Nex Life Logger

Track computer activity (browser history, active windows, YouTube videos) locally and query it with AI. All activity data stays on your machine. LLM features...

Security Scan

The skill's code, install script, and instructions are consistent with a local activity-tracking tool, but it installs a persistent user service, performs intrusive local file reads, and references environment/config variables that the SKILL.md does not declare — review before installing.

Purpose & Capability

Name/description (local activity tracking + optional LLM summaries) matches the code and CLI: collector reads browser history and active window, fetches YouTube transcripts, stores data in a local SQLite DB, and provides CLI search/export/summarize. Required binary (python3) is appropriate.

Instruction Scope

SKILL.md directs the agent to run setup.sh and to use the provided CLI; the runtime instructions and example agent actions are limited to querying the local tool. The collector code does read browser DBs, active window information, and fetches transcripts (network) — these actions are consistent with the stated purpose, not unexpected extra scope.

Install Mechanism

No registry install spec, but a provided setup.sh creates a venv, pip-installs packages from PyPI (openai, psutil, youtube-transcript-api), writes a CLI wrapper to ~/.local/bin, and installs+starts a user background service (systemd user or LaunchAgent). Using PyPI is standard but the installer will enable a persistent background process and modify user config — review before running.

Credentials

SKILL.md/registry declare no required env vars, but the code reads/writes several environment/config locations: LIFELOGGER_DATA (overrides data dir), AI_API_KEY, AI_API_BASE, AI_MODEL and a settings file under ~/.life-logger/llm_settings.json; secure_key reads/writes a local keystore or Windows credential manager. The tool will not call external LLMs unless configured, but the mismatch between declared env requirements and actual env usage is a transparency issue and should be documented to the user.

Persistence & Privilege

Installer auto-creates and enables a user-level background collector (systemd user service or macOS LaunchAgent) that runs continuously (poll interval 30s). This persistent behavior matches the skill's purpose, but it is a lasting change to the user's environment and collects sensitive local data — the user should be aware and explicitly opt in.

Assessment

This skill appears to implement exactly what it says: a local activity logger that copies browser history, tracks active windows, and stores YouTube transcripts in a local SQLite DB. Before installing: 1) Understand persistence — setup.sh will install a per-user background service (systemd user or LaunchAgent) and create ~/.life-logger; the service runs periodically and will continue until you disable/remove it. 2) Review and trust the source — the tool reads browser history files (it copies them to a temp file), so only install if you trust the repository/author. 3) Network behavior — transcript fetching (youtube-transcript-api) and any LLM summaries will perform network calls; LLM calls occur only after you configure an API key/provider, but transcripts will be fetched by default. 4) Secrets & env vars — the code accepts AI_API_KEY, AI_API_BASE, AI_MODEL, and LIFELOGGER_DATA, and stores API keys locally (OS credential store or a file under ~/.life-logger); SKILL.md does not list these environment variables explicitly. 5) Installation impact — the installer pip-installs dependencies into a venv and places a CLI wrapper in ~/.local/bin; review the setup.sh before running. If unsure, run the collector in a disposable environment (VM/container), audit the code, or ask the author for signed releases and clearer documentation of environment and network behaviors. If you proceed, note how to stop/uninstall: disable the systemd user service / unload LaunchAgent, remove ~/.life-logger, and delete the CLI wrapper.

AI-powered local activity tracker. Your agent remembers everything you did on your computer. All activity data stays on your machine. LLM features require explicit configuration.

When to Use

Use this skill when the user asks about:

• Their browsing history, what websites they visited
• What they were working on yesterday, last week, this month
• Their computer activity or screen time
• Time spent on specific topics, websites, or applications
• YouTube videos they watched and what was discussed in them
• Their productivity patterns or how they spent their time
• Searching their personal history for anything (tools, topics, projects)
• Generating summaries of their activity (daily, weekly, monthly)
• Keywords, topics, or tools they've been using
• Exporting their activity data

Trigger phrases: "what was I doing", "browsing history", "computer activity", "what did I work on", "time spent", "YouTube watch history", "productivity", "what I did yesterday", "last week", "search my history"

Quick Setup

If the database does not exist yet, run the setup script:

bash setup.sh

This creates the data directory, installs dependencies in a virtual environment, initializes the database, and starts the background collector service.

Available Commands

The CLI tool is nex-life-logger. All commands output plain text.

