google-workspace
Google Workspace CLI operations: setup diagnostics, security audit, recipe discovery, and output analysis. Usage: /google-workspace <setup|audit|recipe|analyze> [options]
Best use case
google-workspace is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Google Workspace CLI operations: setup diagnostics, security audit, recipe discovery, and output analysis. Usage: /google-workspace <setup|audit|recipe|analyze> [options]
Teams using google-workspace should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/google-workspace/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How google-workspace Compares
| Feature / Agent | google-workspace | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Google Workspace CLI operations: setup diagnostics, security audit, recipe discovery, and output analysis. Usage: /google-workspace <setup|audit|recipe|analyze> [options]
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
Related Guides
AI Agent for Product Research
Browse AI agent skills for product research, competitive analysis, customer discovery, and structured product decision support.
AI Agents for Coding
Browse AI agent skills for coding, debugging, testing, refactoring, code review, and developer workflows across Claude, Cursor, and Codex.
Best AI Skills for ChatGPT
Find the best AI skills to adapt into ChatGPT workflows for research, writing, summarization, planning, and repeatable assistant tasks.
SKILL.md Source
# /google-workspace Google Workspace CLI administration via the `gws` CLI. Run setup diagnostics, security audits, browse and execute recipes, and analyze command output. ## Usage ``` /google-workspace setup [--json] /google-workspace audit [--services gmail,drive,calendar] [--json] /google-workspace recipe list [--persona <role>] [--json] /google-workspace recipe search <keyword> [--json] /google-workspace recipe run <name> [--dry-run] /google-workspace recipe describe <name> /google-workspace analyze [--filter <field=value>] [--group-by <field>] [--stats <field>] [--format table|csv|json] ``` ## Examples ``` /google-workspace setup /google-workspace audit --services gmail,drive --json /google-workspace recipe list --persona pm /google-workspace recipe search "email" /google-workspace recipe run standup-report --dry-run /google-workspace recipe describe morning-briefing /google-workspace analyze --filter "mimeType=pdf" --select "name,size" --format table ``` ## Scripts - `engineering-team/google-workspace-cli/scripts/gws_doctor.py` — Pre-flight diagnostics - `engineering-team/google-workspace-cli/scripts/auth_setup_guide.py` — Auth setup guide - `engineering-team/google-workspace-cli/scripts/gws_recipe_runner.py` — Recipe catalog & runner - `engineering-team/google-workspace-cli/scripts/workspace_audit.py` — Security audit - `engineering-team/google-workspace-cli/scripts/output_analyzer.py` — JSON/NDJSON analyzer ## Subcommands ### setup Run pre-flight diagnostics and auth validation. ```bash python3 engineering-team/google-workspace-cli/scripts/gws_doctor.py [--json] python3 engineering-team/google-workspace-cli/scripts/auth_setup_guide.py --validate [--json] ``` ### audit Run security and configuration audit. ```bash python3 engineering-team/google-workspace-cli/scripts/workspace_audit.py [--services gmail,drive,calendar] [--json] ``` ### recipe Browse, search, and execute the 43 built-in gws recipes. ```bash python3 engineering-team/google-workspace-cli/scripts/gws_recipe_runner.py --list [--persona <role>] [--json] python3 engineering-team/google-workspace-cli/scripts/gws_recipe_runner.py --search <keyword> [--json] python3 engineering-team/google-workspace-cli/scripts/gws_recipe_runner.py --describe <name> python3 engineering-team/google-workspace-cli/scripts/gws_recipe_runner.py --run <name> [--dry-run] ``` ### analyze Parse, filter, and aggregate JSON output from any gws command. ```bash gws <command> --json | python3 engineering-team/google-workspace-cli/scripts/output_analyzer.py [options] python3 engineering-team/google-workspace-cli/scripts/output_analyzer.py --demo --format table ``` ## Skill Reference -> `engineering-team/google-workspace-cli/SKILL.md` ## Related Commands - No direct dependencies (self-contained Google Workspace skill)
Related Skills
google-workspace-cli
Google Workspace administration via the gws CLI. Install, authenticate, and automate Gmail, Drive, Sheets, Calendar, Docs, Chat, and Tasks. Run security audits, execute 43 built-in recipes, and use 10 persona bundles. Use for Google Workspace admin, gws CLI setup, Gmail automation, Drive management, or Calendar scheduling.
cs-workspace-admin
Google Workspace administration agent using the gws CLI. Orchestrates workspace setup, Gmail/Drive/Sheets/Calendar automation, security audits, and recipe execution. Spawn when users need Google Workspace automation, gws CLI help, or workspace administration.
wiki-query
Query the LLM Wiki — reads index.md first, drills into 3-10 relevant pages, synthesizes an answer with inline [[wikilink]] citations, and offers to file the answer back as a new comparison or synthesis page. Usage /wiki-query "<question>"
wiki-log
Show recent entries from the LLM Wiki log (wiki/log.md). Uses the standardized
wiki-lint
Run a health check on the LLM Wiki vault — mechanical checks (orphans, broken links, stale pages, missing frontmatter, log gap, duplicates) plus semantic checks (contradictions, cross-reference gaps, concepts missing their own page). Outputs a markdown report with suggested actions. Usage /wiki-lint [--stale-days N] [--log-gap-days N]
wiki-init
Bootstrap a fresh LLM Wiki vault with the three-layer structure, schema files, and starter templates. Usage /wiki-init <path> --topic "<topic>" [--tool all|claude-code|codex|cursor|antigravity]
wiki-ingest
Ingest a source file from raw/ into the LLM Wiki — read, discuss, write summary page, update cross-references across 5-15 pages, regenerate index, append to log. Usage /wiki-ingest <path-to-source>
tc
Track technical changes with structured records, a state machine, and session handoff. Usage: /tc <init|create|update|status|resume|close|export|dashboard> [args]
tc-tracker
Use when the user asks to track technical changes, create change records, manage TC lifecycles, or hand off work between AI sessions. Covers init/create/update/status/resume/close/export workflows for structured code change documentation.
llm-wiki
Use when building or maintaining a persistent personal knowledge base (second brain) in Obsidian where an LLM incrementally ingests sources, updates entity/concept pages, maintains cross-references, and keeps a synthesis current. Triggers include "second brain", "Obsidian wiki", "personal knowledge management", "ingest this paper/article/book", "build a research wiki", "compound knowledge", "Memex", or whenever the user wants knowledge to accumulate across sessions instead of being re-derived by RAG on every query.
karpathy-coder
Use when writing, reviewing, or committing code to enforce Karpathy's 4 coding principles — surface assumptions before coding, keep it simple, make surgical changes, define verifiable goals. Triggers on "review my diff", "check complexity", "am I overcomplicating this", "karpathy check", "before I commit", or any code quality concern where the LLM might be overcoding.
karpathy-check
Run Karpathy's 4-principle review on staged changes or the last commit. Checks complexity, diff noise, hidden assumptions, and goal verification. Usage /karpathy-check [--last-commit]