architect-orchestrator

Orchestrates multi-agent delegation, topology selection, and deterministic Loop/Graph execution for material work.

3|Updated Jul 28, 2026
One-click install
npx skills add https://github.com/marcmarti9/agentit --skill architect-orchestrator-marcmarti9
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: architect-orchestrator
Source: https://github.com/marcmarti9/agentit/tree/main/skills/architect-orchestrator
Command: npx skills add https://github.com/marcmarti9/agentit --skill architect-orchestrator-marcmarti9

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Complex engineering tasks often fail when a single agent tries to do everything serially, or when delegation happens without bounded context, independent review, or verifiable completion. This Skill gives the primary AI a just-in-time orchestration protocol for deciding when and how to delegate, compare structural alternatives, and enforce deterministic execution contracts. ## Core Features & Use Cases - Topology Selection: Choose from direct, probe, fan_out, pipeline, writer_reviewer, audit, or custom DAG topologies based on dependencies, ownership, and useful independence rather than a fixed hierarchy. - Bounded Worker Contracts: Project a least-privilege context envelope for each delegated worker, including objective, scope, selected skill bodies, write ownership, verifier, and stop condition. - Deterministic Loop/Graph Runtime: Declare Loop Contracts with goal, verifier, attempt budget, and escalation boundary, and materialize DAGs that reject cycles and overlapping write ownership before execution. - Use Case: When refactoring a service with an expensive-to-reverse persistence seam, use this Skill to compare genuinely different structural alternatives, fan out independent research workers, and verify the final implementation with a command-bound loop receipt instead of narrative confidence. ## Quick Start Ask the agent to use the architect-orchestrator skill to decompose this task, delegate independent work to bounded workers, and verify completion with a deterministic loop contract.

Frequently Asked Questions about architect-orchestrator

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I orchestrate multiple AI agents for a coding task?▼

Define a reviewed task decision covering outcome, unknowns, topology, and verification, then delegate workers with bounded context contracts specifying objective, scope, write ownership, and stop conditions. Choose a topology like fan_out, pipeline, or writer_reviewer based on actual dependencies.

When should I delegate work to subagents instead of working directly?▼

Delegate when there are large independent reading sets, distinct ownership boundaries, different expertise needs, or fresh-context critique value. Stop when coordination costs exceed the expected benefit or when remaining work is a tightly coupled integration decision.

What is a Loop Contract in agent runtime execution?▼

A Loop Contract declares a goal, verifier, stop condition, attempt budget, and escalation boundary before action. Retries require fresh evidence or a different strategy, and exhausted routes escalate instead of looping indefinitely.

Does multi-agent orchestration require a fixed agent hierarchy?▼

No. Agent roles are capabilities, not a mandatory org chart. The primary AI owns decomposition and integration, and topologies like direct, probe, or custom DAGs are options selected per task, not required stages.

Why is self-reported completion not accepted for executable tasks?▼

A manually reported passing result is not observed execution. Executable acceptance requires a command-bound loop with a verifier argument, followed by loop-run and loop-check producing a fresh evidence receipt.