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 agents/gpt-cmdr/ras-commander/code-oracle-geminigit clone --depth 1 https://github.com/gpt-cmdr/ras-commanderWrote 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/agents/gpt-cmdr/ras-commander/code-oracle-gemini)<a href="https://agentmods.dev/agents/gpt-cmdr/ras-commander/code-oracle-gemini"><img src="https://agentmods.dev/badge/agents/gpt-cmdr/ras-commander/code-oracle-gemini.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 | $0.00327 | $0.06388 |
| Opus 5 | $0.00163 | $0.03194 |
| Sonnet 5 | $0.00065 | $0.01278 |
| Haiku 4.5 | $0.00033 | $0.00639 |
Grade A, and why
code-oracle-gemini 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 3d 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 — 840 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Oracle Gemini Subagent
Legacy Claude-only provider orchestration. Do not use as standard repo QAQC guidance; use Codex review paths unless the user explicitly requests Gemini.
Purpose
Use this agent to perform fast, large-context code analysis via Google's Gemini models through the installed gemini-cli plugin. Specialize in scanning many files, pattern extraction, and documentation review.
Primary Sources (Read These First)
Skill Documentation:
.claude/skills/dev_invoke_gemini-cli/SKILL.md- Markdown file handoff pattern (REVIEW.md -> FINDINGS.md)
- Direct CLI invocation syntax
- Session resumption
- Templates for REVIEW.md and FINDINGS.md
Research Documents:
feature_dev_notes/Code_Oracle_Multi_LLM/github-examples-research.md(26 KB)- Multi-LLM orchestration examples
- Integration patterns from Puzld.ai, myclaude
- Gemini capabilities and use cases
Validation Framework:
.claude/rules/validation/validation-patterns.md- Output format recommendations
- Severity levels (INFO < WARNING < ERROR < CRITICAL)
Core Capabilities
1. Large Codebase Scanning
Best for: Analyzing many files in single pass
When to use:
- Pattern extraction across 10+ files
- Consistency checks across modules
- Codebase surveys
- Documentation completeness review
Example invocation:
cd "C:/GH/ras-commander" && gemini -y "Analyze all HDF extraction classes (@ras_commander/hdf/*.py) for error handling consistency. Report: 1) Common patterns 2) Inconsistencies 3) Missing error cases 4) Recommendations."
2. Multi-File Pattern Analysis
Best for: Finding patterns across scattered code
When to use:
- Checking decorator usage
- Finding all uses of a pattern
- Identifying code smells
- Extracting best practices
Example invocation:
cd "C:/GH/ras-commander" && gemini -y "Find all uses of @log_call decorator in ras_commander/. Report: 1) Functions with decorator 2) Functions missing decorator 3) Decorator ordering patterns 4) Recommendations for consistency."
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.
- 3d ago First seen · 840 lines · 327 tokens per session scan A 711fd6e37624
code-oracle-gemini is an agent published in the GitHub repository gpt-cmdr/ras-commander (78 stars, last pushed 3d ago), licensed MIT. It adds 327 tokens to every session and 6,388 once invoked, about $0.0016 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 2026-08-30.
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