Goal Driven Execution logo

Goal Driven Execution

Organization
DevelopersGlobal
goal-driven-execution

Transforms imperative instructions into declarative goals with verifiable success criteria. Enables autonomous looping until verified completion.

Overview

PublisherDevelopersGlobal
Repositoryai-agent-skills
Skill namegoal-driven-execution
Stars
212
Forks
33
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 DevelopersGlobal on GitHub. Read the source before you install it.

Installation

Install the Goal Driven Execution 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/DevelopersGlobal/ai-agent-skills/tree/main/skills/goal-driven-execution
  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/DevelopersGlobal/ai-agent-skills.git /tmp/ai-agent-skills
mkdir -p .claude/skills
cp -r /tmp/ai-agent-skills/skills/goal-driven-execution .claude/skills/goal-driven-execution
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Goal Driven Execution 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 Goal Driven Execution 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 Goal Driven Execution 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.

Overview

Andrej Karpathy's key insight: "LLMs are exceptionally good at looping until they meet specific goals. Don't tell it what to do — give it success criteria and watch it go."

This skill converts vague imperative instructions ("make the login work") into declarative goals with concrete, testable success criteria. Agents with clear goals self-correct autonomously. Agents with vague goals produce vague results and require constant intervention.

When to Use

  • Before starting any multi-step task
  • When a task has been described imperatively ("do X, then Y, then Z")
  • When you're unsure how you'll know when you're "done"
  • For long-running or complex implementations

Process

Step 1: Extract the Underlying Goal

  1. Read the full request.
  2. Ask: What is the user trying to achieve, not just what they asked for?
  3. Write the goal as: "The task is complete when [observable, verifiable outcome]."

Example transformation:

  • ❌ Imperative: "Add error handling to the API."
  • ✅ Goal: "The task is complete when: all API endpoints return structured error responses for 4xx/5xx cases, error responses include a code, message, and requestId, and the existing tests pass."

Verify: The goal statement is observable and testable by a third party.

Step 2: Define Success Criteria

  1. List 3–7 specific, binary success criteria:
    Success when:
    - [ ] All existing tests pass
    - [ ] New behavior X is demonstrated by test Y
    - [ ] No regressions in file Z
    - [ ] Manual check: [describe what to look for]
  2. Each criterion must be falsifiable — you can clearly state when it passes or fails.

Verify: Every criterion can be checked without the original author.

Step 3: Define the Execution Plan

  1. Break the goal into ordered steps, each with its own verify check:
    1. [Step] → verify: [command or check]
    2. [Step] → verify: [command or check]
    3. [Step] → verify: [command or check]
  2. Identify the first failure mode — what's most likely to go wrong? Plan for it.

Verify: The plan is readable and each step is independently verifiable.

Step 4: Execute and Loop

  1. Follow the plan step-by-step.
  2. At each verify checkpoint — actually run the check. Do not skip.
  3. If a check fails: diagnose, fix, re-verify. Do not proceed past a failing check.
  4. When all checks pass: report completion with evidence.

Common Rationalizations (and Rebuttals)

ExcuseRebuttal
"The goal is obvious"Obvious goals still need explicit success criteria. What's obvious to you is ambiguous to an agent.
"I'll know when it's done"That's not a verifiable criterion. Write it down.
"The tests will tell me"Which tests? What do they cover? What don't they cover?
"It's too simple for a plan"Simple tasks rarely fail. Complex tasks without a plan always do.

Red Flags

  • The task is described as a to-do list, not a goal
  • You don't know how you'll verify completion
  • You're 80% through and realize the original framing was wrong
  • "It seems to work" is your verification strategy

Verification

  • Goal is stated as an observable, testable outcome
  • Success criteria are listed and binary (pass/fail)
  • Execution plan has verify steps for each phase
  • All verify checks have been run (not just assumed passing)
  • Evidence of completion is documented

References

Frequently asked questions

What does the Goal Driven Execution AI skill do?

Transforms imperative instructions into declarative goals with verifiable success criteria. Enables autonomous looping until verified completion.

Why use Goal Driven Execution on TypingMind?

Because you install it once and use it with any model. Goal Driven Execution 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 Goal Driven Execution in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/DevelopersGlobal/ai-agent-skills/tree/main/skills/goal-driven-execution. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Goal Driven Execution?

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 Goal Driven Execution?

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

Is the Goal Driven Execution AI skill free?

Yes. It is published on GitHub by DevelopersGlobal 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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