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Executing Plans

Community
ericgandrade
executing-plans

This skill should be used when the user has a written implementation plan to execute in a separate session with review checkpoints.

Overview

Publisherericgandrade
Repositoryclaude-superskills
Skill nameexecuting-plans
Stars
86
Forks
16
Bundled files
2
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.

  • 2 bundled files

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

  • Open source

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

Installation

Install the Executing Plans 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/ericgandrade/claude-superskills/tree/main/skills/executing-plans
  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/ericgandrade/claude-superskills.git /tmp/claude-superskills
mkdir -p .claude/skills
cp -r /tmp/claude-superskills/skills/executing-plans .claude/skills/executing-plans
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Executing Plans 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 Executing Plans 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 Executing Plans 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.

Executing Plans

Purpose

Execute approved implementation plans in controlled batches with checkpoints, verification, and review gates.

When to Use

Use this skill when:

  • A written implementation plan already exists
  • Work must be executed with periodic review checkpoints
  • Risk of regressions requires strict plan adherence and verification

Progress Tracking

Display plan execution progress before each batch:

[████░░░░░░░░░░░░░░░░] 20% — Phase 1: Loading & Reviewing Plan
[████████░░░░░░░░░░░░] 40% — Phase 2: Executing Batch N
[████████████░░░░░░░░] 60% — Phase 3: Reporting & Verification
[████████████████░░░░] 80% — Phase 4: Awaiting Feedback
[████████████████████] 100% — Phase 5: Final Verification Complete

For plans with many tasks, adapt the gauge to reflect actual task completion: X/Y tasks done (N%).

Workflow

  1. Load and review plan critically
  2. Execute first batch of tasks
  3. Report results and verification output
  4. Pause for feedback and continue in batches
  5. Finish branch with final verification

Overview

Load plan, review critically, execute tasks in batches, report for review between batches.

Core principle: Batch execution with checkpoints for architect review.

Announce at start: "I'm using the executing-plans skill to implement this plan."

The Process

Step 1: Load and Review Plan

  1. Read plan file
  2. Review critically - identify any questions or concerns about the plan
  3. If concerns: Raise them with your human partner before starting
  4. If no concerns: Create TodoWrite and proceed

Step 2: Execute Batch

Default: First 3 tasks

For each task:

  1. Mark as in_progress
  2. Follow each step exactly (plan has bite-sized steps)
  3. Run verifications as specified
  4. Mark as completed

Step 3: Report

When batch complete:

  • Show what was implemented
  • Show verification output
  • Say: "Ready for feedback."

Step 4: Continue

Based on feedback:

  • Apply changes if needed
  • Execute next batch
  • Repeat until complete

Step 5: Complete Development

After all tasks complete and verified:

  • Announce: "I'm using the finishing-a-development-branch skill to complete this work."
  • REQUIRED SUB-SKILL: Use superpowers:finishing-a-development-branch
  • Follow that skill to verify tests, present options, execute choice

When to Stop and Ask for Help

STOP executing immediately when:

  • Hit a blocker mid-batch (missing dependency, test fails, instruction unclear)
  • Plan has critical gaps preventing starting
  • You don't understand an instruction
  • Verification fails repeatedly

Ask for clarification rather than guessing.

When to Revisit Earlier Steps

Return to Review (Step 1) when:

  • Partner updates the plan based on your feedback
  • Fundamental approach needs rethinking

Don't force through blockers - stop and ask.

Remember

  • Review plan critically first
  • Follow plan steps exactly
  • Don't skip verifications
  • Reference skills when plan says to
  • Between batches: just report and wait
  • Stop when blocked, don't guess
  • Never start implementation on main/master branch without explicit user consent

Integration

Required workflow skills:

  • superpowers:using-git-worktrees - REQUIRED: Set up isolated workspace before starting
  • superpowers:writing-plans - Creates the plan this skill executes
  • superpowers:finishing-a-development-branch - Complete development after all tasks

Critical Rules

  • Stop immediately on blockers and ask for clarification.
  • Do not skip verification commands defined in the plan.
  • Do not continue to the next batch without reporting and waiting for feedback.

Error Handling

ErrorLikely CauseAction
Plan file not foundPath provided doesn't exist or file was movedAsk user to confirm file path; list available plan files in docs/plans/
Ambiguous task descriptionStep in plan is unclear or underspecifiedStop and ask user for clarification before proceeding
Task depends on failed prerequisiteEarlier task failed or was skippedReport the dependency failure; do not continue until resolved
Verification command failsTests or checks defined in plan are failingStop immediately, report full error output, wait for user instruction
Blocker encountered mid-executionUnexpected state, conflicting files, or missing contextPause, report the exact blocker, and ask user how to proceed
Batch completed with partial failuresSome tasks in batch failedReport which succeeded and which failed; ask user whether to retry or skip

Example Usage

  1. "Use executing-plans to implement docs/plans/2026-02-20-api-hardening.md."
  2. "Use executing-plans to run the next 3 tasks and stop for review."
  3. "Use executing-plans to finish remaining tasks and report final checks."

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 Executing Plans AI skill do?

This skill should be used when the user has a written implementation plan to execute in a separate session with review checkpoints.

Why use Executing Plans on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ericgandrade/claude-superskills/tree/main/skills/executing-plans. 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 Executing Plans?

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 Executing Plans?

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

Is the Executing Plans AI skill free?

Yes. It is published on GitHub by ericgandrade 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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