plugins/bash-master/skills/process-substitution-fifos/SKILL.md
Process substitution, named pipes (FIFOs), and advanced IPC patterns for efficient bash data streaming (2025). PROACTIVELY activate for: (1) using process substitution with <(cmd) and >(cmd), (2) creating named pipes (mkfifo) for inter-process streaming, (3) avoiding intermediate temp files in pipelines, (4) tee-ing into multiple consumers, (5) feeding diff/comm with command output, (6) building shell-only producers and consumers, (7) reading from a FIFO with timeout, (8) signal-safe FIFO cleanup. Provides: process substitution patterns, mkfifo recipes, multi-consumer tee patterns, and signal-safe cleanup with trap.
npx skillsauth add JosiahSiegel/claude-plugin-marketplace process-substitution-fifosInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Master advanced inter-process communication patterns in bash using process substitution, named pipes (FIFOs), and efficient data streaming techniques. These patterns enable powerful data pipelines without temporary files.
<(command)#!/usr/bin/env bash
set -euo pipefail
# Compare two command outputs
diff <(sort file1.txt) <(sort file2.txt)
# Compare remote and local files
diff <(ssh server 'cat /etc/config') /etc/config
# Merge sorted files
sort -m <(sort file1.txt) <(sort file2.txt) <(sort file3.txt)
# Read from multiple sources simultaneously
paste <(cut -f1 data.tsv) <(cut -f3 data.tsv)
# Feed command output to programs expecting files
# Many programs require filename arguments, not stdin
wc -l <(grep "error" *.log)
# Process API response with tool expecting file
jq '.items[]' <(curl -s "https://api.example.com/data")
# Source environment from command output
source <(aws configure export-credentials --format env)
# Feed to while loop without subshell issues
while IFS= read -r line; do
((count++))
process "$line"
done < <(find . -name "*.txt")
echo "Processed $count files" # Variable survives!
>(command)#!/usr/bin/env bash
set -euo pipefail
# Write to multiple destinations simultaneously (tee alternative)
echo "Log message" | tee >(logger -t myapp) >(mail -s "Alert" [email protected])
# Compress and checksum in one pass
tar cf - /data | tee >(gzip > backup.tar.gz) >(sha256sum > backup.sha256)
# Send output to multiple processors
generate_data | tee >(processor1 > result1.txt) >(processor2 > result2.txt) > /dev/null
# Log and process simultaneously
./build.sh 2>&1 | tee >(grep -i error > errors.log) >(grep -i warning > warnings.log)
# Real-time filtering with multiple outputs
tail -f /var/log/syslog | tee \
>(grep --line-buffered "ERROR" >> errors.log) \
>(grep --line-buffered "WARNING" >> warnings.log) \
>(grep --line-buffered "CRITICAL" | mail -s "Critical Alert" [email protected])
#!/usr/bin/env bash
set -euo pipefail
# Transform and compare
diff <(sort input.txt | uniq) <(sort reference.txt | uniq)
# Pipeline with multiple branches
cat data.csv | tee \
>(awk -F, '{print $1}' > column1.txt) \
>(awk -F, '{print $2}' > column2.txt) \
| wc -l
# Complex data flow
process_data() {
local input="$1"
# Read from process substitution, write to multiple outputs
while IFS= read -r line; do
echo "$line" | tee \
>(echo "LOG: $line" >> "$log_file") \
>(process_line "$line" >> results.txt)
done < <(cat "$input" | filter_input)
}
#!/usr/bin/env bash
set -euo pipefail
# Create FIFO
mkfifo my_pipe
# Clean up on exit
trap 'rm -f my_pipe' EXIT
# Writer (in background or separate terminal)
echo "Hello from writer" > my_pipe &
# Reader (blocks until data available)
cat < my_pipe
# With timeout (using read)
if read -t 5 line < my_pipe; then
echo "Received: $line"
else
echo "Timeout waiting for data"
fi
#!/usr/bin/env bash
set -euo pipefail
# Create two FIFOs for bidirectional communication
REQUEST_PIPE="/tmp/request_$$"
RESPONSE_PIPE="/tmp/response_$$"
mkfifo "$REQUEST_PIPE" "$RESPONSE_PIPE"
trap 'rm -f "$REQUEST_PIPE" "$RESPONSE_PIPE"' EXIT
# Server process
server() {
while true; do
if read -r request < "$REQUEST_PIPE"; then
case "$request" in
"QUIT")
echo "BYE" > "$RESPONSE_PIPE"
break
;;
"TIME")
date > "$RESPONSE_PIPE"
;;
"UPTIME")
uptime > "$RESPONSE_PIPE"
;;
*)
echo "UNKNOWN: $request" > "$RESPONSE_PIPE"
;;
esac
fi
done
}
# Client function
send_request() {
local request="$1"
echo "$request" > "$REQUEST_PIPE"
cat < "$RESPONSE_PIPE"
}
# Start server in background
server &
SERVER_PID=$!
