#!/bin/bash
# ensure-daemon.sh

if ! whisp status > /dev/null 2>&1; then
    whisp start
    sleep 1
fi

#!/bin/bash
# deploy-helper.sh

# Source whisp shell integration
source ~/.bashrc  # or wherever whisp is loaded

# Generate a command and capture it
cmd=$(whisp shell query "list all docker containers older than 7 days")
echo "Generated: $cmd"

# Execute if confirmed
read -p "Run this command? [y/N] " confirm
if [[ "$confirm" == "y" ]]; then
    eval "$cmd"
fi

#!/bin/bash
# analyze-logs.sh

# Find error patterns
cat /var/log/app.log | whisp shell pipe "extract unique error types and count them"

# Summarize git history
git log --oneline -50 | whisp shell pipe "categorize these commits by type"

# .github/workflows/ai-review.yml
name: AI Code Review

on: [pull_request]

jobs:
  review:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      - name: Install whisp
        run: |
          curl -fsSL https://whisp.dev/install.sh | sh
          echo "$HOME/.local/bin" >> $GITHUB_PATH

      - name: Configure whisp
        env:
          OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
        run: |
          whisp start
          sleep 2

      - name: Analyze changes
        run: |
          git diff origin/main...HEAD | whisp shell pipe "review this diff for potential issues"

# .gitlab-ci.yml
ai-analysis:
  image: rust:latest
  script:
    - curl -fsSL https://whisp.dev/install.sh | sh
    - export PATH="$HOME/.local/bin:$PATH"
    - export OPENAI_API_KEY=$OPENAI_API_KEY
    - whisp start && sleep 2
    - git diff $CI_MERGE_REQUEST_DIFF_BASE_SHA | whisp shell pipe "summarize changes"

#!/bin/bash
# .git/hooks/pre-commit

# Check if whisp is available
if ! command -v whisp &> /dev/null; then
    exit 0
fi

# Get staged diff
staged_diff=$(git diff --cached)
if [ -z "$staged_diff" ]; then
    exit 0
fi

# Quick sanity check
echo "$staged_diff" | whisp shell pipe "check for obvious issues: hardcoded secrets, debug statements, TODO comments. Reply with just 'OK' or list issues." | head -5

#!/bin/bash
# maintenance.sh - run via cron

export PATH="$HOME/.local/bin:$PATH"

# Ensure daemon is running
whisp status > /dev/null 2>&1 || whisp start

# Generate cleanup commands
cleanup_cmd=$(whisp shell query "find and list files in /tmp older than 7 days, larger than 100MB")
echo "Suggested cleanup: $cleanup_cmd"

#!/bin/bash
# analyze-errors.sh

# Collect last hour of errors
errors=$(journalctl --since "1 hour ago" -p err --no-pager)

if [ -n "$errors" ]; then
    echo "$errors" | whisp shell pipe "summarize these system errors and suggest fixes"
fi

#!/bin/bash
# whisp-monitor.sh

# Check daemon health
health=$(whisp health 2>&1)
if echo "$health" | grep -q "Errors:"; then
    error_count=$(echo "$health" | grep "Errors:" | awk '{print $2}')
    if [ "$error_count" -gt 100 ]; then
        echo "WARN: High error count in whisp: $error_count"
    fi
fi

# Export metrics for monitoring
whisp metrics --json >> /var/log/whisp-metrics.jsonl

# Use faster model for simple queries
WHISP_MODEL=gpt-5-nano , list files by size

# Use better model for complex queries
WHISP_MODEL=gpt-4o , debug this race condition in the async code

# ~/.config/whisp/config.toml
default_provider = "gemini"

[providers.gemini]
model = "gemini-1.5-flash"  # Very fast

default_provider = "ollama"

[providers.ollama]
model = "llama3.2"  # Fast local inference

#!/bin/bash
# batch-process.sh

# Warm up connection
whisp health > /dev/null

# Process multiple items quickly
for file in *.log; do
    cat "$file" | whisp shell pipe "extract error count" &
done
wait

# 1. Find what you need
, find all config files containing 'database'

# 2. Understand what you found
,. cat config/database.yml | head -20

# 3. Get alternatives
,,

# 4. Preview changes before making them
,d sed -i 's/localhost/db.prod/g' config/*.yml

# 1. Check recent errors in logs
cat /var/log/app.log | tail -100 |, find the root cause of errors

# 2. When a command fails, whisp auto-suggests fix
npm install  # fails with error
# Whisp suggests: npm cache clean --force && npm install

# 3. Search history for similar issues
,/ permission denied

# 4. Copy solution to share with team
,c

# 1. Get command explanation
,. awk '{print $1}' access.log | sort | uniq -c | sort -rn

# 2. Ask for simpler alternative
, same thing but easier to read

# 3. See other approaches
,,

# 4. Try dry-run before committing
,d the-suggested-command

# ~/.bashrc

if [ "$ENVIRONMENT" = "production" ]; then
    # Use local model in prod (no data leaves machine)
    export WHISP_PROVIDER=ollama
    export WHISP_CONFIRM_DESTRUCTIVE=true
else
    # Use cloud in dev for better quality
    export WHISP_PROVIDER=openai
fi

#!/bin/bash
# project-whisp.sh

# Project-specific model
export WHISP_MODEL=gpt-4o

# Project context
export WHISP_CONTEXT="This is a Django project using PostgreSQL"

# Run whisp with project settings
whisp "$@"

# Add explicit path and wait
export PATH="$HOME/.local/bin:$PATH"
whisp start
sleep 2  # Give daemon time to initialize
whisp status || exit 1

# Increase socket timeout for long operations
export WHISP_TIMEOUT=60000  # 60 seconds

# Use conservative rate limits in CI
[resilience]
requests_per_minute = 10
max_retries = 2