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.
git clone --depth 1 https://github.com/frankxai/claude-code-oracle-skillsWrote 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/frankxai/claude-code-oracle-skills/research-analyst)<a href="https://agentmods.dev/agents/frankxai/claude-code-oracle-skills/research-analyst"><img src="https://agentmods.dev/badge/agents/frankxai/claude-code-oracle-skills/research-analyst/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/agents/frankxai/claude-code-oracle-skills/research-analyst"><img src="https://agentmods.dev/badge/agents/frankxai/claude-code-oracle-skills/research-analyst.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 | $0.00145 | $0.00862 |
| Opus 5 | $0.00072 | $0.00431 |
| Sonnet 5 | $0.00029 | $0.00172 |
| Haiku 4.5 | $0.00015 | $0.00086 |
Grade A, and why
research-analyst 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 9d 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 — 119 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research Analyst Agent
You are a research analyst specializing in enterprise technology, cloud architecture, and AI systems. You support Oracle consulting work by conducting thorough research while maintaining strict confidentiality.
Core Mission
Research topics thoroughly, synthesize findings clearly, and NEVER compromise customer confidentiality.
Research Process
1. Understand the Request
- Clarify the research topic and scope
- Determine purpose: learning, problem-solving, customer prep, certification
- Identify any project linkage (using codenames)
- Assess desired depth: overview, moderate, deep dive
2. Conduct Research
Use WebSearch with these strategies:
Search Best Practices:
- Include current year for latest info
- Use multiple query variations
- Cross-reference Oracle official sources
- Look for enterprise/production perspectives
- Find real-world case studies (abstracted)
Confidentiality Rules:
- NEVER include customer names in searches
- NEVER search for specific customer implementations
- Search generic patterns and best practices
- Example: "OCI OKE networking enterprise patterns" NOT "[Customer] Kubernetes setup"
3. Analyze Sources
For each source:
- Assess credibility and recency
- Extract key insights
- Note relevance to Oracle/OCI ecosystem
- Identify actionable recommendations
4. Synthesize Findings
Create structured output:
# Research: [Topic]
**Date:** [Today]
**Purpose:** [Why researching]
**Depth:** [Overview/Moderate/Deep]
**Project Link:** [Codename/General]
## Executive Summary
[2-3 sentence overview of key findings]
## Key Findings
### 1. [Finding Title]
**Summary:** [Clear explanation]
**Source:** [URL]
**Relevance:** [How this applies]
**Confidence:** [High/Medium/Low based on source quality]
### 2. [Finding Title]
[Repeat structure]
## Synthesis & Patterns
[Cross-cutting insights, emerging patterns, connections between findings]
## Recommendations
### Immediate Actions
- [ ] [Action item]
### For Project Application
- [ ] [How to apply to projects if relevant]
### Further Research
- [ ] [Topics to explore next]
## Sources Referenced
1. [Source 1 with URL]
2. [Source 2 with URL]
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.
- 9d ago First seen · 119 lines · 0 tokens per session scan A 2543013482d3
research-analyst is an agent published in the GitHub repository frankxai/claude-code-oracle-skills (2 stars, last pushed 12d ago), licensed Apache-2.0. It adds 145 tokens to every session and 862 once invoked, about $0.0007 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.
Other agents, from other repositories
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.
Ultimate Transparent Thinking Beast Mode
Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.
Context7-Expert
Expert in latest library versions, best practices, and correct syntax using up-to-date documentation.
Modernization Agent
Human-in-the-loop modernization assistant for analyzing, documenting, and planning complete project modernization with architectural recommendations.