# Send requests
send_request "TIME"
send_request "UPTIME"
send_request "QUIT"
wait "$SERVER_PID"
#!/usr/bin/env bash
set -euo pipefail
WORK_QUEUE="/tmp/work_queue_$$"
mkfifo "$WORK_QUEUE"
trap 'rm -f "$WORK_QUEUE"' EXIT
# Producer
producer() {
local item
for item in {1..100}; do
echo "TASK:$item"
done
echo "DONE"
}
# Consumer (can have multiple)
consumer() {
local id="$1"
while read -r item; do
[[ "$item" == "DONE" ]] && break
echo "Consumer $id processing: $item"
sleep 0.1 # Simulate work
done
}
# Start consumers (they'll block waiting for data)
consumer 1 < "$WORK_QUEUE" &
consumer 2 < "$WORK_QUEUE" &
consumer 3 < "$WORK_QUEUE" &
# Start producer
producer > "$WORK_QUEUE"
wait
echo "All work complete"
#!/usr/bin/env bash
set -euo pipefail
FIFO="/tmp/fd_fifo_$$"
mkfifo "$FIFO"
trap 'rm -f "$FIFO"' EXIT
# Open FIFO for read/write on FD 3
# Opening for both prevents blocking on open
exec 3<>"$FIFO"
# Write to FIFO via FD
echo "Message 1" >&3
echo "Message 2" >&3
# Read from FIFO via FD
read -r msg1 <&3
read -r msg2 <&3
echo "Got: $msg1, $msg2"
# Close FD
exec 3>&-
#!/usr/bin/env bash
set -euo pipefail
# Start coprocess (bidirectional pipe)
coproc BC { bc -l; }
# Send data to coprocess
echo "scale=10; 355/113" >&"${BC[1]}"
# Read result
read -r result <&"${BC[0]}"
echo "Pi approximation: $result"
# More calculations
echo "sqrt(2)" >&"${BC[1]}"
read -r sqrt2 <&"${BC[0]}"
echo "Square root of 2: $sqrt2"
# Close write end to signal EOF
exec {BC[1]}>&-
# Wait for coprocess to finish
wait "$BC_PID"
#!/usr/bin/env bash
set -euo pipefail
# Named coprocess for Python interpreter
coproc PYTHON { python3 -u -c "
import sys
for line in sys.stdin:
exec(line.strip())
"; }
# Send Python commands
echo "print('Hello from Python')" >&"${PYTHON[1]}"
read -r output <&"${PYTHON[0]}"
echo "Python said: $output"
echo "print(2**100)" >&"${PYTHON[1]}"
read -r big_num <&"${PYTHON[0]}"
echo "2^100 = $big_num"
# Cleanup
exec {PYTHON[1]}>&-
wait "$PYTHON_PID" 2>/dev/null || true
#!/usr/bin/env bash
set -euo pipefail
# Create pool of worker coprocesses
declare -A WORKERS
declare -A WORKER_PIDS
start_workers() {
local count="$1"
local i
for ((i=0; i<count; i++)); do
# Each worker runs a processing loop
coproc "WORKER_$i" {
while IFS= read -r task; do
[[ "$task" == "QUIT" ]] && exit 0
# Simulate work
sleep 0.1
echo "DONE:$task"
done
}
# Store FDs dynamically
local -n write_fd="WORKER_${i}[1]"
local -n read_fd="WORKER_${i}[0]"
local -n pid="WORKER_${i}_PID"
WORKERS["$i,in"]="$write_fd"
WORKERS["$i,out"]="$read_fd"
WORKER_PIDS["$i"]="$pid"
done
}
# Note: Coprocess pool management is complex
# Consider GNU Parallel for production workloads
Detailed recipes for multi-source comparison, tee-style stream splitting, process-substitution-driven diffing, streaming transforms, temporary FIFOs, and cleanup-heavy pipeline compositions live in references/advanced-patterns.md. Load that reference when basic <(...), >(...), named pipes, or coprocess examples are not enough.
