Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add ai-analyst-lab/ai-analyst --skill auth-preflightgit clone --depth 1 https://github.com/ai-analyst-lab/ai-analystWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/ai-analyst-lab/ai-analyst/auth-preflight)<a href="https://agentmods.dev/skills/ai-analyst-lab/ai-analyst/auth-preflight"><img src="https://agentmods.dev/badge/skills/ai-analyst-lab/ai-analyst/auth-preflight/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/ai-analyst-lab/ai-analyst/auth-preflight"><img src="https://agentmods.dev/badge/skills/ai-analyst-lab/ai-analyst/auth-preflight.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00186 | $0.02855 |
| Opus 5.5 | $0.00074 | $0.01142 |
| Sonnet 5.5 | $0.00037 | $0.00571 |
| Haiku 4.5 | $0.00019 | $0.00285 |
Grade B, and why
auth-preflight scanned grade B with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Reads MCP configurationmediumAgent snooping
mcp.json carries server URLs and auth tokens; reading it lets a mod discover and abuse other integrations.
cat .mcp.json | grep -A3 google How it starts
The opening of the file, as written. The whole thing — 298 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: Auth Preflight
Purpose
Verify Google Workspace MCP authentication BEFORE beginning any Google-dependent work. Catches auth issues in <30 seconds instead of discovering them after 5-10 minutes of chart generation, narrative writing, or deck building.
Core principle: Fail fast with clear guidance, not slow with cryptic errors mid-workflow.
When to Apply
Trigger this skill immediately when:
- The task involves Google Docs, Slides, or Drive
- Any
mcp__google-*tool will be called - The user mentions "Google Doc", "Google Slides", "upload to Drive", "create a deck", "export to Google", "share on Drive"
- Before running google-slides-creator, google-slides-reviewer, gdoc-builder, or any Google-dependent agent
Critical timing: Run auth preflight as your FIRST action, before exploring data, generating charts, parsing narratives, or any other substantive work.
Preflight Workflow
Step 1: Detect MCP Configuration
Read .mcp.json to discover which Google MCP server(s) are configured:
cat .mcp.json | grep -A3 google
Look for entries like:
google-docs→ Google Docs + Drive MCP server (most common)google-workspace→ Full workspace MCP (Docs, Slides, Drive)google-slides→ Slides-specific MCP server
Extract:
- Server name (JSON key, e.g., "google-docs")
- Command path (where the executable lives)
- Args (to identify server type)
Why this matters: Different MCP implementations store credentials in different locations. Detecting configuration first ensures you check the right paths.
If no Google MCP found: Report:
Auth: FAILED — No Google MCP server configured in .mcp.json
To use Google Docs/Slides, add a Google MCP server to .mcp.json
Step 2: Check Stored Credentials
Based on MCP type from Step 1, check credentials in ALL possible locations (some setups use non-standard paths):
Priority 1: MCP-specific locations
# For google-docs MCP
ls ~/.claude/mcp-servers/google-docs-mcp-server/
# For google-workspace MCP
ls ~/.google_workspace_mcp/credentials/
# For google-slides MCP
ls ~/.claude/mcp-servers/google-slides-mcp-server/
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 298 lines · 186 tokens per session scan B 24b9dfc6c463
auth-preflight is a skill published in the GitHub repository ai-analyst-lab/ai-analyst (322 stars, last pushed 1mo ago), licensed MIT. It adds 186 tokens to every session and 2,855 once invoked, about $0.0007 per session on Opus 5.5. A static security scan graded it B with 1 finding (reads mcp configuration). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-12.
auth-preflight compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| auth-preflightai-analyst-lab/ai-analyst | Skill | 322 | 1mo ago | MIT | B | 186 | Claude Code |
| orbit-notionnexu-io/open-design | Skill | 106k | yesterday | Apache-2.0 | A | 117 | Claude Code, Codex |
| gogopenclaw/openclaw | Skill | 394k | yesterday | MIT | A | 20 | Claude Code, Codex |
| boxNousResearch/hermes-agent | Skill | 261k | yesterday | MIT | A | 14 | Claude Code, Codex |
| feishuHmbown/CodeWhale | Skill | 41k | 5d ago | MIT | A | 33 | Claude Code, Codex |
| material-organizerOrkas-AI/Orkas | Skill | 9.0k | yesterday | MIT | A | 3 | Claude Code, Codex |
| recipe-save-email-attachmentsgoogleworkspace/cli | Skill | 32k | 4d ago | Apache-2.0 | A | 20 | Claude Code, Codex |