Data Structure Protocol (DSP)

The missing memory layer for AI-assisted development

---

The problem

Your agent re-reads the same codebase every session. DSP fixes that.

Every time you start a new task, your AI coding agent spends the first 5–15 minutes "getting oriented" — scanning files, tracing imports, figuring out what depends on what. On large projects this becomes a constant tax on tokens and attention. Context is rebuilt from scratch, every single time.

DSP is a graph-based long-term structural memory stored in .dsp/. It gives agents a persistent, versionable map of your codebase — entities, dependencies, public APIs, and the reasons behind every connection — so they can pick up exactly where they left off.

DSP is not another workflow framework. It's the persistent structural memory layer that's missing from every AI coding workflow.

---

Install

macOS / Linux:

curl -fsSL https://raw.githubusercontent.com/k-kolomeitsev/data-structure-protocol/main/install.sh | bash

Windows:

irm https://raw.githubusercontent.com/k-kolomeitsev/data-structure-protocol/main/install.ps1 | iex

Codex:

$skill-installer install https://github.com/k-kolomeitsev/data-structure-protocol/tree/main/skills/data-structure-protocol

---

What you get

• Agent stops re-learning your project every session — structural context persists across tasks, sessions, and even team members
• Dependency discovery in seconds, not minutes — graph traversal replaces full-repo scanning
• Impact analysis before refactors — know what breaks before you touch it
• Safer changes on brownfield codebases — hidden couplings become visible edges in the graph
• Works with Claude Code, Cursor, Codex — no lock-in — DSP is an agent skill, not a platform
• Git-native and versionable — .dsp/ is plain text, diffs cleanly, reviews like code

Honest trade-off: bootstrapping DSP on a large project takes real effort (time, tokens, discipline). It pays back over the project lifetime through lower per-task token usage, faster discovery, and more predictable agent behavior.

---

How it works

┌──────────────────────┐
│      Codebase        │
│  (files + assets)    │
└──────────┬───────────┘
           │  create/update graph as you work
           ▼
┌──────────────────────┐
│   DSP Builder / CLI  │
│   (dsp-cli.py)       │
└──────────┬───────────┘
           │  writes
           ▼
┌──────────────────────┐
│        .dsp/         │
│ entity graph + whys  │
└──────────┬───────────┘
           │  reads/searches/traverses
           ▼
┌──────────────────────┐
│   LLM Orchestrator   │
│ (your agent + skill) │
└──────────────────────┘

As you work, DSP builds a lightweight graph of your codebase: modules, functions, dependencies, and public APIs. Each connection carries a why — the reason it exists. Your agent reads this graph instead of re-scanning the repo, navigates structure through graph traversal, and keeps the graph updated as code evolves.

The graph lives in .dsp/ — plain text files that commit, diff, and merge like any other source artifact.

---

Quick start

Option A: Start from the boilerplate (fastest)

dsp-boilerplate is a production-ready fullstack starter — NestJS 11 + React 19 + Vite 7 in Docker Compose, with a fully initialized DSP graph, pre-configured skills for all agents, Cursor rules, git hooks, and CI.

git clone https://github.com/k-kolomeitsev/dsp-boilerplate.git my-project
cd my-project
docker-compose up -d

Everything is wired: .dsp/ graph with two roots (backend + frontend), @dsp markers in all source files, DSP skills for Cursor, Claude Code, and Codex. You can start coding and the agent already knows the entire project structure.

Option B: Add DSP to any project

1. Initialize

python dsp-cli.py --root . init

2. Create entities

python dsp-cli.py --root . create-object "src/app.ts" "Main application entrypoint"
# → obj-a1b2c3d4

python dsp-cli.py --root . create-function "src/app.ts#start" "Starts the HTTP server" --owner obj-a1b2c3d4
# → func-7f3a9c12

python dsp-cli.py --root . add-import obj-a1b2c3d4 obj-deadbeef "HTTP routing"

3. Navigate

python dsp-cli.py --root . search "authentication"
python dsp-cli.py --root . find-by-source "src/auth/index.ts"
python dsp-cli.py --root . get-children obj-a1b2c3d4 --depth 2

4. Impact analysis

python dsp-cli.py --root . get-parents obj-a1b2c3d4 --depth inf
python dsp-cli.py --root . get-recipients obj-a1b2c3d4

Before any refactor, run get-parents or get-recipients to see everything that depends on the entity you're about to change.

---

Supported agents

DSP installs as a skill for your agent. Pick your agent and scope.

Don't have a coding agent yet? Install one first:

Agent	Install
Claude Code	npm i -g @anthropic-ai/claude-code — docs
Cursor	cursor.com/downloads — docs
Codex CLI	npm i -g @openai/codex — docs | github

macOS / Linux

Agent	Project Install	Global Install
Cursor	curl -fsSL https://raw.githubusercontent.com/k-kolomeitsev/data-structure-protocol/main/install.sh | bash -s -- cursor	curl -fsSL https://raw.githubusercontent.com/k-kolomeitsev/data-structure-protocol/main/install.sh | bash -s -- --global cursor
Claude Code	curl -fsSL https://raw.githubusercontent.com/k-kolomeitsev/data-structure-protocol/main/install.sh | bash -s -- claude	curl -fsSL https://raw.githubusercontent.com/k-kolomeitsev/data-structure-protocol/main/install.sh | bash -s -- --global claude
Codex	curl -fsSL https://raw.githubusercontent.com/k-kolomeitsev/data-structure-protocol/main/install.sh | bash -s -- codex	curl -fsSL https://raw.githubusercontent.com/k-kolomeitsev/data-structure-protocol/main/install.sh | bash -s -- --global codex