#!/usr/bin/env bash
# Requires Bash 5.3+
set -euo pipefail
# Traditional: forks subshell
result=$(echo "hello")
# Bash 5.3: No fork, runs in current shell
result=${ echo "hello"; }
# Significant for variable modifications
counter=0
# Traditional - counter stays 0 (subshell)
result=$(counter=$((counter + 1)); echo "$counter")
echo "Counter: $counter" # Still 0
# Bash 5.3 - counter is modified (same shell)
result=${ counter=$((counter + 1)); echo "$counter"; }
echo "Counter: $counter" # Now 1
# REPLY variable syntax (even more concise)
${ REPLY="computed value"; }
echo "$REPLY"
# Or using ${| } syntax
${| REPLY=$(expensive_computation); }
echo "Result: $REPLY"
#!/usr/bin/env bash
# Requires Bash 5.3+
set -euo pipefail
# Build result without forks
build_path() {
local parts=("$@")
local result=""
for part in "${parts[@]}"; do
# No fork for each concatenation
result=${ printf '%s/%s' "$result" "$part"; }
done
echo "${result#/}"
}
# Accumulate values efficiently
accumulate() {
local -n arr="$1"
local sum=0
for val in "${arr[@]}"; do
# In-shell arithmetic capture
sum=${ echo $((sum + val)); }
done
echo "$sum"
}
#!/usr/bin/env bash
set -euo pipefail
# Check all pipeline stages
run_pipeline() {
local result
# pipefail ensures we catch errors in any stage
if ! result=$(stage1 | stage2 | stage3); then
echo "Pipeline failed" >&2
return 1
fi
echo "$result"
}
# PIPESTATUS for detailed error info
run_with_status() {
cmd1 | cmd2 | cmd3
local -a status=("${PIPESTATUS[@]}")
for i in "${!status[@]}"; do
if [[ "${status[$i]}" -ne 0 ]]; then
echo "Stage $i failed with status ${status[$i]}" >&2
fi
done
# Return highest exit status
local max=0
for s in "${status[@]}"; do
((s > max)) && max="$s"
done
return "$max"
}
#!/usr/bin/env bash
set -euo pipefail
# Track resources for cleanup
declare -a CLEANUP_PIDS=()
declare -a CLEANUP_FILES=()
cleanup() {
local pid file
for pid in "${CLEANUP_PIDS[@]}"; do
kill "$pid" 2>/dev/null || true
done
for file in "${CLEANUP_FILES[@]}"; do
rm -f "$file" 2>/dev/null || true
done
}
trap cleanup EXIT
# Register cleanup
register_pid() { CLEANUP_PIDS+=("$1"); }
register_file() { CLEANUP_FILES+=("$1"); }
# Example usage
run_safe_pipeline() {
local fifo="/tmp/pipeline_$$"
mkfifo "$fifo"
register_file "$fifo"
producer > "$fifo" &
register_pid "$!"
consumer < "$fifo" &
register_pid "$!"
