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Screenpipe Health

OrganizationPopular
screenpipe
screenpipe-health

Check Screenpipe health status, process state, and diagnose common issues

Overview

Publisherscreenpipe
Repositoryscreenpipe
Skill namescreenpipe-health
Stars
21.9K
Forks
2.2K
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by screenpipe on GitHub. Read the source before you install it.

Installation

Install the Screenpipe Health AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/screenpipe/screenpipe/tree/main/.claude/skills/screenpipe-health
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/screenpipe/screenpipe.git /tmp/screenpipe
mkdir -p .claude/skills
cp -r /tmp/screenpipe/.claude/skills/screenpipe-health .claude/skills/screenpipe-health
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Screenpipe Health in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Screenpipe Health on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Screenpipe Health is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Screenpipe Health Agent

You are a specialized agent for checking Screenpipe's health status and diagnosing issues.

Quick Health Check

1. Check if Screenpipe is Running

bash
# Check for screenpipe processes
pgrep -fl screenpipe

# Check if API is responding
curl -s http://localhost:3030/health | head -100

2. Check Recording Status

bash
# Get health with recording info
curl -s http://localhost:3030/health | jq '.frame_status, .audio_status' 2>/dev/null || curl -s http://localhost:3030/health

3. Check Disk Usage

bash
# Screenpipe data directory size
du -sh ~/.screenpipe/

# Database size
ls -lh ~/.screenpipe/db.sqlite* 2>/dev/null

# Video/audio cache
du -sh ~/.screenpipe/data/ 2>/dev/null

4. Check Recent Logs for Errors

bash
# Last 10 errors from today
grep -i "error" ~/.screenpipe/screenpipe.$(date +%Y-%m-%d).log 2>/dev/null | tail -10

Detailed Diagnostics

Process Information

bash
# Detailed process info
ps aux | grep -i screenpipe | grep -v grep

# Memory usage
ps aux | grep -i screenpipe | grep -v grep | awk '{sum+=$6} END {print "Total Memory: " sum/1024 " MB"}'

# Check if app or CLI
pgrep -fl "screenpipe-app" && echo "Desktop app running"
pgrep -fl "screenpipe$" && echo "CLI running"

API Endpoints Check

bash
# Health endpoint (includes vision + audio pipeline stats)
curl -s http://localhost:3030/health

# Search endpoint (test query)
curl -s "http://localhost:3030/search?limit=1" | head -50

# List audio devices
curl -s http://localhost:3030/audio/list

# List monitors
curl -s http://localhost:3030/vision/list

# Vision pipeline metrics (raw counters)
curl -s http://localhost:3030/vision/metrics

# Audio pipeline metrics (raw counters)
curl -s http://localhost:3030/audio/metrics

Audio Pipeline Diagnostics

bash
# Quick audio pipeline health — is transcription actually working?
curl -s http://localhost:3030/audio/metrics | python3 -c "
import sys,json
m = json.load(sys.stdin)
total_vad = m['vad_passed'] + m['vad_rejected']
print(f'Uptime: {m[\"uptime_secs\"]/60:.0f} min')
print(f'Chunks sent to engine: {m[\"chunks_sent\"]}')
print(f'  Channel full drops:  {m[\"chunks_channel_full\"]}')
print(f'  Stream timeouts:     {m[\"stream_timeouts\"]}')
print(f'VAD passed/rejected:   {m[\"vad_passed\"]}/{m[\"vad_rejected\"]} ({m[\"vad_passthrough_rate\"]*100:.0f}% passthrough)')
print(f'  Avg speech ratio:    {m[\"avg_speech_ratio\"]:.3f}')
print(f'Transcriptions:        {m[\"transcriptions_completed\"]} ok, {m[\"transcriptions_empty\"]} empty, {m[\"transcription_errors\"]} errors')
print(f'DB inserted:           {m[\"db_inserted\"]} ({m[\"total_words\"]} words, {m[\"words_per_minute\"]:.0f} wpm)')
print()
if m['chunks_channel_full'] > 0: print('⚠️  Channel full — transcription engine too slow, audio being dropped')
if total_vad > 0 and m['vad_passthrough_rate'] < 0.1: print('⚠️  Very low VAD passthrough — may be dropping real speech')
if m['transcription_errors'] > 0: print('⚠️  Transcription errors detected')
if m['chunks_sent'] > 0 and m['db_inserted'] == 0: print('🔴 Chunks sent but nothing stored — pipeline is broken')
if m['chunks_sent'] == 0 and m['uptime_secs'] > 120: print('🔴 No chunks sent after 2min — audio capture not working')
"

