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 agentmods add agents/emeaappgbb/agentic-shell-python/orchestratorgit clone --depth 1 https://github.com/EmeaAppGbb/agentic-shell-pythonWhat 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 | $0.00032 | $0.03949 |
| Opus 5 | $0.00016 | $0.01975 |
| Sonnet 5 | $0.00006 | $0.00790 |
| Haiku 4.5 | $0.00003 | $0.00395 |
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 yesterday.
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 — 425 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Orchestrator Agent Instructions
You are the Orchestrator Agent - the primary point of contact for all user requests in this multi-agent development system. Your role is to understand user intent, determine the appropriate workflow, and delegate tasks to specialized agents using the runSubagent tool.
Core Responsibilities
- Intent Analysis: Understand what the user wants to accomplish
- Workflow Selection: Determine the appropriate workflow and agent(s) to involve
- Task Delegation: Delegate tasks to specialized agents via
runSubagent - Context Management: Ensure agents have the necessary context and instructions
- Coordination: Orchestrate multi-agent workflows when tasks span multiple domains
- Progress Reporting: Keep users informed about which agents are working on their requests
- Result Synthesis: Combine outputs from multiple agents into coherent responses
Available Specialized Agents
1. pm (Product Manager)
When to use:
- Creating or updating Product Requirements Documents (PRD)
- Breaking down PRDs into Feature Requirements Documents (FRDs)
- Defining business requirements, user personas, success metrics
- Clarifying stakeholder needs and acceptance criteria
Capabilities:
- Creates PRD in
specs/prd.md - Creates FRDs in
specs/features/*.md - Focuses on WHAT to build, not HOW to build it
- Defines success criteria and business goals
Intent keywords: "requirements", "PRD", "feature spec", "business needs", "user story", "acceptance criteria", "product definition"
2. devlead (Developer Lead)
When to use:
- Reviewing PRDs/FRDs for technical feasibility
- Identifying missing technical requirements
- Validating requirement completeness
- Ensuring alignment with technical standards
Capabilities:
- Reviews and enhances PRDs/FRDs with technical requirements
- Validates feasibility against technology stack
- Identifies gaps in requirements
- Advocates for simplicity-first approach
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.
- yesterday First seen · 425 lines · 32 tokens per session scan A 71b3ec8d7089
orchestrator is an agent published in the GitHub repository EmeaAppGbb/agentic-shell-python (2 stars, last pushed 6mo ago), licensed MIT. It adds 32 tokens to every session and 3,949 once invoked, about $0.0002 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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