Borrowing it
Nothing to install: this file belongs to SHAdd0WTAka/Zen-Ai-Pentest. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/SHAdd0WTAka/Zen-Ai-Pentest/main/.claude/skills/research-orchestrator/SKILL.mdgit clone --depth 1 https://github.com/SHAdd0WTAka/Zen-Ai-PentestWrote 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/shadd0wtaka/zen-ai-pentest/research-orchestrator)<a href="https://agentmods.dev/skills/shadd0wtaka/zen-ai-pentest/research-orchestrator"><img src="https://agentmods.dev/badge/skills/shadd0wtaka/zen-ai-pentest/research-orchestrator/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/shadd0wtaka/zen-ai-pentest/research-orchestrator"><img src="https://agentmods.dev/badge/skills/shadd0wtaka/zen-ai-pentest/research-orchestrator.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00022 | $0.00185 |
| Opus 5 | $0.00011 | $0.00093 |
| Sonnet 5 | $0.00004 | $0.00037 |
| Haiku 4.5 | $0.00002 | $0.00018 |
Grade A, and why
research-orchestrator 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 12d 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.
What it actually says
You are a parallel research orchestrator. When given a question, simulate 4 specialized research agents working simultaneously:
Agent 1 (Official Sources): Find what official documentation and release notes say. Agent 2 (Practical Patterns): Identify how practitioners actually use this in the real world. Agent 3 (Edge Cases & Gotchas): Surface common mistakes, version conflicts, and hidden limitations. Agent 4 (Future Trajectory): Identify where this topic is heading based on recent signals.
Run all 4 agents against the question, then synthesize a final unified answer that integrates all perspectives.
Format output as: [Agent findings in brief] → [Synthesized Answer] → [Top 3 actionable takeaways]
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.
- 12d ago First seen · 18 lines · 22 tokens per session scan A 8b73de1ad1de
research-orchestrator is a skill published in the GitHub repository SHAdd0WTAka/Zen-Ai-Pentest (455 stars, last pushed yesterday), licensed MIT. It adds 22 tokens to every session and 185 once invoked, about $0.0001 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.
Other skills, from other repositories
implementing-data-loss-prevention-with-microsoft-purview
Implements data loss prevention policies using Microsoft Purview to protect sensitive information across Exchange Online, SharePoint, OneDrive, Teams, endpoint devices, and Power BI. The analyst configures sensitivity labels with encryption and content marking, creates DLP policies using built-in and custom sensitive…
implementing-aws-config-rules-for-compliance
Implementing AWS Config rules for continuous compliance monitoring of AWS resources, deploying managed and custom rules aligned to CIS and PCI DSS frameworks, configuring automatic remediation with SSM Automation, and aggregating compliance data across accounts.
implementing-aws-security-hub-compliance
Implementing AWS Security Hub to aggregate security findings across AWS accounts, enable compliance standards like CIS AWS Foundations and PCI DSS, configure automated remediation with EventBridge and Lambda, and create custom security insights for organizational risk management.
implementing-cloud-security-posture-management
Implementing Cloud Security Posture Management (CSPM) to continuously monitor multi-cloud environments for misconfigurations, compliance violations, and security risks using Prowler, ScoutSuite, AWS Security Hub, Azure Defender, and GCP Security Command Center.
implementing-aws-macie-for-data-classification
Implement Amazon Macie to automatically discover, classify, and protect sensitive data in S3 buckets using machine learning and pattern matching for PII, financial data, and credentials detection.
implementing-gcp-organization-policy-constraints
Implement GCP Organization Policy constraints to enforce security guardrails across the entire resource hierarchy, restricting risky configurations and ensuring compliance at organization, folder, and project levels.