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project-development

Plan LLM project architectures with a five-stage pipeline and file-system state tracking.

Updated Jan 19, 2026
Search Tags:#automation#architecture#debugging#LLM#project-management#pipeline-design
One-click install
npx skills add https://github.com/illogical/InstructionLibrary --skill project-development-illogical
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: project-development
Source: https://github.com/illogical/InstructionLibrary/tree/main/skills/project-development
Command: npx skills add https://github.com/illogical/InstructionLibrary --skill project-development-illogical

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill guides teams through evaluating task-model fit for LLM projects, selecting architectures, and iterating rapidly with agent-assisted development. It explains when to start with manual prototyping, how to structure an LLM pipeline, and how to use a file-system-based state model to keep work transparent and debuggable.

Core Features & Use Cases

  • Manual validation: Quick checks of task-model fit before automating with code.
  • Pipeline design: Guidance for a canonical pipeline (acquire → prepare → process → parse → render) and deterministic state management.
  • Architectural decisioning: Advice on single-agent vs multi-agent approaches and how to apply architectural reduction patterns in practice.

Quick Start

Start with a small, representative task; perform a manual prototype to validate model fit; map the five-stage pipeline; implement a minimal data directory structure (data/ and output/), and run the basic workflow to verify idempotent stages.

Frequently Asked Questions about project-development

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

FAQPage Schema
How do I plan the architecture for an LLM-driven project pipeline?▼

Validate task-model fit by starting with a small, representative task and performing a manual prototype before writing automation code. This quick check ensures the LLM handles the task effectively before you commit to pipeline development.

When should I use a multi-agent setup versus a single-agent approach for LLM automation?▼

Track LLM pipeline state by implementing a minimal file-system-based data directory structure using data/ and output/ folders. This transparent state management approach ensures pipeline stages remain idempotent, debuggable, and easy to inspect.

What is the best way to debug an LLM pipeline that is producing non-deterministic outputs?▼

Debug non-deterministic LLM pipelines by adopting a file-system-based state tracking approach combined with the canonical five-stage pipeline design. This enforces deterministic, transparent workflows where every stage can be independently inspected and replayed.

Can I use this pipeline design approach for both prototypes and production LLM applications?▼

Yes, this pipeline design approach applies to both prototypes and production LLM applications. It scales from quick manual prototype validation to complex batch pipelines and interactive agent apps by maintaining consistent state management and architectural patterns.

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