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Create Plan

Organization
DasDigitaleMomentum
create-plan

Create a persistent structured plan (plan.md, phases/, todo.md) when work needs multi-phase or multi-session coordination, durable tracking, or an explicitly requested plan.

Overview

PublisherDasDigitaleMomentum
Repositoryopencode-processing-skills
Skill namecreate-plan
Stars
61
Forks
3
Bundled files
3
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.

  • 3 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by DasDigitaleMomentum on GitHub. Read the source before you install it.

Installation

Install the Create 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/create-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/create-plan .claude/skills/create-plan
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Create 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 Create 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 Create 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: Create Plan

What This Skill Does

Creates a complete plan with the artifacts required to execute work in phases. This documentation shall serve Agents and Humans when working in consecutive sessions with the project:

  1. Plan (plans/<name>/plan.md) - Problem/context, target outcome, guiding decisions, requirements, DoD, phases overview
  2. Phases (plans/<name>/phases/phase-N.md) - Scope definition per phase (what/why)
  3. Todo List (plans/<name>/todo.md) - Trackable items with status

This skill intentionally does not author per-phase implementation plans (plans/<name>/implementation/phase-N-impl.md).

Default next step (second pass): use author-and-verify-implementation-plan to author/verify the per-phase implementation plan against current repo reality.

When to Use

  • When work is multi-phase or too large for a single session.
  • When the user explicitly requests a persistent plan.
  • When durable coordination or tracking is needed across agents, contributors, or sessions.

Do NOT use for a single bounded work package that can be gated with an inline brief containing its task, DoD, constraints, and final verification. Significance or non-triviality alone does not require plans/ persistence.

Execution Model (Recommended)

  • Preferred: the primary agent runs this skill and writes artifacts under plans/<name>/.
  • Rationale: plans are conversation-anchored (requirements, trade-offs, sequencing, DoD). Moving authorship to a subagent risks losing intent and introducing gaps.
  • Read existing documentation first. Use doc-explorer only when bounded codebase impact/symbol analysis is necessary; do not create documentation merely to complete a plan template.
  • Optional (edge cases): use doc-explorer only to materialize/format a large set of plan files after the primary has finalized content and structure.

Routing Matrix (Who does what)

  • Writes: plans/<name>/plan.md, plans/<name>/phases/**, plans/<name>/todo.md, and creates directories under plans/<name>/.
  • Does NOT write: plans/<name>/implementation/** (use author-and-verify-implementation-plan).
  • Primary: owns requirements, scope, DoD, phase breakdown, gating.
  • doc-explorer: optional for repo-anchored analysis persisted into docs/** and/or mechanical plan materialization.
  • implementer: never used for plan authoring.

Workflow

Step 1: Understand the Objective

Gather requirements from the user using the question tool:

  • What is the goal? (feature, bugfix, refactoring, migration)
  • What problem or current state motivates the change, and what target outcome should replace it?
  • Which user-approved decisions and constraints bind the solution?
  • What are the functional requirements?
  • Which non-functional requirements are explicit, or required by a concrete existing invariant or demonstrated risk?
  • What is explicitly out of scope?
  • What defines "done"? (Definition of Done)
  • What testing strategy is expected?
  • Are there assumptions that materially bound scope or acceptance?

If the user provided a detailed brief, extract these from the brief and confirm with the question tool.

Scope and Specification Boundary

Plan the smallest complete solution: the confirmed outcome must work, affected real paths must integrate, and applicable existing invariants must be preserved. Do not invent product rules, guardrails, configurability, abstractions, or future-proofing. Ask the user only for a genuine user-owned fork that changes observable behavior, scope/DoD, policy, or acceptance; resolve codebase-answerable questions through documentation and targeted analysis. Runtime values that operators may change belong in the project's existing configuration pattern.

The full boundary (gold-plating definition, configurable-value rule, and blocking-decision handling) is owned by the execute-work-package skill; this summary is not a separate policy.

Step 2: Analyze the Codebase (if applicable)

If the plan involves changes to existing code:

  • Read existing project documentation (docs/overview.md, module docs) if available
  • Use the Task tool with doc-explorer only for decision-relevant gaps that the existing docs and targeted reads cannot resolve
  • Identify only dependencies and risks that can change scope, sequencing, acceptance, or feasibility
  • Carry decision-relevant context into the plan, but reference detailed architecture documentation instead of reproducing it.

Step 3: Design the Phase Structure

Design the smallest complete plan: use the fewest phases needed for the confirmed outcome and stable execution boundaries while covering all necessary real paths and existing invariants. Omit phases that are useful only for hypothetical future requirements or can be merged without losing a required outcome.

Single-phase plans (work that otherwise warrants persistence):

  • One phase covering the entire scope
  • Create only the plan, phase, and todo artifacts currently needed
  • Use this structure only because the work needs persistent coordination or tracking, or the user requested it; a single-phase shape alone is not a reason to create a plan.

