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Gdd Resume

Community
hegemonart
gdd-resume

Restore session context from a numbered checkpoint. Lists available checkpoints when no argument given.

Overview

Publisherhegemonart
Repositoryget-design-done
Skill namegdd-resume
Stars
3
Forks
1
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 hegemonart on GitHub. Read the source before you install it.

Installation

Install the Gdd Resume 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.
    https://github.com/hegemonart/get-design-done/tree/main/scripts/skill-templates/resume
  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/hegemonart/get-design-done.git /tmp/get-design-done
mkdir -p .claude/skills
cp -r /tmp/get-design-done/scripts/skill-templates/resume .claude/skills/gdd-resume
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Gdd Resume 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 Gdd Resume 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 Gdd Resume 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.

@reference/retrieval-contract.md @reference/cycle-handoff-preamble.md

{{command_prefix}}resume

Inverse of {{command_prefix}}pause. Reads a checkpoint file, prints a clear "you were here" summary, and routes to the next command.

Step 0 - Prime cycle context

Two paths - MCP preferred when available, file-read fallback otherwise. This runs BEFORE checkpoint restoration so the "you were here" summary has full cycle context (phase, plans, decisions).

MCP path (preferred)

When mcp__gdd_status is exposed (Phase 27.7+, registered via npx @hegemonart/get-design-done --register-mcp):

  1. Call mcp__gdd_status (no args) → {phase, branch, last_decisions, last_completed_plans, blocker_count}. One call replaces reading STATE.md + parsing frontmatter + extracting decisions.
  2. Call mcp__gdd_cycle_recap (no args) → diff vs last cycle snapshot. Critical for session-restoration context: what changed since you paused?
  3. Call mcp__gdd_decisions_list (no args) → full D-XX list with rationale. Use for the "decisions you made" line in the resume summary.
  4. (Optional) Call mcp__gdd_plans_list (no args) → current phase plans + status, to identify next incomplete plan.

Three to four MCP calls = full resume priming (~5s, ~32k tokens - Storybloq benchmark). Proceed to Step 1.

File-read path (fallback)

When MCP tools are not available:

  1. Read .design/STATE.md and parse the frontmatter + <position>, <decisions>, <plans> sections. Extract cycle, branch, last N decisions, completed plans.
  2. Also Read .design/CYCLES.md (if present) to see prior cycle state for the recap.
  3. Proceed to Step 1.

This path loads the same context in 3–5 file reads (~60s, ~46.5k tokens - file-reading baseline).

Steps

  1. Parse argument:

    • If argument is a number N → restore checkpoint N.
    • If no argument → list available checkpoints and ask which to restore (see step 2).
  2. List mode (no argument):

    bash
    ls .design/checkpoints/ 2>/dev/null | sort -n

    If empty, fall back to reading .design/HANDOFF.md (legacy single-slot format). If checkpoints exist, present the list and ask (AskUserQuestion): "Which checkpoint would you like to restore? (enter number, or press Enter for the latest)" Use the answer (or latest if Enter pressed) as N.

  3. Read checkpoint: load .design/checkpoints/NN-*.md. If not found, try .design/HANDOFF.md as legacy fallback.

  4. Check paused status via MCP: call mcp__gdd_state__get and inspect status. If it does not start with paused:, print "No pause to resume" and exit - the prior session was not paused via {{command_prefix}}pause, so there is nothing to restore.

  5. Restore prior status via MCP: parse the prior status from the paused:<prior> prefix. Call mcp__gdd_state__set_status with status: <prior> to restore the pre-pause state.

  6. Clear the pause blocker: optionally call mcp__gdd_state__resolve_blocker to clear the pause-related blocker (match by text containing "paused"). Skip if no such blocker exists.

  7. Stamp last_checkpoint via MCP: call mcp__gdd_state__checkpoint.

  8. Print summary in this exact shape:

    Checkpoint NN restored.
    Saved: <timestamp>
    You were: <in-progress description>
    Next step: <next>
    Active sketch: <path or none>
    Open todos: <N>
  9. Staleness check: compare mtime of .design/ artifacts vs src/ via Bash stat when available. If src/ has commits newer than the checkpoint timestamp, warn: "Source has changed since checkpoint NN - consider re-running explore or verify."

  10. Route recommendation based on checkpoint Stage: field:

    • brief → "Run {{command_prefix}}brief"
    • explore → "Run {{command_prefix}}explore"
    • plan → "Run {{command_prefix}}plan"
    • design → "Run {{command_prefix}}design to continue"
    • verify → "Run {{command_prefix}}verify"

Do Not

  • Do not delete checkpoint files.
  • Do not mutate STATE.md directly - all STATE.md writes go through the gdd-state MCP tools above.
  • Do not auto-execute the next command - just recommend.
  • Do not call mcp__gdd_state__transition_stage - resume restores prior status without moving the pipeline.

RESUME COMPLETE

Frequently asked questions

What does the Gdd Resume AI skill do?

Restore session context from a numbered checkpoint. Lists available checkpoints when no argument given.

Why use Gdd Resume on TypingMind?

Because you install it once and use it with any model. Gdd Resume 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 Gdd Resume in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/hegemonart/get-design-done/tree/main/scripts/skill-templates/resume. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Gdd Resume?

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 Gdd Resume?

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

Is the Gdd Resume AI skill free?

Yes. It is published on GitHub by hegemonart 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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