Complete AI TrainingYourJobSkills for your job

Skills / data-science

data-structure-protocol

Give agents persistent structural memory of a codebase — navigate dependencies, track public APIs, and understand why connections exist without re-reading the whole repo.

Data & AnalyticsScience & Research

Data Structure Protocol (DSP)

LLM coding agents lose context between tasks. On large codebases they spend most of their tokens on "orientation" — figuring out where things live, what depends on what, and what is safe to change. DSP solves this by externalizing the project's structural map into a persistent, queryable graph stored in a .dsp/ directory next to the code.

DSP is NOT documentation for humans and NOT an AST dump. It captures three things: meaning (why an entity exists), boundaries (what it imports and exposes), and reasons (why each connection exists). This is enough for an agent to navigate, refactor, and generate code without loading the entire source tree into the context window.

When to Use

Use this skill when:

  • The project has a .dsp/ directory (DSP is already set up)
  • The user asks to set up DSP, bootstrap, or map a project's structure
  • Creating, modifying, or deleting code files in a DSP-tracked project (to keep the graph updated)
  • Navigating project structure, understanding dependencies, or finding specific modules
  • The user mentions DSP, dsp-cli, .dsp, or structure mapping
  • Performing impact analysis before a refactor or dependency replacement

Core Concepts

Code = graph

DSP models the codebase as a directed graph. Nodes are entities, edges are imports and shared/exports.

Two entity kinds exist:

  • **Obje

Subscribers only

The full skill, its 1 bundled files and every download is included with every paid Complete AI plan.

Details

Sourcek-kolomeitsev/data-structure-protocol
License
Risk labelsafe ("critical" means the skill may run commands or touch files — read before use)
FilesSKILL.md
Added2026-02-27

Related skills

data-engineering-data-driven-feature

Build features guided by data insights, A/B testing, and continuous measurement using specialized agents for analysis, implementation, and experimentation.

data-engineering-data-pipeline

You are a data pipeline architecture expert specializing in scalable, reliable, and cost-effective data pipelines for batch and streaming data processing.

data-quality-frameworks

Implement data quality validation with Great Expectations, dbt tests, and data contracts. Use when building data quality pipelines, implementing validation rules, or establishing data contracts.

data-scientist

Expert data scientist for advanced analytics, machine learning, and statistical modeling. Handles complex data analysis, predictive modeling, and business intelligence.

data-storytelling

Transform raw data into compelling narratives that drive decisions and inspire action.

plotly

Interactive visualization library. Use when you need hover info, zoom, pan, or web-embeddable charts. Best for dashboards, exploratory analysis, and presentations. For static publication figures use matplotlib or scientific-visualization.