Multi-phase plans (complex features):

  • Each phase must be completable in a single session
  • Phases should have clear boundaries - no phase should depend on "half-done" work from another phase
  • Each phase should produce a testable, committable result
  • Order phases by actual dependency while preferring the smallest end-to-end outcome. A separate foundation phase is valid only when its result is immediately required and cannot be delivered directly in the consuming phase.

Guidelines for phase sizing:

  • A phase should represent roughly one focused work session
  • Split only when a real dependency boundary or demonstrated single-session capacity limit requires it; file count alone is not sufficient
  • Each phase should end with passing tests and a clean commit

Perform one compact deletion pass before presenting the structure: identify anything that can be removed or merged without violating the confirmed outcome, acceptance criteria, or existing invariants. Apply those reductions once; do not create an internal review loop.

Step 4: Confirm Scope and Structure

Before writing artifacts, present:

  • The confirmed outcome and scope boundaries
  • The proposed phase count and one-line necessity of each phase
  • Any unresolved decision that blocks a phase

Use the question tool to confirm the smallest complete scope and phase structure or gather adjustments. Do not create plan artifacts before this gate.

Step 5: Create the Plan Document

Create plans/<name>/plan.md:

  • Concise problem/current-state context and target outcome
  • Binding guiding decisions and constraints
  • Assumptions only when they materially bound scope or acceptance
  • Functional requirements and only applicable non-functional requirements
  • Scope (in/out)
  • Definition of Done
  • Testing strategy
  • Phases table with titles and brief descriptions
  • Decision-relevant risks and open questions, or omit the optional section
  • Initialize the changelog

Step 6: Create Phase Documents

For each phase, create plans/<name>/phases/phase-N.md:

  • Phase objective and contribution to the plan goal
  • Scope: what this phase includes and explicitly excludes
  • Prerequisites (what must be true before starting)
  • Deliverables (concrete outputs)
  • Acceptance criteria (how to verify the phase is done)
  • Dependencies on other phases

Step 7: Create the Todo List

Create plans/<name>/todo.md:

  • Populate with items from Phase 1 (the starting phase)
  • All items start as "Pending"
  • Fill in the Phase Context section with links to the phase doc and relevant module docs
  • Add the implementation-plan link only after author-and-verify-implementation-plan creates that artifact
  • Initialize the changelog with the plan creation entry

Create implementation/ when the first implementation plan is authored and handovers/ only when a handover is requested. Empty scaffolding is not a deliverable.

Rules

  1. File-based interface: All artifacts go into plans/<name>/ directory structure. The directory name should be lowercase, hyphenated, descriptive.
  2. Phase independence: Each phase must end in a stable state. No phase should leave the codebase broken.
  3. Phase describes scope: Keep phase docs focused on what/why and acceptance criteria. The per-phase implementation approach (how) is authored later via author-and-verify-implementation-plan.
  4. Reference, don't duplicate: Implementation plans reference module docs and phase docs. Don't repeat requirements from the plan in each phase.
  5. Smallest complete phase set: Use the fewest phases that preserve a stable result, cover necessary real paths and invariants, and fit demonstrated execution boundaries. Uncertainty alone is not a reason to split.
  6. No built-in explore agent: Do NOT use the built-in explore subagent type in this framework.
  7. Use doc-explorer for codebase analysis: Delegate deep symbol/dependency analysis via the Task tool. Results are written to docs/, not returned as text.
  8. Two-pass default for implementation plans: Implementation plans are authored/verified separately via author-and-verify-implementation-plan before executing a phase.
  9. Always ask for confirmation before writing: Validate requirements, the smallest complete phase structure, and scope with the user before creating artifacts.
  10. Initialize changelog: The plan's changelog should document its creation with the current date.
  11. Create artifacts on demand: Create only directories and files needed by the current workflow. Do not scaffold empty implementation or handover directories.
  12. Preserve framing without duplication: Include the problem/current state, target outcome, and binding guiding decisions needed to interpret scope. Reference detailed architecture docs rather than turning the plan into an architecture inventory.
  13. Material assumptions only: Record an assumption only when it constrains scope, acceptance, or a later decision.
  14. No speculative completeness: Optional template categories may be omitted or marked N/A with a short reason. Never invent NFRs, risks, deliverables, infrastructure, or future-proofing to fill a heading.
  15. Present necessity: Keep foundation, migration, cleanup, or preparatory phases only when the confirmed outcome needs them now.

Templates

This skill includes normative templates as bundled files. Only read the templates when processing them. Output MUST preserve template frontmatter and required headings; sections marked optional may be omitted rather than filled speculatively:

  • tpl-plan.md - Structure for the plan document
  • tpl-phase.md - Structure for phase documents
  • tpl-todo.md - Structure for the todo list

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Create Plan AI skill do?

Create a persistent structured plan (plan.md, phases/, todo.md) when work needs multi-phase or multi-session coordination, durable tracking, or an explicitly requested plan.

Why use Create Plan on TypingMind?

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

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

Which AI models can use Create 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 Create Plan?

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

Is the Create 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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