Agent skill

pair-programmer

AI pair programming with real-time screen and audio context. Use when the user wants to record their screen, start/stop recording, or get context from what they're doing.

View SKILL.md on GitHub Repository
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Install this agent skill to your Project

npx add-skill https://github.com/video-db/pair-programmer/tree/main/skills/pair-programmer

SKILL.md

VideoDB Pair Programmer

AI pair programming with real-time screen and audio context. Record your screen and audio, with AI-powered indexing that logs visual and audio events in real-time.

Commands

When user asks for a command, read the corresponding file for instructions:

Command Description Reference
/pair-programmer record Start screen/audio recording See commands/record.md
/pair-programmer stop Stop the running recording See commands/stop.md
/pair-programmer search Search recording context (screen, mic, audio) See commands/search.md
/pair-programmer act Act on a spoken instruction from the mic See commands/act.md
/pair-programmer what-happened Summarize recent activity See commands/what-happened.md
/pair-programmer setup Install deps and configure API key See commands/setup.md
/pair-programmer config Change indexing and other settings See commands/config.md

How It Works

  1. User runs /pair-programmer setup to install dependencies and set VIDEO_DB_API_KEY environment variable
  2. User runs /pair-programmer record to start recording
  3. A picker UI appears to select screen and audio sources
  4. Recording starts and events are logged to /tmp/videodb_pp_events.jsonl
  5. User can stop recording from the tray icon (🔴 PP → Stop Recording)

Output Files

Path Content
/tmp/videodb_pp_pid Process ID of the recorder
/tmp/videodb_pp_events.jsonl All WebSocket events (JSONL format)
/tmp/videodb_pp_info.json Current session info (session_id, rtstream_ids)

Event File Format

Events are written as JSONL (one JSON object per line):

json
{"ts": "2026-03-05T10:15:30.123Z", "unix_ts": 1709374530.12, "channel": "visual_index", "data": {"text": "User is viewing VS Code with auth.ts open"}}
{"ts": "2026-03-05T10:15:31.456Z", "unix_ts": 1709374531.45, "channel": "transcript", "data": {"text": "Let me check the login flow", "is_final": true}}

Environment Variables

The recorder reads these from environment variables:

Variable Required Description
VIDEO_DB_API_KEY Yes VideoDB API key
VIDEO_DB_BASE_URL No API endpoint (default: https://api.videodb.io)

Reading Context

Events are in /tmp/videodb_pp_events.jsonl. Use CLI tools to filter — never read the whole file.

Channel Content Density
visual_index Screen descriptions Dense (~1 every 2s)
transcript Mic speech Sparse (sentences)
audio_index System audio summaries Sparse (sentences)

Channel filter — use grep to filter by channel, pipe to tail for recent events:

bash
grep '"channel":"visual_index"' /tmp/videodb_pp_events.jsonl | tail -10

Keyword search — grep across all channels:

bash
grep -i 'keyword' /tmp/videodb_pp_events.jsonl

Time-window filter — filter events from the last N minutes:

  1. Get current epoch: $(date +%s)
  2. Compute cutoff: epoch - N*60
  3. Filter lines where the unix_ts JSON field exceeds the cutoff
  4. Pipe through grep to narrow by channel

Generate the appropriate filtering command (grep, awk, python3, jq) based on complexity.

For semantic search across indexed content, use search-rtstream.js:

bash
node search-rtstream.js --query="your query" --cwd=<PROJECT_ROOT>

<PROJECT_ROOT> is the absolute path to the user's project directory. This is NOT the skill directory — resolve it before running the command.

See commands/search.md for the full search strategy and CLI patterns.

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