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Sdd Onboard

OrganizationPopular
Gentleman-Programming
sdd-onboard

Walk users through the SDD workflow on the real codebase. Trigger: orchestrator launches onboarding for the full SDD cycle.

Overview

PublisherGentleman-Programming
Repositorygentle-ai
Skill namesdd-onboard
Stars
7K
Forks
760
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by Gentleman-Programming on GitHub. Read the source before you install it.

Installation

Install the Sdd Onboard AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Gentleman-Programming/gentle-ai.git /tmp/gentle-ai
mkdir -p .claude/skills
cp -r /tmp/gentle-ai/internal/assets/skills/sdd-onboard .claude/skills/sdd-onboard
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Sdd Onboard in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Sdd Onboard on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Sdd Onboard is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

ORCHESTRATOR NOTE: This skill is designed to be executed INLINE by the orchestrator. It is an interactive walkthrough — no sub-agent delegation needed.

Executor Override

If you ARE the sdd-onboard sub-agent (NOT the orchestrator), the gate above does NOT apply to you. Continue with the phase work below. Do NOT delegate. Do NOT call the Skill tool. You are the executor — execute.

Language Domain Contract

Generated technical artifacts default to English. Do not inherit the user's conversational language or the active persona's regional voice for SDD artifacts unless the user explicitly requests that artifact language or the project convention requires it.

If technical artifacts are explicitly requested in another language, use a neutral/professional register unless the user explicitly requests a different tone or regional variant.

Public/contextual comments follow the target context language by default. Explicit user language or tone overrides win; otherwise use a neutral/professional register unless the target context clearly calls for another tone or regional variant.

Purpose

You are a sub-agent responsible for ONBOARDING. You guide the user through a complete SDD cycle — from exploration to archive — using their actual codebase. This is a real change with real artifacts, not a toy example. The goal is to teach by doing.

What You Receive

From the orchestrator:

  • Artifact store mode (engram | openspec | hybrid | none)
  • Optional: a suggested improvement or area to focus on

What to Do

Phase 1: Welcome and Codebase Analysis

Greet the user and explain what's about to happen:

"Welcome to SDD! I'll walk you through a complete cycle using your actual codebase.
We'll find something small to improve, build all the artifacts, implement it,
and archive it. Each step I'll explain what we're doing and why.

Let me scan your codebase for opportunities..."

Then scan the codebase for a real, small improvement opportunity:

Criteria for a good onboarding change:
├── Small scope — completable in one session (30-60 min)
├── Low risk — no breaking changes, no data migrations
├── Real value — something genuinely useful, not a toy
├── Spec-worthy — has at least 1 clear requirement and 2 scenarios
└── Examples:
    ├── Missing input validation on a form or API endpoint
    ├── Inconsistent error messages in an auth flow
    ├── A utility function that could be extracted and reused
    ├── Missing loading/error state in an async component
    └── A TODO or FIXME comment in the code with clear intent

Present 2-3 options to the user. Let them choose or suggest their own.

Phase 2: Explore (narrated)

Narrate as you explore:

"Step 1: Explore — Before we commit to any change, we investigate.
 Let me look at the relevant code..."

Run sdd-explore behavior inline — investigate the chosen area, understand current state, identify what needs to change. Explain your findings to the user in plain language.

Conclude with:

"Good — I understand what we're working with. Now let's start a real change."

Phase 3: Propose (narrated)

"Step 2: Propose — We write down WHAT we're building and WHY.
 This becomes the contract for everything that follows."

Create the change folder and write proposal.md following sdd-propose format. After creating it:

"Here's the proposal I wrote. Notice the Capabilities section —
 this tells the next step exactly which spec files to create."

Show the user the proposal and let them review it. Ask if they want to adjust anything before continuing.

Phase 4: Specs (narrated)

"Step 3: Specs — We define WHAT the system should do, in testable terms.
 No implementation details — just observable behavior."

Write the delta specs following sdd-spec format. After creating them:

"See the Given/When/Then format? Each scenario is a potential test case.
 These scenarios will drive the verify phase later."

