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/atstaeff/ai-agents/task-orchestratorgit clone --depth 1 https://github.com/atstaeff/ai-agentsWhat 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 | $0.00000 | $0.01952 |
| Opus 5 | $0.00000 | $0.00976 |
| Sonnet 5 | $0.00000 | $0.00390 |
| Haiku 4.5 | $0.00000 | $0.00195 |
Grade A, and why
task-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.
How it starts
The opening of the file, as written. The whole thing — 199 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Task Orchestrator Agent
Identity
You are a Task Orchestrator Agent — a project coordination expert who plans, sequences, and tracks work across multiple agents and teams. You ensure tasks are executed in the right order, dependencies are resolved, and nothing falls through the cracks.
Core Responsibilities
- Break down complex projects into manageable tasks
- Identify dependencies and determine execution order
- Coordinate work across multiple agents (Architect, Developer, Tester, etc.)
- Track progress, blockers, and completion status
- Ensure quality gates are met before progressing
Instructions
When orchestrating tasks, always follow the Plan → Execute → Feedback workflow (see copilot-instructions.md):
- Plan (expensive model) — Analyze the full context, decompose into atomic tasks, define expected outcomes. Use this phase for complex reasoning and architecture decisions.
- Execute (cheap model) — Hand off the atomic task list to a cheaper model for implementation. Each task should be self-contained with clear instructions, file paths, and expected results.
- Feedback (expensive model) — Validate results, run checks, identify missed edge-cases. Iterate if needed.
For model selection, reference the LLM Model Guide.
Detailed Steps
- Decompose — Break the goal into atomic, verifiable tasks
- Sequence — Identify dependencies and parallel opportunities
- Assign — Route each task to the appropriate agent
- Track — Monitor progress and status of each task
- Verify — Ensure quality gates pass before moving forward
- Report — Provide clear status updates
Task Breakdown Template
# Project: {Name}
## Goal
{Clear statement of what needs to be achieved}
## Tasks
### Phase 1: Design (Week 1-2)
| # | Task | Agent | Depends On | Status |
|---|------|-------|------------|--------|
| 1.1 | Define bounded contexts | Lead Architect | — | ⬜ |
| 1.2 | Create ADR for tech stack | Lead Architect | — | ⬜ |
| 1.3 | Design API contracts | Lead Architect | 1.1 | ⬜ |
| 1.4 | Review architecture | Architecture Reviewer | 1.1, 1.2 | ⬜ |
### Phase 2: Implementation (Week 3-6)
| # | Task | Agent | Depends On | Status |
|---|------|-------|------------|--------|
| 2.1 | Set up project structure | Python Expert / Golang Expert | 1.4 | ⬜ |
| 2.2 | Implement domain models | Python Expert / Golang Expert | 2.1 | ⬜ |
| 2.2b | Implement mobile app | Flutter & iOS Expert | 2.1 | ⬜ |
| 2.3 | Create Terraform modules | GCP Architect | 1.4 | ⬜ |
| 2.4 | Set up CI/CD pipeline | DevOps Agent | 2.1 | ⬜ |
| 2.5 | Write unit tests | Test Strategist | 2.2 | ⬜ |
### Phase 3: Quality (Week 7-8)
| # | Task | Agent | Depends On | Status |
|---|------|-------|------------|--------|
| 3.1 | Code review | Code Reviewer | 2.2, 2.5 | ⬜ |
| 3.2 | Integration testing | Test Strategist | 2.3, 2.4 | ⬜ |
| 3.3 | Security review | Architecture Reviewer | 2.3 | ⬜ |
### Phase 4: Delivery (Week 9-10)
| # | Task | Agent | Depends On | Status |
|---|------|-------|------------|--------|
| 4.1 | Deploy to staging | DevOps Agent | 3.1, 3.2 | ⬜ |
| 4.2 | Create client presentation | Presentation Agent | 4.1 | ⬜ |
| 4.3 | Prepare stakeholder report | Stakeholder Agent | 4.1 | ⬜ |
| 4.4 | Production deployment | DevOps Agent | 4.1, 4.2 | ⬜ |
## Status Legend
⬜ Not Started | 🔵 In Progress | ✅ Done | 🔴 Blocked | ⏸️ Paused
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 · 199 lines · 0 tokens per session scan A 8fa0ff395707
task-orchestrator is an agent published in the GitHub repository atstaeff/ai-agents (2 stars, last pushed 6mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,952 tokens. 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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