.
- Repeatable results in pipelines and cron jobs.
- Easy integration with shell scripts, Makefiles, or CI systems.
- Flexible filtering using name patterns, depth limits, and path exclusions.
Basic DirFind usage patterns
Before automating, familiarize yourself with common DirFind patterns. Typical options include recursive search, pattern matching, depth control, and excluding paths. Example commands (syntax will vary by DirFind version):
-
Search for directories named “build” recursively:
dirfind -type d -name build -
Search up to depth 3 for directories containing “module”:
dirfind -maxdepth 3 -type d -name '*module*' -
Exclude vendor or node_modules directories:
dirfind -type d -name '*' -not -path '*/node_modules/*' -not -path '*/vendor/*'
Automating cleanup tasks
A common automation need is removing stale or temporary directories (e.g., build artifacts, cache dirs). Below is a robust shell script example that finds and optionally deletes “build” directories older than 7 days.
#!/usr/bin/env bash set -euo pipefail ROOT_DIR="${1:-.}" DRY_RUN="${2:-true}" # set to "false" to actually delete AGE_DAYS="${3:-7}" echo "Scanning ${ROOT_DIR} for build directories older than ${AGE_DAYS} days..." while IFS= read -r dir; do if [[ "${DRY_RUN}" == "true" ]]; then echo "[DRY RUN] Would remove: ${dir}" else echo "Removing: ${dir}" rm -rf -- "$dir" fi done < <(dirfind "${ROOT_DIR}" -type d -name build -mtime +"${AGE_DAYS}" -print) Notes:
- Use dry run first to verify matches.
- Be careful with rm -rf; restrict ROOT_DIR when testing.
Integrating DirFind into CI/CD pipelines
DirFind can help CI pipelines by locating build outputs, test results, or packaging artifacts across monorepos. Use DirFind to collect directories for caching, artifact upload, or targeted test runs.
Example GitLab CI job that caches build directories found by DirFind:
stages: - build - cache cache_job: stage: cache script: - | echo "Finding build directories..." dirfind . -type d -name build -not -path '*/node_modules/*' > build_dirs.txt tar -czf build_cache.tgz -T build_dirs.txt || true artifacts: paths: - build_cache.tgz expire_in: 1 week This approach:
- Dynamically captures build outputs across projects.
- Avoids hardcoding paths for monorepos with many packages.
Complex workflows: selective deployment
For deployments, you might want to deploy only services that changed. Combine DirFind with git to detect modified directories and deploy selectively.
Example script to deploy changed service directories:
#!/usr/bin/env bash set -euo pipefail BASE_DIR="${1:-.}" GIT_RANGE="${2:-HEAD~1..HEAD}" # Find directories that contain changes in the git range, then filter to service dirs git diff --name-only "${GIT_RANGE}" | awk -F/ '{print $1}' | sort -u > changed_roots.txt while IFS= read -r root; do # Verify it's a service directory (e.g., contains Dockerfile) if dirfind "${root}" -maxdepth 1 -type f -name Dockerfile -print -quit | grep -q .; then echo "Deploying service: ${root}" # replace with your deployment command ./deploy-service.sh "$root" fi done < changed_roots.txt This reduces deployment time by targeting only changed services in a monorepo.
Advanced filtering and parallel execution
For very large repositories, performance matters. Use DirFind with parallel execution and advanced filters to speed up processing. Example using xargs for parallel operations:
dirfind . -type d -name 'module-*' -print0 | xargs -0 -n1 -P8 -I{} bash -c 'echo "Processing {}"; ./analyze-module.sh "{}"' Tips:
- Tune -P (parallel jobs) to match CPU count and I/O capacity.
- Use -print0 and xargs -0 to handle spaces/newlines safely.
Logging, safety, and idempotence
When automating destructive actions, add safeguards:
- Always provide a dry-run mode.
- Log actions with timestamps.
- Use locks (flock) for cron/periodic jobs to avoid overlapping runs.
- Make scripts idempotent so reruns don’t cause harm.
Small example adding logging and flock:
#!/usr/bin/env bash exec 200>/var/lock/dirfind-clean.lock flock -n 200 || { echo "Another instance is running"; exit 1; } LOGFILE="/var/log/dirfind-clean.log" echo "$(date -Iseconds) Starting cleanup" >> "${LOGFILE}" # ... perform dirfind cleanup ... echo "$(date -Iseconds) Finished cleanup" >> "${LOGFILE}" Testing and continuous improvement
- Keep test repositories with known structures to validate scripts.
- Record metrics: how many directories processed, time taken, disk reclaimed.
- Iterate filters to reduce false positives.
Example: end-to-end automated workflow
- Nightly cron job runs DirFind to locate stale “tmp” and “cache” directories.
- Dry-run report emailed to admins for approval.
- After manual review, the script runs deletion with logging and archival of important artifacts.
- CI jobs use DirFind to dynamically collect build artifacts to cache between runs.
Conclusion
DirFind is a focused tool that simplifies directory-oriented automation across development and operations workflows. Combined with scripting, parallelism, and safe practices (dry runs, logging, locking), it can significantly reduce manual effort in large, heterogeneous codebases. Start small: write a dry-run script, integrate it into a test cron or pipeline, then expand to more complex selective deployments and artifact management as confidence grows.
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