data-structure-protocol

Overview of data-structure-protocol skill

What this skill does

The data-structure-protocol skill helps an agent build and navigate DSP, a graph-based memory layer for codebases. It records modules, functions, imports, exports, and the reasons links exist, so the model can answer “what exists, what depends on it, and why” instead of guessing from filenames alone.

Who should install it

Use the data-structure-protocol skill if your project already has a .dsp/ directory, if you need to bootstrap DSP in a repo, or if you want the agent to make safer code changes by checking structural context first. It is especially useful for maintainers, agentic coding workflows, and repos where dependency boundaries matter more than prose docs.

Why it’s different

This is not a generic prompt wrapper. The data-structure-protocol skill is built around a concrete storage model, a CLI, and a bootstrap workflow that turns real files into an entity graph. That makes it more decision-useful than an ordinary “understand this repo” prompt, because it tells the agent what to read, what to verify, and when not to register dead imports.

How to Use data-structure-protocol skill

Install and confirm the repo shape

Install the data-structure-protocol skill with:
npx skills add k-kolomeitsev/data-structure-protocol --skill data-structure-protocol

After install, confirm the repo contains SKILL.md, references/, and scripts/dsp-cli.py. The repository supports a practical workflow around bootstrap, operations, and storage-format, so those files matter more than a quick skim of the README would in a typical project.

Give the skill a concrete task

The skill works best when you state the repo state and the job-to-be-done. Good input includes the project root, whether .dsp/ already exists, and the type of change you plan to make. For example: “This repo uses DSP and I need to add a new module; first identify affected entities, then tell me which files and UIDs need updates.”

For data-structure-protocol usage, avoid vague requests like “help me with DSP.” Instead specify whether you need bootstrap, navigation, updates, or cleanup. The skill is optimized for structural decisions, not broad architecture advice.

Read the right files first

Start with SKILL.md to understand the agent prompt and core rules. Then read references/bootstrap.md for the DFS bootstrap flow, references/operations.md for create/update/read commands, and references/storage-format.md for UID and folder structure. If you plan to use the CLI directly, inspect scripts/dsp-cli.py for actual command behavior and edge-case handling.

Workflow that produces better output

A reliable data-structure-protocol guide flow is: confirm root entrypoints, read affected entities, verify imports used in file bodies, then create or update DSP records. When asking the model to act, include enough source context to identify the entrypoint, exported surface, and any modules whose relationships are changing. That is the information the protocol uses to keep the graph accurate.

data-structure-protocol skill FAQ

Is this only for projects that already use DSP?

No. The skill is useful both for existing .dsp/ projects and for bootstrap work. If the repo has no graph yet, data-structure-protocol can still guide how to map root files and dependencies into DSP correctly.

Is this better than a normal prompt?

Usually yes when structure matters. A normal prompt may summarize code, but data-structure-protocol is designed to preserve long-term structural memory: entities, imports, exports, and reasons. If your task depends on accurate cross-file relationships, the skill is a better fit than a one-off explanation prompt.

Can beginners use it?

Yes, if they can identify the project root and describe the change they want. The main learning curve is understanding that DSP tracks code structure, not human-facing documentation. If you cannot tell which files are entrypoints or which imports are actually used, the skill may need more repository context before it can help.

When should I not use it?

Skip data-structure-protocol if your task is mostly prose editing, UI copy, or a one-file toy script where graph memory adds little value. It is also a poor fit when you do not have source access, cannot inspect the repo tree, or do not plan to maintain DSP metadata after the first pass.

How to Improve data-structure-protocol skill

Provide the inputs the graph needs

The best data-structure-protocol skill results come from precise source references: entrypoint files, changed modules, and the specific symbol or import being added or removed. If possible, name the root file and the affected path, such as src/app.py, lib/index.ts, or cmd/main.go, so the agent can map the update to the right entity.

Watch for common failure modes

The biggest failure mode is treating DSP like generic documentation and skipping verification. Another is registering imports that are present in the import block but unused in the file body. The bootstrap and operations docs show that data-structure-protocol cares about verified relationships, so ask the model to check actual usage before it records links.

Iterate after the first pass

If the first output is too broad, narrow the prompt to one phase: bootstrap, search, read, or update. For data-structure-protocol for Skill Authoring, that usually means: first ask for affected entities and boundary checks, then ask for the exact DSP operations or file edits. Iteration works best when each step has one clear structural goal and one repo area.