/delivery-orchestrator
Decompose an epic into atomic parallelizable tasks and route each task to the right skill. Use this as a meta-router when you have more than one skill available and need to decide which applies. Trigger at the start of any multi-track epic or when the skill count in your harness
$ npx -y skills add jpantsjoha/ai-native-developer-experience --skill delivery-orchestrator --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/delivery-orchestrator
Context preview
The summary Claude sees to decide when to auto-load this skill.
Decompose an epic into atomic parallelizable tasks and route each task to the right skill. Use this as a meta-router when you have more than one skill available and need to decide which applies. Trigger at the start of any multi-track epic or when the skill count in your harness
SKILL.md
delivery-orchestrator.SKILL.mdname: delivery-orchestrator
description: Decompose an epic into atomic parallelizable tasks and route each task to the right skill. Use this as a meta-router when you have more than one skill available and need to decide which applies. Trigger at the start of any multi-track epic or when the skill count in your harness exceeds ~12.
Delivery Orchestrator
> **A skill for choosing skills.** Once your harness grows past a handful of skills, the agent needs a way to select the right one. This is that skill.
The orchestrator has two jobs: decompose work into the smallest independently executable units, then route each unit to the skill that owns it.
When to use
- Starting a new epic or multi-track piece of work
- When an agent is about to attempt everything in one context window
- When parallel execution across multiple agents is needed
- When you need to decide which skill applies to an incoming task
Procedure
Part 1 — Decomposition
1. **Read the spec or brief** — confirm a feature spec or HLD exists. If not, invoke `spec-first-delivery` first. 2. **Identify tracks** — group work into independent tracks (e.g. backend API, frontend, infrastructure, testing). Tracks can run in parallel. Dependencies between tracks must be explicit. 3. **Break each track into atomic tasks** — an atomic task is one that:
- Can be assigned to a single agent
- Has a clear input and a clear output
- Does not require coordination with another concurrent task to complete
- Can be validated independently
4. **Sequence dependencies** — where task B requires output from task A, mark the dependency. Everything else is parallel. 5. **Assign context boundaries** — each agent gets only the context it needs. Avoid stuffing all specs into every agent's context.
Part 2 — Skill routing
1. **Map each task to a skill** using the routing table below. If no skill matches exactly, use the closest and note the gap. 2. **Validate integration after parallel work** — run the full regression suite (`make check` or the project's equivalent convergence command) after all parallel tracks complete. Parallel work that skips integration validation is not done.
Skill routing table
| Task type | Route to skill | |---|---| | New feature / epic planning | `spec-first-delivery` | | Architecture decision or trade-off | `the-architect` | | High-stakes design review | `adversarial-gate` | | Pre-deployment go/no-go | `release-readiness` | | Release process governance, SemVer, ADR, changelog | `release-manager` | | Enterprise policy, compliance, or governance alignment | `governance-guardrail` | | GitHub repo operations: CI triggers, billing, issues, labels, branch protection | `github-manager` | | Google ADK agent patterns | `adk-expert` | | Cloud infrastructure guardrails | `gcp-expert` / `aws-expert` / `azure-expert` / `alibaba-expert` | | MCP server design or governance | `mcp-server-scaffold` | | Agent output validation | `domain-validator` | | PR or code review | `pr-reviewer` | | LLM cost or model selection | `cost-guardrail` | | Status or standup synthesis | `sitrep` | | Plugin-directory, marketplace, or curated-list submission | `plugin-submission` | | New repo or operating model install / repair | `operating-model-bootstrap` | | Delivery controls: source of truth, issue/PR conventions, DoD, escalation | `delivery-orchestrator` (own it here) | | Routing this list | `delivery-orchestrator` (you are here) |
Outputs
- Task breakdown: tracks, atomic tasks, dependencies
- Skill routing: task → skill mapping
- Parallel execution plan with integration validation step
- A brief orchestration summary for the human lead
Guardrails
- **Atomic means independently validatable.** If you cannot describe how to verify a task in isolation, it is not atomic — split it further or merge it with its dependency.
- **Parallel agents must not share mutable state without a coordination strategy.** If two tracks write to the same file or schema, they are not independent. Use worktrees or serialise them.
- **Integration validation is not optional.** Parallel work that skips the post-merge regression check is not complete.
- **Keep agent context lean.** Each agent gets its task spec and relevant ADRs, not the entire repo.
Read more
name: delivery-orchestrator description: Decompose an epic into atomic parallelizable tasks and route each task to the right skill. Use this as a meta-router when you have more than one skill available and need to decide which applies. Trigger at the start of any multi-track epic or when the skill count in your harness exceeds ~12.
Delivery Orchestrator
> **A skill for choosing skills.** Once your harness grows past a handful of skills, the agent needs a way to select the right one. This is that skill.
The orchestrator has two jobs: decompose work into the smallest independently executable units, then route each unit to the skill that owns it.
