Delivery orchestrator
Skill jpantsjoha/ai-native-developer-experience/.agents/skills/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 exceeds ~12.From its SKILL.md
npx -y skills add jpantsjoha/ai-native-developer-experience --skill delivery-orchestratorAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
2 things to look at
- 11 stars11 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
- runs commandsInstructs the agent to run 1 command, including `make check`.
SKILL.md
4.2 KB, 894 tokens by cl100k_base, as published. Nobody here has run it
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
- Read the spec or brief — confirm a feature spec or HLD exists. If not, invoke
spec-first-deliveryfirst. - 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.
- 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
- Sequence dependencies — where task B requires output from task A, mark the dependency. Everything else is parallel.
- Assign context boundaries — each agent gets only the context it needs. Avoid stuffing all specs into every agent's context.
Part 2 — Skill routing
- Map each task to a skill using the routing table below. If no skill matches exactly, use the closest and note the gap.
- Validate integration after parallel work — run the full regression suite (
make checkor 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.
What ships with it
Read from the repository
Just SKILL.md. No reference files, no scripts.
Gives 0 of the 12 instructions most agent orchestration skills give in 894 tokens
Counted across 848 of the 1,300 authors here whose files we hold, read 2026-09-06
- Dispatch one agent per independent problem domainin 56 of 848, across 42 files
- Run full test suite after integrationin 55 of 848, across 42 files
- Verify fixes do not conflictin 40 of 848, across 32 files
- Review each summary when agents returnin 40 of 848, across 31 files
- Write a handoff document summarising the current conversationin 30 of 848, across 25 files
- Reference existing artifacts by path or URLin 26 of 848, across 24 files
- Give each agent a specific scopein 19 of 848, across 10 files
- Give each agent a clear goalin 19 of 848, across 10 files
- Include a suggested skills section in the documentin 18 of 848, across 16 files
- Tailor the doc to the user argumentsin 18 of 848, across 15 files
- Issue all subagent dispatches in the same responsein 17 of 848, across 11 files
- Use git worktrees for isolationin 17 of 848, across 8 files
Said here and by no other author read
- Read the spec or brief
- Identify independent tracks
- Break each track into atomic tasks
- Sequence dependencies
- Assign context boundaries
- Map each task to a skill
Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.