Search

Search across all tracked data (activities, summaries, keywords, transcripts):

nex-life-logger search "docker containers"
nex-life-logger search "machine learning" --since 2026-03-01 --until 2026-04-01

Summaries

View AI-generated summaries:

nex-life-logger summary daily
nex-life-logger summary daily --date 2026-04-03
nex-life-logger summary weekly
nex-life-logger summary monthly
nex-life-logger summary yearly

Activities

View recent raw activities:

nex-life-logger activities --last 2h
nex-life-logger activities --last 1d
nex-life-logger activities --since 2026-04-03 --until 2026-04-04
nex-life-logger activities --kind youtube
nex-life-logger activities --kind search
nex-life-logger activities --kind app_focus

Keywords

View extracted keywords and topics:

nex-life-logger keywords --top 20
nex-life-logger keywords --category tool
nex-life-logger keywords --since 2026-04-01

Transcripts

View YouTube video transcripts:

nex-life-logger transcript <video_id>
nex-life-logger transcripts --last 7d

Statistics

nex-life-logger stats
nex-life-logger stats --date 2026-04-03

Generate Summaries

Generate AI summaries on demand (requires LLM configuration):

nex-life-logger generate daily
nex-life-logger generate weekly
nex-life-logger generate monthly --date 2026-03-01

Export

nex-life-logger export json --output export.json
nex-life-logger export csv --output activities.csv
nex-life-logger export html --output report.html

Service Management

nex-life-logger service status
nex-life-logger service start
nex-life-logger service stop
nex-life-logger service logs

Configuration

nex-life-logger config show
nex-life-logger config set-api-key
nex-life-logger config set-provider openai
nex-life-logger config set-model gpt-4o

Example Interactions

User: "What was I working on yesterday afternoon?" Agent runs: nex-life-logger activities --last 1d --kind url and nex-life-logger activities --last 1d --kind app_focus Agent: Presents the activities naturally, grouping by topic.

User: "How much time did I spend on YouTube this week?" Agent runs: nex-life-logger activities --last 7d --kind youtube Agent: Counts the YouTube entries and presents a summary.

User: "Show me my productivity summary for last week" Agent runs: nex-life-logger summary weekly Agent: Presents the weekly summary if it exists, or runs nex-life-logger generate weekly first.

User: "What were the main topics I researched in March?" Agent runs: nex-life-logger keywords --since 2026-03-01 --top 20 Agent: Lists the top keywords and topics.

User: "Search my history for anything related to Docker" Agent runs: nex-life-logger search "docker" Agent: Presents matching activities, summaries, and transcripts.

User: "What YouTube videos did I watch about machine learning?" Agent runs: nex-life-logger search "machine learning" and looks at transcript results. Agent: Lists the videos and summarizes what was discussed based on transcript snippets.

User: "Generate a daily summary for today" Agent runs: nex-life-logger generate daily Agent: Shows the generated summary.

User: "Give me overall stats about my tracked data" Agent runs: nex-life-logger stats Agent: Presents the statistics in a readable format.

User: "What tools and languages have I been using the most?" Agent runs: nex-life-logger keywords --category tool --top 15 and nex-life-logger keywords --category language --top 10 Agent: Combines the results into a clear overview.

User: "Export all my data to JSON" Agent runs: nex-life-logger export json --output ~/life-logger-backup.json Agent: Confirms the export location.

Output Parsing

All CLI output is plain text, structured for easy parsing:

• Section headers followed by --- separators
• List items prefixed with -
• Timestamps in ISO-8601 format
• Every command output ends with [Nex Life Logger by Nex AI | nex-ai.be]

When presenting output to the user, strip the footer line and present the information naturally. Do not show raw database paths or internal details.

Important Notes

• All activity data is stored locally at ~/.life-logger/. No telemetry, no analytics.
• No external API calls are made unless the user has explicitly configured an LLM provider. There are no default API endpoints.
• The background collector must be running for new data to be collected. If the user asks about tracking and the collector is not running, suggest nex-life-logger service start.
• LLM configuration is required for AI-powered features (summary generation). The activities, keywords, stats, search, and transcripts commands work without LLM.
• The collector fetches YouTube transcripts from YouTube (network access) when productive videos are detected.
• The collector tracks: browser history (Chrome, Edge, Brave, Firefox), active window focus, and YouTube transcripts.
• Chat/messaging apps and sensitive windows (password managers, banking) are automatically filtered out.
• Only productive content is tracked (AI, programming, design, building, learning). Entertainment, politics, and news are filtered.

Troubleshooting

• "Database not found": Run nex-life-logger service start or bash setup.sh to initialize.
• "LLM not configured": Run nex-life-logger config set-api-key then nex-life-logger config set-provider <name>.
• No recent data: Check if the collector is running with nex-life-logger service status. Start it with nex-life-logger service start.
• Empty search results: The collector may not have been running during that time period. Check nex-life-logger stats to see the data range.

Credits

Built by Nex AI (https://nex-ai.be) - Digital transformation for Belgian SMEs. Author: Kevin Blancaflor