When to use
- Starting a new epic or multi-track piece of work
- When an agent is about to attempt everything in one context window
- When parallel execution across multiple agents is needed
- When you need to decide which skill applies to an incoming task
Procedure
Part 1 — Decomposition
1. **Read the spec or brief** — confirm a feature spec or HLD exists. If not, invoke `spec-first-delivery` first. 2. **Identify tracks** — group work into independent tracks (e.g. backend API, frontend, infrastructure, testing). Tracks can run in parallel. Dependencies between tracks must be explicit. 3. **Break each track into atomic tasks** — an atomic task is one that:
- Can be assigned to a single agent
- Has a clear input and a clear output
- Does not require coordination with another concurrent task to complete
- Can be validated independently
4. **Sequence dependencies** — where task B requires output from task A, mark the dependency. Everything else is parallel. 5. **Assign context boundaries** — each agent gets only the context it needs. Avoid stuffing all specs into every agent's context.
Part 2 — Skill routing
1. **Map each task to a skill** using the routing table below. If no skill matches exactly, use the closest and note the gap. 2. **Validate integration after parallel work** — run the full regression suite (`make check` or the project's equivalent convergence command) after all parallel tracks complete. Parallel work that skips integration validation is not done.
Skill routing table
| Task type | Route to skill | |---|---| | New feature / epic planning | `spec-first-delivery` | | Architecture decision or trade-off | `the-architect` | | High-stakes design review | `adversarial-gate` | | Pre-deployment go/no-go | `release-readiness` | | Release process governance, SemVer, ADR, changelog | `release-manager` | | Enterprise policy, compliance, or governance alignment | `governance-guardrail` | | GitHub repo operations: CI triggers, billing, issues, labels, branch protection | `github-manager` | | Google ADK agent patterns | `adk-expert` | | Cloud infrastructure guardrails | `gcp-expert` / `aws-expert` / `azure-expert` / `alibaba-expert` | | MCP server design or governance | `mcp-server-scaffold` | | Agent output validation | `domain-validator` | | PR or code review | `pr-reviewer` | | LLM cost or model selection | `cost-guardrail` | | Status or standup synthesis | `sitrep` | | Plugin-directory, marketplace, or curated-list submission | `plugin-submission` | | New repo or operating model install / repair | `operating-model-bootstrap` | | Delivery controls: source of truth, issue/PR conventions, DoD, escalation | `delivery-orchestrator` (own it here) | | Routing this list | `delivery-orchestrator` (you are here) |
Outputs
- Task breakdown: tracks, atomic tasks, dependencies
- Skill routing: task → skill mapping
- Parallel execution plan with integration validation step
- A brief orchestration summary for the human lead
Guardrails
- **Atomic means independently validatable.** If you cannot describe how to verify a task in isolation, it is not atomic — split it further or merge it with its dependency.
- **Parallel agents must not share mutable state without a coordination strategy.** If two tracks write to the same file or schema, they are not independent. Use worktrees or serialise them.
- **Integration validation is not optional.** Parallel work that skips the post-merge regression check is not complete.
- **Keep agent context lean.** Each agent gets its task spec and relevant ADRs, not the entire repo.
A team-project AI harness bootstrap that gives humans and agents a shared operating contract from day one, moving AI leverage from an individual “IC superhero” advantage to a repeatable team capability on an equal playing field.
Other skills on join-the-team.
- /adk-expert
Google ADK (Agent Development Kit) orchestration patterns — boundaries, agent composition, and tool seams. Trigger when designing or reviewing multi-agent systems built on ADK. Authoritative source: adk.dev.
Open skill - /adversarial-gate
JP's signature red-team pass — "how would I break this?" Argue against your own approach before proceeding. Trigger on any high-stakes decision, architecture choice, or before marking work complete.
Open skill - /alibaba-expert
Alibaba Cloud expert guardrails — RAM least-privilege, data boundaries, residency (mainland/international split), cost, and official-source validation. Trigger when designing or reviewing any Alibaba Cloud workload, especially agents or LLMs (Model Studio/Bailian, Qwen,
Open skill - /aws-expert
AWS expert guardrails — IAM least-privilege, data boundaries, cost controls, residency, and official-source validation. Trigger when designing or reviewing any AWS workload, especially agents, LLMs (Bedrock), or multi-account systems.
Open skill - /azure-expert
Azure expert guardrails — Entra ID least-privilege, policy-first governance, data boundaries, cost, residency, and official-source validation. Trigger when designing or reviewing any Azure workload, especially agents, LLMs (Foundry/OpenAI), or landing-zone systems.
Open skill - /cost-guardrail
LLM and cloud cost awareness — model tiering, token budgets, right-sizing, and when a cheaper model suffices. Trigger before finalising any architecture that calls LLMs, before scaling a workload, or when a cost estimate is needed.
Open skill

