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/diillson/chatcli/orchestratorgit clone --depth 1 https://github.com/diillson/chatcliWhat 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 | $0.00053 | $0.03352 |
| Opus 5 | $0.00026 | $0.01676 |
| Sonnet 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 2d 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.
- 2d ago First seen · 417 lines · 53 tokens per session scan A 00689c0e790e
orchestrator is an agent published in the GitHub repository diillson/chatcli (89 stars, last pushed 3d ago), licensed Apache-2.0. It adds 53 tokens to every session and 3,352 once invoked, about $0.0003 per session on Opus 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
reviewer
Independent critic in fresh context. Use when an artifact (code, spec, plan, doc) needs verification against a validator (acceptance criteria, checklist file, or any explicit ruleset). Returns reviewed items, findings, completion score and quality score. Never edits the artifact, never decides what to do next.
implementer
Milestone executor. Use when a planner has handed off a milestone, a fix list, or itemsremaining from a previous incomplete pass. Codes, tests, repairs. Returns what's done, what's remaining, and a completion score. Never replans, never judges.
planner
Planning agent. Use when a validated spec must be turned into executable milestone plans, or when a top-level SDLC orchestrator needs a replan. Writes plans and decisions only. Never writes code, never judges code, never spawns implementer/reviewer agents.
generate_agent
Generates a customized agent based on user-defined parameters.
<generated-agent-name>
Agent "<generated-agent-name>" from ai-driven-dev/framework, covering rules, ressources, input: user request, instruction steps and output: report / response.
async-orchestrator
Drives one async development cycle end-to-end. Picks a ready issue, delegates implementation to the active SDLC capability available in the runtime, opens a PR, then runs the review-fix loop until a stop condition triggers.