premium-agent-orchestration

premium-agent-orchestration is a skill for Claude Code, Codex from dryvist/claude-code-plugins. It costs 66 tokens per session (2,639 once invoked), scanned A, original, Apache-2.0.

A workflow for using a high-end AI model as the decision-maker while sending routine, checkable work to cheaper agents or local language models.

In plain words
What is it for?
Use it to divide software tasks between agents, choose who handles planning or implementation, manage trade-offs, and review the resulting work.
Why use it?
It keeps the most capable model focused on intent, design choices, risks, and review instead of spending its time on work that can be checked mechanically.

Skill for Claude CodeCodex

Part of the ai-delegation plugin — 6 skills shipped together

Install

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.

agentmods
npx agentmods add skills/dryvist/claude-code-plugins/premium-agent-orchestration
Any agent
npx skills add dryvist/claude-code-plugins --skill premium-agent-orchestration
Clone the repo
git clone --depth 1 https://github.com/dryvist/claude-code-plugins

Made for: Claude Code, Codex.

Or install ai-delegation, the plugin that ships this one along with the rest of its 6 skills.

Wrote 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.

agentmods badge for premium-agent-orchestration

README.md
[![agentmods](https://agentmods.dev/badge/skills/dryvist/claude-code-plugins/premium-agent-orchestration.svg)](https://agentmods.dev/skills/dryvist/claude-code-plugins/premium-agent-orchestration)
Your own site
<a href="https://agentmods.dev/skills/dryvist/claude-code-plugins/premium-agent-orchestration"><img src="https://agentmods.dev/badge/skills/dryvist/claude-code-plugins/premium-agent-orchestration.svg" alt="Measured on agentmods" height="20"></a>
Per session 66 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,639 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00066 $0.02639
Opus 5 $0.00033 $0.01319
Sonnet 5 $0.00013 $0.00528
Haiku 4.5 $0.00007 $0.00264

Measured today against content hash 664f2099a1f2, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

premium-agent-orchestration 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 today.

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.

ai-delegation/skills/premium-agent-orchestration/SKILL.md · 259 lines

How it starts

The opening of the file, as written. The whole thing — 259 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Premium Agent Orchestration

Treat the model running the current session as the senior decision-maker, whatever it is — the skill is model- and vendor-agnostic and applies equally to any present or future top-tier model. Spend premium reasoning only where stronger judgment changes the outcome, and route checkable labor to the cheapest capable executor.

Purpose

Use this skill to keep expensive top-tier models focused on judgment instead of labor. Preserve premium reasoning for understanding intent, choosing strategy, managing risk, resolving ambiguity, reviewing critical outputs, and giving the final answer.

Delegate work when the result can be checked from concrete evidence. Prefer the lowest-cost executor that can reliably produce that evidence.

Senior Model Owns

Keep these decisions with the premium lead (the current session's model):

  • Understand the real user intent.
  • Decide what matters and what is out of scope.
  • Choose the architecture or approach.
  • Break ambiguous work into clear parts.
  • Decide task order and dependencies.
  • Make tradeoffs between speed, quality, risk, and scope.
  • Identify hidden risks.
  • Resolve disagreement between agents.
  • Review important outputs.
  • Decide when the work is good enough.
  • Give the final answer to the user.

Model Tiers

Each row below is a model tier — a capability role, not a specific model name. Resolve each tier against whatever models the current environment actually offers (native subagent model options, configured CLIs, local serving); never assume a specific vendor's lineup.

Tier Use for Boundary
Local/free File discovery, log summaries, simple scans, checklist verification, cheap summaries Report facts and evidence; avoid product or architecture calls
Small/cheap cloud Repo discovery, large-file summaries, log inspection, simple checks, edge-case scanning Report facts, not direction
Mid execution Scoped implementation, tests, medium debugging, local refactors, following existing patterns Execute the plan; avoid changing architecture or product intent
Strong reasoning Complex implementation, deep debugging, cross-module reasoning, risky review, security-sensitive reasoning Reason deeply, but leave final authority with the premium lead
Premium lead Intent, architecture, decomposition, tradeoffs, risk, disagreement, final review, synthesis Own final decisions and user communication

Read the full file on GitHub · 259 lines

Changes

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.

  1. today Changed · +4 lines 664f2099a1f2
  2. 5d ago First seen · 255 lines · 66 tokens per session scan A 7574a98a1b07

Subscribe to this mod's changes

premium-agent-orchestration is a skill published in the GitHub repository dryvist/claude-code-plugins (3 stars, last pushed today), licensed Apache-2.0. It adds 66 tokens to every session and 2,639 once invoked, about $0.0003 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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