# Audio pipeline info from /health (summary view)
curl -s http://localhost:3030/health | python3 -c "
import sys,json
h = json.load(sys.stdin)
print(f'Audio status: {h[\"audio_status\"]}')
if 'audio_pipeline' in h and h['audio_pipeline']:
    p = h['audio_pipeline']
    print(f'  VAD passthrough: {p[\"vad_passthrough_rate\"]*100:.0f}%')
    print(f'  Words/min:       {p[\"words_per_minute\"]:.0f}')
    print(f'  DB inserted:     {p[\"db_inserted\"]}')
"

Database Health

bash
# Check database integrity
sqlite3 ~/.screenpipe/db.sqlite "PRAGMA integrity_check;" 2>/dev/null

# Database size and tables
sqlite3 ~/.screenpipe/db.sqlite "SELECT name, (SELECT COUNT(*) FROM main WHERE name=t.name) FROM sqlite_master t WHERE type='table';" 2>/dev/null

# Recent frame count
sqlite3 ~/.screenpipe/db.sqlite "SELECT COUNT(*) as frames_today FROM frames WHERE timestamp > datetime('now', '-1 day');" 2>/dev/null

Permissions Check (macOS)

bash
# Check screen recording permission
sqlite3 ~/Library/Application\ Support/com.apple.TCC/TCC.db "SELECT client,allowed FROM access WHERE service='kTCCServiceScreenCapture';" 2>/dev/null | grep -i screenpipe

# Check microphone permission
sqlite3 ~/Library/Application\ Support/com.apple.TCC/TCC.db "SELECT client,allowed FROM access WHERE service='kTCCServiceMicrophone';" 2>/dev/null | grep -i screenpipe

# Or use tccutil (if available)
echo "Check System Preferences > Privacy & Security > Screen Recording and Microphone for screenpipe permissions"

Common Issues & Fixes

Issue: Screenpipe not running

bash
# Start CLI
screenpipe

# Or start app
open /Applications/screenpipe.app

Issue: No frames being captured

  1. Check screen recording permission in System Preferences
  2. Check logs for permission errors:
bash
grep -i "permission\|denied\|cg\|capture" ~/.screenpipe/screenpipe.$(date +%Y-%m-%d).log | tail -20

Issue: No audio transcription

  1. Check microphone permission
  2. Check audio pipeline metrics:
bash
# Is audio being captured at all?
curl -s http://localhost:3030/audio/metrics | python3 -c "
import sys,json; m=json.load(sys.stdin)
print(f'chunks_sent={m[\"chunks_sent\"]}, vad_passed={m[\"vad_passed\"]}, vad_rejected={m[\"vad_rejected\"]}, db_inserted={m[\"db_inserted\"]}')
if m['chunks_sent']==0: print('→ No audio reaching engine. Check device/permissions.')
elif m['vad_passed']==0: print('→ VAD rejecting everything. Check mic input level or lower vad_sensitivity.')
elif m['db_inserted']==0: print('→ Transcription failing. Check engine config or logs.')
"
  1. Check audio device selection:
bash
curl -s http://localhost:3030/audio/list
grep -i "audio\|device\|whisper" ~/.screenpipe/screenpipe.$(date +%Y-%m-%d).log | tail -20

Issue: High CPU/Memory

bash
# Check current usage
top -l 1 -s 0 | grep -i screenpipe

# Check for memory leaks in logs
grep -i "memory\|oom" ~/.screenpipe/screenpipe.$(date +%Y-%m-%d).log

Issue: Database locked

bash
# Check for lock
fuser ~/.screenpipe/db.sqlite 2>/dev/null

# Check for multiple processes
pgrep -c screenpipe

Response Format

When reporting health status:

  1. Start with overall status (healthy/unhealthy)
  2. List any issues found
  3. Provide specific error messages if relevant
  4. Suggest fixes for identified issues

Frequently asked questions

What does the Screenpipe Health AI skill do?

Check Screenpipe health status, process state, and diagnose common issues

Why use Screenpipe Health on TypingMind?

Because you install it once and use it with any model. Screenpipe Health is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Screenpipe Health in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/screenpipe/screenpipe/tree/main/.claude/skills/screenpipe-health. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Screenpipe Health?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Screenpipe Health?

As many as you like. As long as a model supports skills, you can use Screenpipe Health with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Screenpipe Health AI skill free?

It is published on GitHub by screenpipe. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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