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 commands/raw1mage/patentmcp/configure-bigquerygit clone --depth 1 https://github.com/Raw1mage/patentmcpWrote 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/commands/raw1mage/patentmcp/configure-bigquery)<a href="https://agentmods.dev/commands/raw1mage/patentmcp/configure-bigquery"><img src="https://agentmods.dev/badge/commands/raw1mage/patentmcp/configure-bigquery.svg" alt="Measured on agentmods" 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 | $0.00014 | $0.00289 |
| Opus 5 | $0.00007 | $0.00144 |
| Sonnet 5 | $0.00003 | $0.00058 |
| Haiku 4.5 | $0.00001 | $0.00029 |
Grade C, and why
configure-bigquery scanned grade C 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 5d 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.
Reaches for credential fileshighPrivilege escalation
SSH keys, cloud credentials, git-credentials, .npmrc, /etc/shadow: reading these is how a config file becomes a credential leak.
- Windows: `%APPDATA%\gcloud\application_default_credentials.json` This is a copy
100% identical to configure-bigquery — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
Configure BigQuery Authentication
Set up Google Cloud authentication to enable patent search across 100M+ worldwide patents.
Instructions
- Verify Google Cloud SDK is installed
- Run BigQuery authentication setup
- Authorize browser OAuth flow
- Verify credentials are saved
- Test BigQuery connection
Command
cd ${CLAUDE_PLUGIN_ROOT}
python scripts/setup_bigquery_auth.py
Or using gcloud directly:
gcloud auth application-default login
What This Does
- Opens browser for Google account authorization
- Saves credentials to:
- Windows:
%APPDATA%\gcloud\application_default_credentials.json - Mac/Linux:
~/.config/gcloud/application_default_credentials.json
- Windows:
- Verifies access to patents-public-data dataset
- Tests connection with sample query
Requirements
- Google account (free)
- Google Cloud project (free tier: 1TB queries/month)
- Internet connection
Verification
After setup, test with:
python scripts/test_bigquery.py
Should return sample patent results.
Environment Variable
Set your project ID:
export GOOGLE_CLOUD_PROJECT=your-project-id
Or add to .env file under ${CLAUDE_PLUGIN_ROOT}.
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.
- 5d ago First seen · 65 lines · 14 tokens per session scan C 0e58824146ab
configure-bigquery is a command published in the GitHub repository Raw1mage/patentmcp (2 stars, last pushed 1mo ago), licensed MIT. It adds 14 tokens to every session and 289 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it C with 1 finding (reaches for credential files). It is 100% identical to configure-bigquery, differing in 0 lines, and is treated as a copy.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.