wait
}
#!/usr/bin/env bash
set -euo pipefail
# Include PID and descriptive name
create_fifo() {
local name="$1"
local fifo="/tmp/${name}_$$_$(date +%s)"
mkfifo -m 600 "$fifo" # Restrictive permissions
echo "$fifo"
}
# Use tmpdir for security
create_secure_fifo() {
local name="$1"
local tmpdir
tmpdir=$(mktemp -d)
local fifo="$tmpdir/$name"
mkfifo -m 600 "$fifo"
echo "$fifo"
}
#!/usr/bin/env bash
set -euo pipefail
# ✗ DEADLOCK - writer blocks, reader never starts
# mkfifo pipe
# echo "data" > pipe # Blocks forever
# ✓ SAFE - open both ends or use background
mkfifo pipe
trap 'rm -f pipe' EXIT
# Option 1: Background writer
echo "data" > pipe &
cat < pipe
# Option 2: Open for read/write
exec 3<>pipe
echo "data" >&3
read -r data <&3
exec 3>&-
# Option 3: Non-blocking open (requires careful handling)
exec 3<pipe &
exec 4>pipe
echo "data" >&4
read -r data <&3
#!/usr/bin/env bash
set -euo pipefail
# Read with timeout
read_with_timeout() {
local fifo="$1"
local timeout="$2"
local result
if read -t "$timeout" -r result < "$fifo"; then
echo "$result"
return 0
else
echo "Timeout after ${timeout}s" >&2
return 1
fi
}
# Write with timeout (using timeout command)
write_with_timeout() {
local fifo="$1"
local timeout="$2"
local data="$3"
if timeout "$timeout" bash -c "echo '$data' > '$fifo'"; then
return 0
else
echo "Write timeout after ${timeout}s" >&2
return 1
fi
}
Master process substitution and FIFOs for efficient inter-process communication without temporary files.
development
This skill should be used when the user asks to train, debug, scale, or improve ML models. PROACTIVELY activate for: (1) PyTorch, TensorFlow/Keras, JAX, Flax, Hugging Face Trainer/Accelerate training loops, (2) distributed training, DDP/FSDP/DeepSpeed, TPU/GPU setup, (3) mixed precision AMP/bf16, gradient accumulation, checkpointing, seeding, (4) overfitting, imbalance, loss functions, regularization, LR schedules, warmup, (5) memory optimization, gradient checkpointing, offloading, quantization-aware training. Provides: reproducible training best practices across deep learning and classical ML.
development
This skill should be used when the user asks to productionize, track, version, govern, monitor, or automate ML systems. PROACTIVELY activate for: (1) MLflow, Weights & Biases, Neptune, Comet, ClearML experiment tracking, (2) model registry, model versioning, artifact lineage, reproducibility, (3) Kubeflow, SageMaker Pipelines, Vertex AI Pipelines, Azure ML pipelines, Databricks workflows, (4) CI/CD, continuous training/evaluation, A/B tests, canary/shadow deployments, (5) drift detection, model monitoring, data validation, responsible AI governance. Provides: end-to-end MLOps architecture and operational safeguards.
development
This skill should be used when the user asks to optimize, export, serve, compress, or accelerate ML inference. PROACTIVELY activate for: (1) latency, throughput, p95/p99, batching, concurrency, KV cache, memory, or cost issues, (2) quantization INT8/INT4, GPTQ, AWQ, bitsandbytes, pruning, sparsity, distillation, (3) ONNX export, ONNX Runtime, TensorRT, TorchScript, torch.compile, XLA, OpenVINO, Core ML, TFLite, (4) Triton, TorchServe, TF Serving, BentoML, Seldon, KServe configuration, (5) edge deployment, CPU/GPU/TPU/Inferentia serving. Provides: hardware-aware inference optimization and safe benchmarking.
testing
This skill should be used when the user asks to tune hyperparameters, run sweeps, optimize search spaces, or use AutoML. PROACTIVELY activate for: (1) Optuna, Ray Tune, FLAML, AutoGluon, Hyperopt, Nevergrad, KerasTuner, W&B sweeps, (2) grid search, random search, Bayesian optimization, TPE, Gaussian processes, evolutionary search, (3) ASHA, Hyperband, successive halving, multi-fidelity optimization, population-based training, (4) learning-rate finder, batch-size search, early stopping, pruning, (5) reproducible sweep design and experiment analysis. Provides: budget-aware hyperparameter search strategy.