Selects architecture paradigm via research before scaffolding. Use when architecture is undecided and the choice needs justification and documentation.
npx skills add https://github.com/athola/claude-night-market --skill architecture-aware-init
Project initialization that combines online research, archetype
selection, template customization, and decision documentation
into one workflow. Use this skill when the architecture is
undecided and the choice deserves justification.
Use instead of project-init when architecture is undecided.
Use before project-specification to establish the
architectural foundation.
arch-init:research-completed: Online research completedarch-init:paradigm-selected: Architecture paradigm chosenarch-init:templates-customized: Templates adapted to paradigmarch-init:decision-recorded: ADR createdLoad modules/research-flow.md for the full project-context
questionnaire and the three-tier search strategy. Output: a
synthesis brief that feeds Step 3.
Load modules/paradigm-selection.md for the decision matrix
(team size by domain complexity) and the special-case overrides
(streaming, serverless, microkernel, etc.). Two routes:
archetypes:architecture-paradigms skill for guidedexploration.
Load modules/scaffold-generation.md for the paradigm-specific
directory layouts (Functional Core / Hexagonal / Microservices
shown; others delegated to the corresponding
archetypes:architecture-paradigm-{name} skill) and the ADR
template.
After completing the workflow, the project has:
tooling, dependency hints)
implementation guidance
The interactive workflow above is the default. For automation,
load modules/script-integration.md for the three Python
helpers under plugins/attune/scripts/ (architecture researcher,
template customizer, full interactive flow) and library-style
import examples.
This skill enhances /attune:project-init by adding an
architecture-selection phase before scaffolding:
# Standard initialization (no architecture decision)
/attune:project-init --lang python --name my-project
# Architecture-aware initialization
/attune:brainstorm # Explore project needs
Skill(architecture-aware-init) # Select architecture
/attune:project-init --arch <paradigm> --name my-project
User: "I'm creating a Python web API for a fintech application.
Team of 8 developers, complex business rules, need high security
and audit trails."
engineers, security and auditability requirements.
architecture, CQRS+ES Python examples.
Event Sourcing.
module, event store, aggregate patterns, projection handlers.
rules, regulatory compliance).
Result: project initialized with paradigm-appropriate structure
and clear decision rationale.
Skill(archetypes:architecture-paradigms): paradigm catalogSkill(archetypes:architecture-paradigm-*): per-paradigmimplementation guidance
Skill(attune:project-brainstorming): ideation beforearchitecture
Skill(attune:project-specification): requirements afterthe paradigm is chosen
/attune:project-init: basic project initialization/attune:blueprint: architecture planning after paradigmselection
plugins/archetypes/README.md: full paradigm referencearch-init:research-completed, arch-init:paradigm-selected,
arch-init:templates-customized, arch-init:decision-recorded.
chosen, referencing evidence from the Step 2 research synthesis.
archetypes:architecture-paradigm-* skill (verified by listing the created directories).
with trade-offs and waits for explicit user selection rather than defaulting silently.
Comprehensive toolkit for creating, analyzing, and visualizing complex networks and graphs in Python. Use when working with network/graph data structures, analyzing relationships between entities, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks, or visualizing network topologies. Applicable to social networks, biological networks, transportation systems, citation networks, and any domain involving pairwise relationships.
Interact with Zotero reference management libraries using the pyzotero Python client. Retrieve, create, update, and delete items, collections, tags, and attachments via the Zotero Web API v3. Use this skill when working with Zotero libraries programmatically, managing bibliographic references, exporting citations, searching library contents, uploading PDF attachments, or building research automation workflows that integrate with Zotero.
Comprehensive toolkit for creating, analyzing, and visualizing complex networks and graphs in Python. Use when working with network/graph data structures, analyzing relationships between entities, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks, or visualizing network topologies. Applicable to social networks, biological networks, transportation systems, citation networks, and any domain involving pairwise relationships.
Create, analyze, and visualize complex networks and graphs in Python with NetworkX. Use when working with network/graph data structures, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks (random, scale-free, small-world), reading/writing graph file formats, or drawing network topologies. Common applications include social, biological, transportation, and citation networks.
Use NeuroKit2 to build or audit reproducible research workflows for physiological time-series preprocessing, event/interval analysis, multimodal alignment, variability, and complexity. Trigger when code imports neurokit2 or needs its current APIs, schemas, and method-aware validation—not for diagnosis or device validation.
Interact with Zotero reference management libraries using the pyzotero Python client. Retrieve, create, update, and delete items, collections, tags, and attachments via the Zotero Web API v3. Use this skill when working with Zotero libraries programmatically, managing bibliographic references, exporting citations, searching library contents, uploading PDF attachments, or building research automation workflows that integrate with Zotero.
Comprehensive toolkit for creating, analyzing, and visualizing complex networks and graphs in Python. Use when working with network/graph data structures, analyzing relationships between entities, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks, or visualizing network topologies. Applicable to social networks, biological networks, transportation systems, citation networks, and any domain involving pairwise relationships.
A practical, jargon-free guide to fp-ts functional programming - the 80/20 approach that gets results without the academic overhead. Use when writing TypeScript with fp-ts library.
Take athola/architecture-aware-init from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.