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Resume Plan

Organization
DasDigitaleMomentum
resume-plan

Bootstrap a new session to continue working on an existing plan. Reads plan artifacts, builds context, validates prerequisites, and prepares the agent for implementation. Use this skill at the start of a session when continuing a multi-session plan.

Overview

PublisherDasDigitaleMomentum
Repositoryopencode-processing-skills
Skill nameresume-plan
Stars
61
Forks
3
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 DasDigitaleMomentum on GitHub. Read the source before you install it.

Installation

Install the Resume Plan 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/DasDigitaleMomentum/opencode-processing-skills/tree/main/skills/resume-plan
  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/DasDigitaleMomentum/opencode-processing-skills.git /tmp/opencode-processing-skills
mkdir -p .claude/skills
cp -r /tmp/opencode-processing-skills/skills/resume-plan .claude/skills/resume-plan
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Resume Plan 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 Resume Plan 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 Resume Plan 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.

Skill: Resume Plan

What This Skill Does

Bridges the gap between planning artifacts and implementation. When a new session starts and the agent needs to continue work on an existing plan, this skill:

  1. Finds the active plan and identifies the current phase
  2. Reads all relevant artifacts in the correct order (context bootstrap)
  3. Validates that prerequisites for the current phase are met
  4. Presents a concise briefing to the agent/user: what to do, where to look, what was decided
  5. Prepares the todo list as the working entry point

Why This Skill Exists

Planning skills (create-plan, update-plan) produce artifacts. Implementation happens across sessions. Without a defined bootstrap procedure, every new session starts with ad-hoc exploration -- the agent must independently discover which plan exists, which phase is active, what was decided, and what files to read. This wastes context budget and risks missing critical decisions from prior sessions.

This skill codifies the read-path that complements the write-path of the other planning skills.

When to Use

  • At the start of a new session when continuing a multi-session plan
  • When the user says "continue with the plan", "resume phase N", or "what's next"
  • After a session handover was created in a previous session
  • When onboarding a different agent to an existing plan

Do NOT use this skill to create or modify plans -- use create-plan or update-plan instead.

Execution Model

  • Always: the primary agent runs this skill directly.
  • Rationale: session bootstrap is fundamentally a context-building activity. The primary needs the context to guide the implementation session. Delegating to a subagent would build context in the wrong place.
  • Use doc-explorer: optionally, to validate prerequisites that require codebase inspection (e.g., "Phase 1 tests must pass"). Results are written to docs/, not returned as text.
  • Do NOT delegate to doc-explorer: this skill reads artifacts, it does not write them.

Workflow

Step 1: Discover the Plan

Find active plans:

  • Check plans/ directory for existing plan directories
  • For each plan, read the frontmatter of plan.md to check status (active or draft)
  • If multiple active plans exist, use the question tool to ask the user which plan to resume
  • If no plans exist, inform the user and suggest using create-plan

Step 2: Read the Plan Hub (Ordered)

Read artifacts in this specific order -- each builds on the previous:

  1. plans/<name>/plan.md -- Understand objective, requirements, DoD, phase overview
  2. plans/<name>/todo.md -- Identify the active phase, current task state, and Phase Context links
  3. Latest handover in plans/<name>/handovers/ (most recent by filename) -- Session context, decisions, blockers, next steps from the last session
  4. Active phase document plans/<name>/phases/phase-N.md -- What/Why for the current phase, acceptance criteria
  5. Active implementation plan plans/<name>/implementation/phase-N-impl.md -- How to implement, Required Context files, implementation steps

This order is intentional:

  • Plan gives the big picture (why we're here)
  • Todo gives the current state (where we are)
  • Handover gives the session narrative (what happened last time)
  • Phase gives the scope (what we need to do)
  • Implementation plan gives the approach (how to do it)

Step 3: Read Required Context

From the implementation plan's Required Context section, read each listed file:

  • Module documentation (docs/modules/<name>.md) -- understand the code being modified
  • Feature documentation (docs/features/<name>.md) -- understand the feature being built
  • Any other files listed as required context

If Required Context is empty or the section doesn't exist, check the Affected Modules table for module doc links and read those instead.

If no project documentation exists under docs/, note this as a gap and suggest running generate-docs.

Step 4: Validate Prerequisites

Check the phase document's Prerequisites section:

  • For checklist items that can be verified from artifacts (e.g., "Phase 1 completed"): verify by reading the referenced phase's frontmatter status
  • For checklist items that require codebase inspection (e.g., "Phase 1 tests pass"): optionally delegate to doc-explorer via Task tool
  • For items that cannot be verified automatically: use the question tool to confirm with the user

If prerequisites are not met, inform the user and stop. Do not proceed with implementation on unmet prerequisites.

Step 5: Present the Briefing

Summarize what was gathered into a concise briefing for the session. This is NOT written to a file -- it is presented in the conversation to establish shared context:

Briefing structure:

  1. Plan: Name, objective (one line)
  2. Active Phase: Number, title, objective (one line)
  3. Last Session: Key decisions, blockers resolved/remaining (from handover)
  4. Current State: What's completed, what's in progress, what's pending (from todo)
  5. This Session's Focus: The next 3-5 concrete tasks from the todo list
  6. Key Files: The specific files the agent will need to modify (from implementation plan steps)

Step 6: Confirm and Begin

Use the question tool to confirm with the user:

  • Does this briefing match their intent?
  • Should the focus be adjusted?
  • Are there any new constraints or changes since the last session?

After confirmation, the session is bootstrapped. The agent can begin implementation, using update-plan to track progress as work proceeds.

Integration with Other Skills

SkillRelationship
create-planCreates the artifacts that resume-plan reads. Must run before resume-plan is useful.
update-planUsed DURING implementation (after resume-plan bootstraps context) to track progress.
generate-handoverCreates the handover that resume-plan reads at session start.
generate-docsCreates module/feature docs referenced by implementation plans' Required Context.
update-docsShould run after implementation to keep docs in sync with code changes.

Rules

  1. Read-only: This skill does NOT create or modify any files. It builds context from existing artifacts.
  2. Order matters: Read artifacts in the specified order (Step 2). Each document builds understanding that informs the next.
  3. Don't skip handovers: If a handover exists, it MUST be read. It contains decisions and context that no other artifact captures.
  4. Briefing is conversation, not a file: The briefing (Step 5) is presented in chat, not written to plans/. It is ephemeral session context.
  5. Prerequisites are gates: Do not proceed past Step 4 if prerequisites are unmet. Inform the user.
  6. Suggest missing artifacts: If docs/ doesn't exist, suggest generate-docs. If no handover exists, note it (first session on this plan -- that's fine).
  7. No built-in explore agent: Do NOT use the built-in explore subagent type.
  8. Use question tool: For plan selection, prerequisite confirmation, and briefing sign-off.

Frequently asked questions

What does the Resume Plan AI skill do?

Bootstrap a new session to continue working on an existing plan. Reads plan artifacts, builds context, validates prerequisites, and prepares the agent for implementation. Use this skill at the start of a session when continuing a multi-session plan.

Why use Resume Plan on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/DasDigitaleMomentum/opencode-processing-skills/tree/main/skills/resume-plan. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Resume Plan?

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 Resume Plan?

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

Is the Resume Plan AI skill free?

Yes. It is published on GitHub by DasDigitaleMomentum 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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