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 agentmods add skills/pauldatta/gemini-cli-scanner/scannpx skills add pauldatta/gemini-cli-scanner --skill scangit clone --depth 1 https://github.com/pauldatta/gemini-cli-scannerWhat 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 | $0.00082 | $0.01020 |
| Opus 5 | $0.00041 | $0.00510 |
| Sonnet 5 | $0.00016 | $0.00204 |
| Haiku 4.5 | $0.00008 | $0.00102 |
Grade A, and why
scan scanned grade A with 0 findings 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 2d 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 118 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Scan AI Coding Environment
Audit your AI coding tool ecosystem and discover reusable patterns.
When to Use
- User says "scan my environment", "audit my setup", "check my tools"
- User asks about their maturity score or wants recommendations
- User wants to discover what skills they should create
- User asks what AI tools they have installed
- User wants to compare tool configurations across their ecosystem
Quick Scan (No API Key)
Run a full environment scan without AI-powered skill suggestions:
npx gemini-cli-scanner@latest --skip-suggestions
This produces:
scan-results/gemini-env-manifest.json— structured data for aggregationscan-results/gemini-env-report.md— human-readable report with scores and recommendations
After the scan completes, read the report and present the key findings to the user.
Full Scan (With Skill Suggestions)
If the user wants AI-generated skill suggestions, ensure credentials are set:
# Check for credentials
echo $GOOGLE_CLOUD_PROJECT # Vertex AI
echo $GOOGLE_API_KEY # API Key
Then run:
npx gemini-cli-scanner@latest
Scan With Repos
To include project-level scanning (.gemini/ configs, GEMINI.md, repo patterns):
npx gemini-cli-scanner@latest --repos ~/Code --skip-suggestions
For deeper repo discovery:
npx gemini-cli-scanner@latest --repos ~/Code --repo-depth 4
Read and Present Results
After scanning, always:
-
Read the JSON manifest:
cat scan-results/gemini-env-manifest.json -
Read the markdown report:
cat scan-results/gemini-env-report.md -
Present to the user:
- Maturity score (0–67) and tier (Getting Started / Intermediate / Advanced / Expert)
- Top recommendations — actionable items to improve their setup
- Ecosystem summary — which tools are installed, skill counts, MCP servers
- Antigravity brain intelligence — if present, summarize tool usage patterns, conversation counts, and top tools
- Skill suggestions — if generated, present the candidates with install commands
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.
- 2d ago First seen · 118 lines · 82 tokens per session scan A 7ede562af9a9
scan is a skill published in the GitHub repository pauldatta/gemini-cli-scanner (2 stars, last pushed 29d ago), licensed Apache-2.0. It adds 82 tokens to every session and 1,020 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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