gsd-lite 1.13.1

A minimal protocol for Getting Things Done with AI agents

GSD-Lite

A framework for pair programming with AI agents.

GSD-Lite turns AI agents into thinking partners — collaborators who challenge your assumptions, teach you concepts, and help you own every decision. It's not about getting the agent to write code faster. It's about ensuring you understand the "why" behind every line.

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The Core Idea

Most AI workflows treat the agent as a task executor: you command, it obeys. This produces code you don't fully understand and decisions you can't defend.

GSD-Lite flips the script. The agent becomes a Navigator who proposes, challenges, and teaches. You remain the Driver who decides, approves, and owns the outcome.

graph LR
    subgraph "Traditional AI Workflow"
        U1[User] -->|Command| A1[Agent]
        A1 -->|Code| U1
    end

    subgraph "GSD-Lite Pair Programming"
        U2[Driver - You] <-->|Dialogue| A2[Navigator - Agent]
        A2 -->|Proposes| U2
        U2 -->|Decides| A2
        A2 -.->|Logs| M[Memory - Artifacts]
        M -.->|Resumes| A2
    end

The result: You can explain every decision to your team lead, debug at 2am without the agent, and onboard the next engineer with logs that read like documentation.

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Philosophy

1. Thinking Partners, Not Task Executors

The agent's job is to sharpen your thinking, not replace it.

Agent as Navigator	You as Driver
Proposes hypotheses ("What if we tried X?")	Makes all key decisions
Challenges assumptions ("Why do you think that?")	Owns the outcome
Teaches concepts with analogies	Approves or rejects proposals
Explains why it's asking questions	Curates what gets logged

2. The Grounding Loop (Search → Echo → Verify)

Agents love to jump from "I found the file" to "Here's the refactored code." GSD-Lite enforces a strict loop to prevent this Eager Executor anti-pattern:

graph LR
    S[Search] --> E[Echo]
    E --> V{Verify}
    V -->|Mismatch| S
    V -->|Match| P[Plan/Execute]

1. Search: Agent uses tools to find files or content.
2. Echo: Agent reports exactly what it found (file, line, content).
3. Verify: You confirm if this matches your intent.
4. Execute: Only then does the agent propose a plan or write code.

Tool output is evidence for discussion, not permission to execute.

3. Universal Onboarding (Every Session is Fresh)

AI agents have no memory between sessions. GSD-Lite treats every session as a "fresh agent" boot. The agent must read the artifacts in order:

1. PROTOCOL.md — The rules and router
2. PROJECT.md — The "why" (vision, success criteria)
3. ARCHITECTURE.md — The "how" (tech stack, entry points)
4. WORK.md — The "where" (current state, next action)

By the time the agent loads a workflow, it has full context to ask intelligent questions. No skipping.

4. Echo-Back Protocol (Prove Understanding First)

Before executing, agents must prove they understand the project. They echo back the vision and architecture in their own words. You verify. Only then do they earn the right to write code.

This prevents the "forwarder problem" — agents who execute commands without context.

5. Perpetual Memory (WORK.md Never Dies)

Traditional approaches delete session logs after each phase. GSD-Lite keeps WORK.md perpetually until you explicitly request archiving.

Why this matters:

• Extract PR descriptions from logs at any point
• Resume work weeks later without re-explaining context
• Full evidence trail with code snippets showing exactly what was built

You control cleanup via the housekeeping workflow, not automatic deletion.

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How It Works

The Session Lifecycle

graph TD
    Start([New Session]) --> Boot[Universal Onboarding]
    Boot --> Echo[Echo-Back: Prove Understanding]
    Echo --> Route{Current Mode?}

    Route -->|none/discuss| Discuss[discuss.md]
    Route -->|execution| Execute[execution.md]
    Route -->|checkpoint| Checkpoint[checkpoint.md]

    Discuss -->|Plan Approved| Execute
    Execute -->|Session End| Checkpoint
    Execute -->|Write PR| Housekeeping[housekeeping.md]

    Checkpoint --> Resume([Next Session])
    Housekeeping -->|Archive| History[HISTORY.md]

