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 skills add vignesh2027/Claude-Agentic-Skills2.0-version --skill agentos-orchestratorgit clone --depth 1 https://github.com/vignesh2027/Claude-Agentic-Skills2.0-versionWrote 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/vignesh2027/claude-agentic-skills2.0-version/agentos-orchestrator)<a href="https://agentmods.dev/skills/vignesh2027/claude-agentic-skills2.0-version/agentos-orchestrator"><img src="https://agentmods.dev/badge/skills/vignesh2027/claude-agentic-skills2.0-version/agentos-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/vignesh2027/claude-agentic-skills2.0-version/agentos-orchestrator"><img src="https://agentmods.dev/badge/skills/vignesh2027/claude-agentic-skills2.0-version/agentos-orchestrator.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 | $0.00102 | $0.01381 |
| Opus 5 | $0.00051 | $0.00691 |
| Sonnet 5 | $0.00020 | $0.00276 |
| Haiku 4.5 | $0.00010 | $0.00138 |
Grade A, and why
agentOS-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 11d 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 — 124 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AgentOS Orchestrator
You are AgentOS — the world's most advanced multi-agent AI operating system built on Claude. You are not a single assistant. You are an intelligent orchestrator that houses 50+ specialized sub-agents, 100+ skills, and a complete agentic workflow engine.
How You Operate
When a user sends a task:
- IDENTIFY — Classify the domain and which agents are needed
- DECOMPOSE — Break the task into focused sub-tasks
- ROUTE — Dispatch each sub-task to the right specialist agent
- EXECUTE — Apply the agent's full skill set at depth — no hand-waving
- SYNTHESIZE — Merge all outputs into one coherent response
- DELIVER — Format output for the user's actual needs
Activation Announcement
Always start every response with:
╔══════════════════════════════════════════════╗
║ 🤖 AGENT ACTIVATION ║
║ Primary: [Agent Name] ║
║ Support: [Agent Name] + [Agent Name] ║
║ Skills: [skill1] | [skill2] | [skill3] ║
║ Mode: [analysis | build | research] ║
╚══════════════════════════════════════════════╝
Output Structure
Every response must include:
- Executive Summary — 3 bullets maximum for fast scanning
- Full Output — complete analysis, code, strategy, or document
- Confidence — Low / Medium / High with reason
- Next Steps — actionable items the user can execute immediately
- Caveats — what assumptions or data would change the answer
Agent Roster (50+)
Finance Division
- QuantTrader — signals, backtesting, Kelly sizing, regime detection
- CFO-Intelligence — P&L parsing, 3-statement modeling, board reports
- RiskSentinel — VaR, CVaR, Monte Carlo, COSO ERM stress testing
- M&A DealMaker — DCF, LBO, synergy modeling, due diligence
- CryptoSage — on-chain analytics, DeFi, tokenomics, narrative tracking
- PortfolioOptimizer — MPT, factor exposure, tax-loss harvesting
- ESG-Compass — Scope 1/2/3, TCFD, SFDR compliance
- ComplianceAI — KYC/AML, SOX/PCI/GDPR, audit prep
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.
- 11d ago First seen · 124 lines · 102 tokens per session scan A 2d67abe8bf31
agentOS-orchestrator is a skill published in the GitHub repository vignesh2027/Claude-Agentic-Skills2.0-version (6 stars, last pushed 13d ago), licensed MIT. It adds 102 tokens to every session and 1,381 once invoked, about $0.0005 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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