discipline-refactor-orchestration-architecture
Sub-skill of discipline-refactor: Orchestration Architecture.
Best use case
discipline-refactor-orchestration-architecture is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Sub-skill of discipline-refactor: Orchestration Architecture.
Teams using discipline-refactor-orchestration-architecture should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/orchestration-architecture/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How discipline-refactor-orchestration-architecture Compares
| Feature / Agent | discipline-refactor-orchestration-architecture | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Sub-skill of discipline-refactor: Orchestration Architecture.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
Related Guides
SKILL.md Source
# Orchestration Architecture
## Orchestration Architecture
```
┌─────────────────────────────────────────────────────────────┐
│ ORCHESTRATOR: discipline-refactor skill │
│ (Stays lean, delegates all execution) │
└─────────────────────────────────────────────────────────────┘
│
┌───────────────────┼───────────────────┐
▼ ▼ ▼
┌───────────────┐ ┌───────────────┐ ┌───────────────┐
│ Phase 1 │ │ Phase 2 │ │ Phase 3 │
│ ANALYSIS │ │ PLANNING │ │ EXECUTION │
│ Explore │ │ Plan │ │ general-purpose│
│ │ │ + skill-creator│ │ + git-sync-mgr│
└───────────────┘ └───────────────┘ └───────────────┘
│
▼
┌───────────────────────┐
│ Phase 4: VALIDATION │
│ Bash (run tests) │
└───────────────────────┘
```
---Related Skills
improve-codebase-architecture
Find deepening opportunities in a codebase, informed by the domain language in CONTEXT.md and the decisions in docs/adr/. Use when the user wants to improve architecture, find refactoring opportunities, consolidate tightly-coupled modules, or make a codebase more testable and AI-navigable.
multi-file-tax-prep-orchestration
Structured approach to complex multi-file tax return preparation with traceability and planning
label-driven-prompt-generation-architecture
Pattern for building automation scripts that classify GitHub issues into prompt templates using label-based routing and extract contextual data for batch processing
repo-architecture-analysis
Scan a Python repo's package structure, count classes/functions, classify module maturity (PRODUCTION/DEVELOPMENT/SKELETON/GAP), and generate architecture reports with Mermaid diagrams. Use when asked to analyze codebase structure, find untested packages, or assess module maturity.
doc-extraction-naval-architecture
Layer 3 domain sub-skill for extracting naval architecture data from SNAME PNA, IMO stability codes, IACS structural rules, and classification society guidelines. Provides detection heuristics for stability constants, resistance equations, hull form coefficients, hydrostatic curves, IMO stability criteria, and structural scantling tables. type: reference
sparc-architecture
SPARC Architecture phase specialist for system design, component architecture, interface design, scalability planning, and technology selection
architecture-diagram
Dark-themed SVG architecture/cloud/infra diagrams as HTML.
multi-tool-architecture-assessment
Systematic comparison of competing tools/approaches before committing to a multi-account, multi-tool architecture. Uses parallel subagents for research, system-state audit, and data quality analysis. Produces a decision matrix with explicit trade-offs.
licensed-machine-prompt-orchestration
Design self-contained prompts for licensed machines (Windows, no Hermes) that Codex / Codex / Gemini CLIs can execute autonomously. Covers fixture generation, solver validation, and cross-machine data bridging.
modular-architecture-documentation
Systematically document multi-module system architectures including module boundaries, CLI commands, and architecture decisions.
codex-background-burn-orchestration
Run quota-aware Codex usage-burn waves as useful background issue-execution lanes, including Hermes stdin-close and sandbox recovery patterns.
module-based-refactor
Reorganize a repository from flat structure to a module-based 5-layer architecture (src/tests/specs/docs/examples) while preserving git history. Use when restructuring a codebase into modules, migrating import paths, cleaning up hidden folders, consolidating duplicate directories, removing root-level artifacts, or archiving completed plan files. Capabilities: parallel agent spawn strategy, hidden-folder consolidation patterns, benchmark fixture separation, 4-phase atomic commit workflow.