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Planning

Community
mblode
planning

Creates and reviews executable implementation plans grounded in repository evidence, with vertical slices, explicit decisions, and verification criteria. Use when asked to "plan this feature", "stress-test this plan", "grill me", or "split this into tickets". For architecture use codebase-architecture; for code review use pr-reviewer.

Overview

Publishermblode
Repositoryagent-skills
Skill nameplanning
Stars
118
Forks
11
Bundled files
8
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.

  • 8 bundled files

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

  • Open source

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

Installation

Install the Planning 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/mblode/agent-skills.git /tmp/agent-skills
mkdir -p .claude/skills
cp -r /tmp/agent-skills/skills/planning .claude/skills/planning
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Planning 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 Planning 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 Planning 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.

Planning

Produce an executable plan, or strengthen an existing one. Planning alone produces no implementation edits. When the user asks to plan and implement, finish the plan and continue within the host's active mode and authorization.

  • Create: no plan exists; ground the approach in the repository and write the plan.
  • Review: a plan exists; verify its consequential claims and resolve concrete gaps in the file.
  • Interview: the user asks to be grilled or interviewed; explore decisions interactively.
  • Split: multiple independently deliverable outcomes need tickets with native dependency links.

For architecture contracts use codebase-architecture; for code findings use pr-reviewer.

References

FileRead when
references/interrogation-protocol.mdA consequential choice is unresolved, or the user requested an interview
references/doc-grounding.mdADRs, specifications, or library docs constrain the approach
references/handoff-plans.mdAnother session or person will execute the plan
references/plan-quality-rubric.mdReviewing completeness, feasibility, scope, testability, risk, and assumptions
references/questioning-framework.mdA review gap needs a focused user question
references/claim-verification.mdA plan claim can be checked against code or documentation
references/splitting.mdDecomposing work into executable tickets

Workflow

  1. Identify the requested outcome and authoritative plan path. Use the host's plan file where one exists. A durable handoff goes at the project path, default docs/plans/<slug>.md; name which copy is authoritative.
  2. Inspect the modules, tests, and decisions that constrain this change. Resolve questions the repository answers yourself. Ask only when an unresolved choice materially changes scope, behavior, or a hard-to-reverse action. Routine assumptions belong in the draft.
  3. Choose the smallest vertical slice that exercises the real boundary. Name the existing code or platform capability it extends. A new dependency or abstraction needs a current requirement the existing mechanism cannot satisfy.
  4. Write the plan with the contract below. Review the consequential claims once against the rubric; fix evidenced gaps directly. Interview mode can explore competing approaches, but has no minimum question count.
  5. Return the plan path, unresolved decisions, and verification limits. Use the host's approval mechanism when its mode requires it. Do not add a second approval question for the plan's own review.

Plan contract

Include only sections this change needs:

  • Outcome: triggering problem, intended behavior, and acceptance criteria.
  • Approach: chosen slice, affected files or interfaces, and migration order where applicable.
  • Decisions: evidence for consequential choices; assumptions that remain unverified.
  • Boundaries: exclusions only where an adjacent change would plausibly be mistaken for scope.
  • Verification: a command, test scenario, or observation tied to each material acceptance criterion, including expected failure behavior.
  • Recovery: rollback or recovery for migrations and irreversible writes.

A handoff is self-contained. Replace "as discussed" with the decision. Preserve user corrections in the file, not just the chat. Split tickets by shippable outcome, not database/backend/frontend layers.

Review completion

Resolve gaps supported by code, the task, or operational constraints. Do not add speculative requirements to improve a self-score. Scores are optional unless requested; when used, mark unverified claims and explain residual gaps rather than iterating until every cell says 5/5.

Repeat review only after a substantive edit or new evidence. A user decision that remains unanswered is recorded at the affected step; continue independent work. If the user says to skip questions, draft from available evidence and label the assumptions.

Gotchas

  • A plan in ~/.claude/plans/ is not available to other checkouts or CI. Durable handoffs need a project artifact.
  • A bare "run tests" step does not establish the changed behavior. Name the acceptance scenario and expected result.
  • Publishing slices without native blocker relations leaves the execution queue unaware of dependencies.
  • A plan written for a prior revision can name moved files. Verify consequential paths and interfaces against the current checkout.

Maintenance only: evals/evals.json contains regression scenarios for changes to this skill; it does not load during a user task.

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 Planning AI skill do?

Creates and reviews executable implementation plans grounded in repository evidence, with vertical slices, explicit decisions, and verification criteria. Use when asked to "plan this feature", "stress-test this plan", "grill me", or "split this into tickets". For architecture use codebase-architecture; for code review use pr-reviewer.

Why use Planning on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mblode/agent-skills/tree/main/skills/planning. 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 Planning?

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 Planning?

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

Is the Planning AI skill free?

Yes. It is published on GitHub by mblode 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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