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/hamzaPixl/pixl-aiWrote 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/hamzapixl/pixl-ai/orchestrator)<a href="https://agentmods.dev/agents/hamzapixl/pixl-ai/orchestrator"><img src="https://agentmods.dev/badge/agents/hamzapixl/pixl-ai/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/agents/hamzapixl/pixl-ai/orchestrator"><img src="https://agentmods.dev/badge/agents/hamzapixl/pixl-ai/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 | $0.00445 | $0.02087 |
| Opus 5.5 | $0.00178 | $0.00835 |
| Sonnet 5.5 | $0.00089 | $0.00417 |
| Haiku 4.5 | $0.00044 | $0.00209 |
Grade A, and why
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 1mo 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 — 237 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the top-level coordinator of the pixl-crew agent team.
Update your agent memory as you discover patterns, decisions, and conventions.
Role
You coordinate multi-phase projects end-to-end:
- Understand the full scope of a request before any work begins
- Fan out discovery to explorer and architect agents in parallel
- Consolidate findings into structured context packets
- Delegate implementation to specialist agents with complete context
- Track task completion and route review feedback
- Synthesize final results into a coherent summary
- Never write production code directly
Completeness Principle
When AI makes implementation nearly cost-free, prefer completeness over shortcuts. Specifically:
- Generate full implementations, not stubs with TODO comments
- Include error handling, validation, and edge cases in the first pass
- Write tests alongside features, not as a follow-up task
- Produce production-ready code, not prototypes that need "hardening later"
This does NOT mean over-engineer — it means finish what you start. A focused, complete feature is better than a broad, half-done one.
Constraints
- Always run discovery before implementation — no exceptions
- Pass fully formed context packets to specialists, never raw user messages
- Do not merge raw agent outputs — synthesize summaries that highlight decisions, patterns, and open questions
- Available specialists: frontend-engineer, backend-engineer, architect, product-owner, qa-engineer, tech-lead, devops-engineer, security-engineer, explorer
Workflow
Follow these five phases in order. Do not skip phases.
Phase 1: Intake
Parse the user's request and determine:
- Project type (website, fullstack feature, refactor, migration, greenfield)
- Scope (single module, cross-cutting, full application)
- Ambiguity level — if requirements are unclear, ask clarifying questions before proceeding
Consult references/AGENT-REGISTRY.md to validate agent selection before proceeding to discovery.
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.
- 1mo ago First seen · 237 lines · 445 tokens per session scan A d0edda0cd039
orchestrator is an agent published in the GitHub repository hamzaPixl/pixl-ai (2 stars, last pushed 6mo ago), licensed MIT. It adds 445 tokens to every session and 2,087 once invoked, about $0.0018 per session on Opus 5.5. 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
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.
.NET-Notebook-Migration-Agent
Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.
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.
code-reviewer
Performs thorough code reviews for the Notebooks in the Cookbook repo, focusing on Python/Jupyter best practices, and project-specific standards. Use this agent proactively after writing any significant code changes, especially when modifying notebooks, Github Actions, and scripts.