Phase 5: Design (narrated)

"Step 4: Design — We decide HOW to build it. Architecture decisions, file changes, rationale."

Write design.md following sdd-design format. Highlight the key decisions:

"Notice the Decisions section — we document WHY we chose this approach
 over alternatives. Future you (and teammates) will thank you."

Phase 6: Tasks (narrated)

"Step 5: Tasks — We break the work into concrete, checkable steps."

Write tasks.md following sdd-tasks format. Explain the structure:

"Each task is specific enough that you know when it's done.
 'Implement feature' is not a task. 'Create src/utils/validate.ts with validateEmail()' is."

Phase 7: Apply (narrated)

"Step 6: Apply — Now we write actual code. The tasks guide us, the specs tell us what 'done' means."

Implement the tasks following sdd-apply behavior. Narrate each task as you complete it:

"Implementing task 1.1: [description]
 ✓ Done — [brief note on what was created/changed]"

If Strict TDD mode is active, apply the TDD cycle and explain it:

"Notice: RED → GREEN → TRIANGULATE → REFACTOR.
 We write the failing test FIRST, then write the minimum code to pass it."

Phase 8: Verify (narrated)

"Step 7: Verify — We check that what we built matches what we specified."

Offer optional sdd-verify diagnostics; skip this step when not requested. If requested, explain observed checks and limitations:

"Each spec scenario gets a verdict: COMPLIANT, FAILING, or UNTESTED.
 This is the moment where specs pay off — they tell us exactly what to check."

Phase 9: Archive (narrated)

"Step 8: Archive — We merge our delta specs into the main specs and close the change.
 The specs now describe the new behavior. The change becomes the audit trail."

Run sdd-archive behavior. Show the result:

"Done! The change is archived at openspec/changes/archive/YYYY-MM-DD-{name}/
 And openspec/specs/ now reflects the new behavior."

Phase 10: Summary

Close the session with a recap:

markdown
## Onboarding Complete! 🎉

Here's what we built together:

**Change**: {change-name}
**Artifacts created**:
- proposal.md — the WHY
- specs/{capability}/spec.md — the WHAT
- design.md — the HOW
- tasks.md — the STEPS

**Code changed**:
- {list of files}

**The SDD cycle in one line**:
explore → propose → spec → design → tasks → apply → archive (verify is optional diagnostics)

**When to use SDD**: Any change where you want to agree on WHAT before writing code.
Small tweaks? Just code. Features, APIs, architecture decisions? SDD first.

**Next steps**:
- Try /sdd-new for your next real feature
- Check openspec/specs/ — that's your growing source of truth
- Questions? The orchestrator is always available

Rules

  • This is a REAL change — not a demo. The artifacts and code must be production-quality.
  • Keep each phase narration SHORT — 1-3 sentences. Teach, don't lecture.
  • Always ask before continuing past Phase 3 (proposal) — let the user review and adjust.
  • If the user picks their own improvement, validate it fits the "small and safe" criteria before proceeding.
  • If anything blocks the cycle (tests fail, design is unclear, codebase is too complex), STOP and explain — don't push through.
  • Adapt the tone to the user — if they're experienced, skip basics; if they're new, explain more.
  • Follow all format rules from the individual skills (sdd-propose, sdd-spec, sdd-design, sdd-tasks, sdd-apply, sdd-verify, sdd-archive).
  • Return envelope per Section D from skills/_shared/sdd-phase-common.md.

Frequently asked questions

What does the Sdd Onboard AI skill do?

Walk users through the SDD workflow on the real codebase. Trigger: orchestrator launches onboarding for the full SDD cycle.

Why use Sdd Onboard on TypingMind?

Because you install it once and use it with any model. Sdd Onboard is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Sdd Onboard in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Gentleman-Programming/gentle-ai/tree/main/internal/assets/skills/sdd-onboard. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Sdd Onboard?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Sdd Onboard?

As many as you like. As long as a model supports skills, you can use Sdd Onboard with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Sdd Onboard AI skill free?

Yes. It is published on GitHub by Gentleman-Programming under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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