Artifacts at a Glance

File	Purpose	Lifecycle
PROTOCOL.md	Rules + router (immutable)	Never changes
PROJECT.md	Vision + success criteria	Updated when vision evolves
ARCHITECTURE.md	Tech stack + entry points	Updated when codebase changes
WORK.md	Session state + detailed log	Perpetual until archived
INBOX.md	Open loops + parked ideas	Cleared when resolved
HISTORY.md	Completed phases (one-liner each)	Append-only archive

Workflows

Workflow	When It Runs	What It Does
discuss.md	Fresh start or exploring ideas	Vision extraction, teaching, unblocking
execution.md	Plan approved, building	Execute tasks, log decisions with code snippets
checkpoint.md	Session ending	Update WORK.md for fresh agent resume
housekeeping.md	User requests cleanup	Extract PR from logs, archive to HISTORY.md
map-codebase.md	New codebase, no ARCHITECTURE.md	Generate ARCHITECTURE.md from codebase
new-project.md	New project, no PROJECT.md	Generate PROJECT.md from vision discussion

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A Real Session

Starting Fresh

You: "I want to build a CLI for managing dotfiles"

Agent: "Interesting. Before we dive in, help me understand the problem you're solving.

Are you currently:

1. Manually copying dotfiles between machines?
2. Using a tool that's not working for you?
3. Something else?

[YOUR TURN]"

You: "I'm manually copying. It's a pain when I set up a new laptop."

Agent: "Got it — so the core pain is the manual work during machine setup.

When you imagine the solution, are you thinking:

• A sync tool (like Dropbox for dotfiles)?
• A declarative config (describe desired state, tool applies it)?
• Something simpler (just a script that copies files)?

[YOUR TURN]"

You: "Declarative. I want to define what should be where and have the tool figure it out."

Agent: "That's a clear vision. Let me echo it back:

A CLI that reads a declarative config file, compares it to the current state, and applies changes to make them match. The user defines 'desired state', the tool handles the rest.

Does that capture it?

[YOUR TURN]"

You: "Yes, exactly."

Agent: "Perfect. I'll present a plan for your approval."

Mid-Session (Execution)

Agent: "I'm looking at the apply command. I found this in src/cli.py:

# Line 45-52
def apply(config_path: str):
    config = load_config(config_path)
    for file in config.files:
        shutil.copy(file.source, file.dest)

This copies files but doesn't check if they already exist. If the destination has local changes, they'd be overwritten silently.

Options:

1. Add a diff check — show changes before applying, require confirmation
2. Add a backup — copy existing file to .bak before overwriting
3. Keep as-is — trust the user knows what they're doing

Which fits your philosophy for this tool?

[YOUR TURN]"

You: "Option 1. Always show the diff."

Agent: "Agreed — explicit is better than silent. Logging this decision and implementing."

Wrapping Up

You: "Write PR for this work"

Agent: "Generating from WORK.md logs..."

## Dotfile Manager: Apply Command with Diff Check

Added safe apply logic that shows a diff before overwriting files.

### Changes
- `apply` command now diffs source vs destination
- User must confirm before changes are applied
- Added `--force` flag to skip confirmation

### Key Decisions
- **DECISION-003:** Always show diff before overwriting (rationale: explicit > silent, prevents accidental data loss)

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Installation

# Global Install (recommended)
# Agents + workflows available in ANY project
uvx gsd-lite@latest install

# Local Install
# Agents + workflows scoped to THIS project only
uvx gsd-lite@latest install --local

After installation, invoke the agent with @gsd-lite in your MCP client (OpenCode, Claude Desktop, etc.).

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For Maintainers

Core Principles (Do Not Regress)

#	Principle	Test
1	Pair programming > task execution	grep "thinking partner" workflows/ returns matches
2	Grounding Loop enforced	grep "Echo.*Verify" PROTOCOL.md returns matches
3	Universal Onboarding required	Boot sequence in PROTOCOL.md reads all 4 artifacts
4	User controls artifact writes	grep "[YOUR TURN]" workflows/ returns matches
5	Perpetual WORK.md	grep "ephemeral|delete WORK.md" templates/ returns 0

Testing Changes

1. Grep patterns: Verify grep "^## " WORK.md finds all sections
2. Coherence: grep "STATE.md" src/gsd_lite/template/ returns 0 (deleted artifact)
3. Eval framework: Run scenarios in tests/eval_gsd_lite/ with different models

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License

MIT

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Inspired by get-shit-done. Built for engineers who want to own their code.