Windows

# Project-level (current directory)
irm https://raw.githubusercontent.com/k-kolomeitsev/data-structure-protocol/main/install.ps1 | iex

# With specific agent
powershell -ExecutionPolicy Bypass -File install.ps1 -Agent cursor
powershell -ExecutionPolicy Bypass -File install.ps1 -Agent claude
powershell -ExecutionPolicy Bypass -File install.ps1 -Agent codex

# Global (user-level)
powershell -ExecutionPolicy Bypass -File install.ps1 -Agent cursor -Global

Codex (alternative)

$skill-installer install https://github.com/k-kolomeitsev/data-structure-protocol/tree/main/skills/data-structure-protocol

Project install puts the skill in your repo (.cursor/skills/, .claude/skills/, .codex/skills/). Global install puts it in your home directory so it's available across all projects.

---

DSP vs alternatives

Modern agents already know how to plan, write tests, verify, and ship. They don't need process wrappers. What they lack is memory.

	DSP	GSD	Superpowers
Core idea	Persistent structural memory	Process/confidence wrapper	Engineering discipline (TDD)
What it solves	Agent has no memory of project between sessions	Agent doesn't follow structured workflow	Agent might skip tests/planning
Is the problem real?	Yes — no model has built-in project memory	Diminishing — modern models plan and verify natively	Diminishing — modern models know TDD when prompted
Persistent memory	Full graph across sessions	None	None
Impact analysis	Built-in (graph traversal)	No	No
Brownfield	First-class	One-time scan	No explicit support
Overhead	Low	Medium	Medium

Modern agents are smarter than most mid-level engineers. They plan, they test, they verify. They just can't remember your project. DSP is the fix. Detailed comparison with GSD | Detailed comparison with Superpowers

---

Core concepts

Concept	What it is
Entity	A node in the graph. Either an Object (module/file/class/config/external dep) or a Function (function/method/handler)
UID	Stable identifier (obj-<8hex>, func-<8hex>). File paths are attributes, not identity — entities survive renames and moves
imports	Outgoing edges — what this entity uses, with a why for each connection
shared	Public API of an object — what it exposes to consumers
exports/	Reverse index — who imports this entity and why (incoming edges)
TOC	Per-entrypoint table of contents listing all reachable entities from a root

UID markers anchor identity in source code:

// @dsp func-7f3a9c12
export function calculateTotal(items: Item[]): number { /* ... */ }

# @dsp func-3c19ab8e
def process_payment(order):
    ...

---

Storage format

.dsp/ is plain text in a deterministic directory layout:

.dsp/
├── TOC                        # Table of contents (single root)
├── TOC-<rootUid>              # One TOC per root (multi-root projects)
├── obj-a1b2c3d4/              # Object entity
│   ├── description            # source, kind, purpose
│   ├── imports                # imported UIDs (one per line)
│   ├── shared                 # exported/shared UIDs (one per line)
│   └── exports/               # reverse index
│       ├── <importer_uid>     # why the whole object is imported
│       └── <shared_uid>/      # per shared entity
│           ├── description    # what is exported
│           └── <importer_uid> # why this shared is imported
└── func-7f3a9c12/             # Function entity
    ├── description
    ├── imports
    └── exports/
        └── <owner_uid>        # ownership link

Full specification: ARCHITECTURE.md

---

Git hooks & CI

DSP ships with hooks that keep the graph in sync with your code:

Hook	What it does	LLM required
pre-commit	Checks staged files against DSP graph — flags new files without entities, deleted files still referenced, orphans	No
pre-push	Full graph integrity — orphan detection, cycle detection, stats summary	No
Agent-assisted review	Deep semantic analysis of changes against DSP entities, dependency impact	Yes

Install hooks:

./hooks/install-hooks.sh          # macOS/Linux
.\hooks\install-hooks.ps1         # Windows

See hooks/ for configuration, standalone scripts, and GitHub Actions integration.

---

Integration packs

Ready-made configurations for each supported agent:

Agent	Skill location
Cursor	.cursor/skills/data-structure-protocol/
Claude Code	.claude/skills/data-structure-protocol/
Codex	.codex/skills/data-structure-protocol/

Each integration includes the skill instructions (SKILL.md), CLI (dsp-cli.py), and reference docs. See integrations/ for agent-specific setup guides.

---

Documentation

Document	Description
dsp-boilerplate	Fullstack boilerplate (NestJS + React + Docker Compose) with DSP pre-initialized — the fastest way to start
GETTING_STARTED.md	Step-by-step guide from install to first impact analysis
ARCHITECTURE.md	Full protocol specification — entity model, storage format, operations
docs/comparisons/	Detailed comparisons with GSD, Superpowers, and other tools
docs/workflows/	Workflow guides — bootstrap, brownfield adoption, team usage
integrations/	Agent-specific integration guides and configurations

---

Contributing

Contributions are welcome. Areas where help is most valuable:

• Architecture spec — improving ARCHITECTURE.md
• CLI — keeping dsp-cli.py aligned with the spec
• Skill instructions — refining SKILL.md for agent clarity
• New integrations — adding support for more agents and editors
• Documentation — examples, workflow guides, comparisons

Please keep changes minimal, explicit, and consistent with the "minimal sufficient context" philosophy.

---

License

Apache License 2.0 — see LICENSE.