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Pre Plan Workflow

Community
jpicklyk
pre-plan-workflow

Internal, hook-triggered: gathers existing MCP items and schema gate requirements to set the definition floor before planning.

Overview

Publisherjpicklyk
Repositorytask-orchestrator
Skill namepre-plan-workflow
Stars
204
Forks
22
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 jpicklyk on GitHub. Read the source before you install it.

Installation

Install the Pre Plan Workflow 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/jpicklyk/task-orchestrator.git /tmp/task-orchestrator
mkdir -p .claude/skills
cp -r /tmp/task-orchestrator/claude-plugins/task-orchestrator/skills/pre-plan-workflow .claude/skills/pre-plan-workflow
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Pre Plan Workflow 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 Pre Plan Workflow 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 Pre Plan Workflow 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.

Pre-Plan Workflow — Definition Floor

When entering plan mode, use the MCP to set the definition floor before writing your plan.

The definition floor is the baseline of existing work, documentation requirements, and gate constraints that the plan must account for.

If MCP is unreachable: Proceed with planning based on conversation context. Note in the plan that MCP state could not be verified — existing work may overlap. Re-check after MCP reconnects.

Step 1: Check Existing MCP State

Resolve the project rootId first: check session context for a rootId injected by the SessionStart hook, or read .taskorchestrator/config.yaml's top-level project.rootId (a file read, not an MCP call).

Call the health check to see what's already tracked:

get_context()

When a rootId is known, pass it to scope the check to this project: get_context(ancestorId="<rootId>"). When no rootId is known, call unscoped exactly as shown — this is the same behavior as before project scoping existed.

If active or stalled items exist:

  • Identify items related to the current request — avoid planning work that duplicates what's already tracked
  • For each relevant active item, call get_context(itemId=...) to inspect:
    • Note schema — which notes are expected for this item's tags
    • Gate status — which required notes are filled vs. missing, and whether the item can advance
    • Guidance keyguidanceKey names the first unfilled required note; resolve its authoring guidance via query_items(operation="schema", itemId=...)

If no items exist (clean slate):

  • The definition floor is simply "no existing MCP state to account for"
  • Proceed with planning, but still check Step 2 for schema awareness

Step 2: Discover Note Schema Requirements

Read .taskorchestrator/config.yaml in the project root (this is a file read, not an MCP call):

  • If the file exists, list the discovered schemas and their required notes per phase
  • Schemas are defined under work_item_schemas: (preferred) or note_schemas: (legacy)
  • Each schema key (e.g., feature-implementation, bug-fix) is a type identifier — set it as the item's type field to activate gate enforcement. Tags can be used for additional categorization but are no longer the primary schema activator.
  • Required queue-phase notes define what documentation must exist before work starts
  • Required work-phase notes define what must be captured during implementation
  • Use guidanceKey from get_context(itemId=...), resolved via query_items(operation="schema", itemId=...), to understand how to author each note

If no config file exists, the project has no note schemas — items will be schema-free with no gate enforcement. Proceed with planning normally.

Minimal config example:

yaml
work_item_schemas:
  feature-task:
    notes:
      - key: task-scope
        role: queue
        required: true
      - key: implementation-notes
        role: work
        required: true

Use schemas to inform the plan: When a schema applies, each planned task should:

  • Note which schema type will be applied at materialization (e.g., type: "feature-task")
  • Account for required notes — plan sections should naturally produce content that maps to required note keys
  • Respect dependency ordering — which tasks block others (these become BLOCKS edges)

Step 3: Plan with MCP Awareness

Structure the plan knowing it will be materialized into MCP items after approval:

  • Each planned task should map to one work item with clear boundaries — a single unit of work a subagent can own
  • Account for dependency ordering — which tasks block others (these become BLOCKS edges)
  • Consider the hierarchy — a root container item with child task items is the standard pattern

Continue with Plan Mode

The prerequisite is complete. Now proceed with plan mode's normal workflow — explore the codebase, understand existing patterns, and design your implementation approach. Use the definition floor from Steps 1-3 to inform your plan.

Once the plan is approved, the post-plan hook will guide you through materialization and implementation dispatch. Do not materialize before approval.

Frequently asked questions

What does the Pre Plan Workflow AI skill do?

Internal, hook-triggered: gathers existing MCP items and schema gate requirements to set the definition floor before planning.

Why use Pre Plan Workflow on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/jpicklyk/task-orchestrator/tree/main/claude-plugins/task-orchestrator/skills/pre-plan-workflow. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Pre Plan Workflow?

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 Pre Plan Workflow?

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

Is the Pre Plan Workflow AI skill free?

Yes. It is published on GitHub by jpicklyk 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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