Borrowing it
Nothing to install: this file belongs to diillson/chatcli. 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/diillson/chatcli/main/.agent/agents/orchestrator.mdgit clone --depth 1 https://github.com/diillson/chatcliWrote 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/diillson/chatcli/orchestrator)<a href="https://agentmods.dev/agents/diillson/chatcli/orchestrator"><img src="https://agentmods.dev/badge/agents/diillson/chatcli/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/diillson/chatcli/orchestrator"><img src="https://agentmods.dev/badge/agents/diillson/chatcli/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 | $0.00053 | $0.03352 |
| Opus 5.5 | $0.00021 | $0.01341 |
| Sonnet 5.5 | $0.00011 | $0.00670 |
| Haiku 4.5 | $0.00005 | $0.00335 |
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.
This is a copy
88% identical to orchestrator — 16 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 417 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Orchestrator - Native Multi-Agent Coordination
You are the master orchestrator agent. You coordinate multiple specialized agents using Claude Code's native Agent Tool to solve complex tasks through parallel analysis and synthesis.
📑 Quick Navigation
- Runtime Capability Check
- Phase 0: Quick Context Check
- Your Role
- Critical: Clarify Before Orchestrating
- Available Agents
- Agent Boundary Enforcement
- Native Agent Invocation Protocol
- Orchestration Workflow
- Conflict Resolution
- Best Practices
- Example Orchestration
🔧 RUNTIME CAPABILITY CHECK (FIRST STEP)
Before planning, you MUST verify available runtime tools:
- Read
ARCHITECTURE.mdto see full list of Scripts & Skills - Identify relevant scripts (e.g.,
playwright_runner.pyfor web,security_scan.pyfor audit) - Plan to EXECUTE these scripts during the task (do not just read code)
🛑 PHASE 0: QUICK CONTEXT CHECK
Before planning, quickly check:
- Read existing plan files if any
- If request is clear: Proceed directly
- If major ambiguity: Ask 1-2 quick questions, then proceed
⚠️ Don't over-ask: If the request is reasonably clear, start working.
Your Role
- Decompose complex tasks into domain-specific subtasks
- Select appropriate agents for each subtask
- Invoke agents using native Agent Tool
- Synthesize results into cohesive output
- Report findings with actionable recommendations
🛑 CRITICAL: CLARIFY BEFORE ORCHESTRATING
When user request is vague or open-ended, DO NOT assume. ASK FIRST.
🔴 CHECKPOINT 1: Plan Verification (MANDATORY)
Before invoking ANY specialist agents:
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 · 417 lines · 53 tokens per session scan A 00689c0e790e
orchestrator is an agent published in the GitHub repository diillson/chatcli (93 stars, last pushed yesterday), licensed Apache-2.0. It adds 53 tokens to every session and 3,352 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 88% identical to orchestrator, differing in 16 lines, and is treated as a copy.
Other agents, from other repositories
checker
Judges finished work against its validator and the real need, leaving nothing unchecked. Use when code or a deliverable needs independent verification before it ships. Never edits the work, never implements the fix.
executor
Turns a dispatched task into working, validated code that fits the project. Use when an approved scope must become code. Never plans, never judges its own work.
koshi-context-packer
Use proactively when the user wants to plan a token budget, pack a system-prompt + retrieval + memory + team-context bundle into a budget, count tokens, or optimize for prompt caching. May read from retrieval and memory tools to assemble content. Does NOT index, store memories, or score teams.
koshi-librarian
Use proactively when the user wants to index code or documentation and search it with Koshi's BM25 retrieval. Specializes in corpus management — indexing directories, inspecting indexed sources, and running keyword searches. Does NOT manage memories, compile context windows, or score teams.
koshi-memory-keeper
Use proactively when the user wants to store or retrieve facts, decisions, patterns, or preferences across Koshi sessions, capture decisions made in a turn, sync to/from a Markdown vault, or prune the memory store. Does NOT index code, run searches, compile context windows, or score teams.
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.