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Executing Plans

Community
rileyhilliard
executing-plans

Executes implementation plans directly with a verification gate and conditional code review. Use when carrying out a written plan step by step; delegates to subagents only for large, genuinely independent, parallelizable work.

Overview

Publisherrileyhilliard
Repositoryclaude-essentials
Skill nameexecuting-plans
Stars
127
Forks
19
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 rileyhilliard 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.
  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/rileyhilliard/claude-essentials.git /tmp/claude-essentials
mkdir -p .claude/skills
cp -r /tmp/claude-essentials/plugins/ce/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

Default to implementing directly. Delegation is the exception, not the default. Delegate to a subagent only for large tasks that are genuinely independent and parallelizable, such as a wide multi-file investigation. Do not delegate work you can finish yourself in a handful of tool calls, and do not use subagents to verify or double-check your own work. If one subagent can complete the task, use one rather than several, and keep spawn counts low.

1. Setup

Create a worktree using EnterWorktree before starting any work. This isolates changes from the main branch and makes cleanup safe. Skip only for trivial single-file changes that don't warrant isolation.

Clarify ambiguity upfront. If the plan has unclear requirements or meaningful tradeoffs, ask before starting. Don't guess when the user can clarify in 10 seconds.

Track progress with tasks. Create tasks for each major work item from the plan. Set up dependency chains between tasks using addBlocks/addBlockedBy so blocked tasks don't start prematurely. Update task status as work progresses. This keeps execution visible to the user and persists across context compactions.

2. Execute

Work through the plan's tasks directly, in order, updating task status as you go.

If delegation is warranted (large, independent, parallelizable work identified in step 1), group related tasks by subsystem rather than spawning per-task, since each agent re-investigates the codebase.

SignalGroup together
Same directory prefixsrc/auth/* tasks
Same domain/featureAuth tasks, billing tasks
Plan sectionsTasks under same ## heading

Keep concurrent agents to a small handful. Claude Code hard-fails above 20 concurrent subagents, but that's not a target, stay well under it.

Parallel vs sequential: Groups that touch different subsystems run in parallel. Groups with dependencies run sequentially (e.g., create shared types before using them). When parallel agents may touch overlapping files, use isolation: "worktree" on the Agent call.

Recovery: Fix failures inline yourself. If the same error recurs after a second attempt, stop and ask the user rather than keep retrying.

3. Verify

Verification is a gate, not a checklist. Nothing proceeds to merge until all checks pass.

Automated tests. Run the full test suite. All tests must pass.

Manual verification. Automated tests aren't sufficient. Actually exercise the changes:

  • API changes: Curl endpoints with realistic payloads
  • External integrations: Test against real services to catch rate limiting, format drift, bot detection
  • CLI changes: Run actual commands, verify output
  • UI changes: Start the dev server and use the feature in a browser
  • Parser changes: Feed real data, not just fixtures

Watch for DX friction during manual testing: confusing error messages, noisy output, inconsistent behavior, rough edges that technically work but feel bad. Fix inline or document for follow-up. Don't ship friction.

Code review (conditional). For large or risky diffs, dispatch the ce:code-reviewer agent to review the full diff against the base branch after tests pass and manual verification is done. Skip it for small or simple changes.

Load relevant domain skills into the reviewer based on what was implemented. Evaluate which apply and include them in the agent prompt:

  • Skill(architecting-systems) - system design, module boundaries
  • Skill(managing-databases) - database work
  • Skill(handling-errors) - error handling
  • Skill(writing-tests) - test quality
  • Skill(optimizing-performance) - performance work

Handle the review verdict:

  • Must fix: Fix all Critical and Important issues
  • Suggestions: Fix these too unless there's a clear reason not to

4. Complete

Once verification passes:

  1. Commit the work with a message summarizing what was implemented
  2. Merge to main from the worktree branch
  3. Exit worktree using ExitWorktree with action: "remove" to clean up
  4. Mark plan as COMPLETED and move to ./plans/done/ if applicable

Match the length of written documents (commit messages, failure reports) to what the task needs: cover the substance, but do not pad with filler sections, redundant summaries, or boilerplate.

Frequently asked questions

What does the Executing Plans AI skill do?

Executes implementation plans directly with a verification gate and conditional code review. Use when carrying out a written plan step by step; delegates to subagents only for large, genuinely independent, parallelizable work.

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/rileyhilliard/claude-essentials/tree/main/plugins/ce/skills/executing-plans. TypingMind reads its SKILL.md 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 rileyhilliard 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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