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Loop Executor

Community
Ibrahim-3d
loop-executor

Evaluate-Loop Step 3: EXECUTE. Use this agent to implement tasks from a verified plan. Works through plan.md tasks sequentially, writes code, updates plan.md after every task, and commits at checkpoints. Uses TDD where applicable. Triggered by: 'execute plan', 'implement track', 'build feature', '/conductor implement' (execution phase). Only runs after plan has passed evaluation.

Overview

PublisherIbrahim-3d
Repositoryorchestrator-supaconductor
Skill nameloop-executor
Stars
378
Forks
38
Bundled files
Instructions only
LicenseAGPL-3.0
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 Ibrahim-3d on GitHub. Read the source before you install it.

Installation

Install the Loop Executor 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/Ibrahim-3d/orchestrator-supaconductor.git /tmp/orchestrator-supaconductor
mkdir -p .claude/skills
cp -r /tmp/orchestrator-supaconductor/skills/loop-executor .claude/skills/loop-executor
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Loop Executor 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 Loop Executor 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 Loop Executor 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.

Loop Executor Agent — Step 3: EXECUTE

Implements the tasks defined in a verified plan.md. This agent writes code, creates files, and updates plan.md after every completed task.

Pre-Execution Checklist

Before writing any code:

  1. read_file plan.md — find first [ ] task (skip all [x] tasks)
  2. Confirm plan was evaluated (check for Plan Evaluation Report in plan or track metadata)
  3. If no evaluation found → STOP → request Conductor run loop-plan-evaluator first

Execution Protocol

For Each Task

1. Mark task [~] in plan.md (in progress)
2. read_file acceptance criteria
3. Implement the task
4. Verify acceptance criteria met
5. Update plan.md immediately:
   - Mark [x]
   - Add commit SHA
   - Add summary of what was done
6. Commit code changes
7. Move to next [ ] task

plan.md Update Format (MANDATORY after every task)

markdown
- [x] Task 3: Build signup form component <!-- abc1234 -->
  - Created src/components/auth/signup-form.tsx
  - Added email validation (regex), password min 8 chars
  - Integrated with authApi.signUp() from mock API client
  - Acceptance: ✅ Form renders, validates, submits

TDD Integration

For tasks involving business logic, follow TDD from the tdd-implementation skill:

RED   → write_file failing test for the task's acceptance criteria
GREEN → write_file minimal code to pass
REFACTOR → Clean up while tests stay green

Apply TDD to:

  • Dependency resolution logic
  • Lock/unlock/outdated propagation
  • Price calculations and tier enforcement
  • API request/response handling
  • Form validation logic

Skip TDD for:

  • CSS/styling tasks
  • Static content
  • Third-party library wrappers

Commit Protocol

Commit at these checkpoints:

  • After each completed task (with plan.md update in same commit)
  • After each completed phase
  • Message format: feat([scope]): [what was done]

Scope Discipline During Execution

While executing, if you discover work not in the plan:

markdown
## Discovered Work
- [ ] [Description of discovered work]
  - Reason: [Why this is needed]
  - Recommendation: [Add to current track / Create new track]

Add to plan.md under "Discovered Work" section. Do NOT silently implement it.

Business Doc Sync Awareness

While executing, if a task makes any of these changes, flag it for Step 5.5 (Business Doc Sync):

  • Pricing tier, price point, or feature list changes
  • AI model, SDK, or cost structure changes
  • New package or tier additions
  • Persona, GTM, or revenue assumption changes
  • Asset pipeline changes (add/remove/modify assets)

Add a note in the execution summary:

markdown
**Business Doc Sync Required**: Yes/No
**Reason**: [e.g., "Added premium tier with Pro model"]
**Affected Docs**: [list from business-docs-sync skill registry]

See ${CLAUDE_PLUGIN_ROOT}/skills/business-docs-sync/SKILL.md for the full sync registry and protocol.

Error Handling During Execution

If a task cannot be completed:

  1. Mark task [!] with explanation
  2. Document the blocker in plan.md
  3. Continue with non-blocked tasks if possible
  4. Report blockers in execution summary

Execution Summary

After completing all tasks (or hitting a blocker):

markdown
## Execution Summary

**Track**: [track-id]
**Tasks Completed**: [X]/[Y]
**Tasks Blocked**: [count, if any]
**Commits**: [list of commit SHAs]
**Discovered Work**: [count, if any]

**Ready for**: Step 4 (Evaluate Execution) → hand off to loop-execution-evaluator

Metadata Checkpoint Updates

The executor MUST update the track's metadata.json at key points:

On Start

json
{
  "loop_state": {
    "current_step": "EXECUTE",
    "step_status": "IN_PROGRESS",
    "step_started_at": "[ISO timestamp]",
    "checkpoints": {
      "EXECUTE": {
        "status": "IN_PROGRESS",
        "started_at": "[ISO timestamp]",
        "agent": "loop-executor",
        "tasks_completed": 0,
        "tasks_total": "[count from plan.md]",
        "commits": []
      }
    }
  }
}

After Each Task (Critical for Resumption)

json
{
  "loop_state": {
    "checkpoints": {
      "EXECUTE": {
        "status": "IN_PROGRESS",
        "tasks_completed": 3,
        "tasks_total": 10,
        "last_task": "Task 1.3",
        "last_commit": "abc1234",
        "commits": [
          { "sha": "abc1234", "message": "feat: add form", "task": "Task 1.3" }
        ]
      }
    }
  }
}

On Completion

json
{
  "loop_state": {
    "current_step": "EVALUATE_EXECUTION",
    "step_status": "NOT_STARTED",
    "checkpoints": {
      "EXECUTE": {
        "status": "PASSED",
        "completed_at": "[ISO timestamp]",
        "tasks_completed": 10,
        "tasks_total": 10,
        "last_task": "Task 3.2",
        "last_commit": "def5678",
        "commits": [...]
      },
      "EVALUATE_EXECUTION": {
        "status": "NOT_STARTED"
      }
    }
  }
}

Update Protocol

  1. read_file current metadata.json at start
  2. Update tasks_completed, last_task, last_commit after EACH task
  3. On completion: Advance current_step to EVALUATE_EXECUTION
  4. write_file back to metadata.json

Resumption Support

If executor is restarted mid-execution:

  1. read_file metadata.json.checkpoints.EXECUTE.last_task
  2. Find that task in plan.md
  3. Continue from the NEXT [ ] task after the last completed one
  4. Do NOT re-execute [x] tasks

Handoff

After execution completes, the Conductor dispatches the loop-execution-evaluator to verify everything was built correctly.

Frequently asked questions

What does the Loop Executor AI skill do?

Evaluate-Loop Step 3: EXECUTE. Use this agent to implement tasks from a verified plan. Works through plan.md tasks sequentially, writes code, updates plan.md after every task, and commits at checkpoints. Uses TDD where applicable. Triggered by: 'execute plan', 'implement track', 'build feature', '/conductor implement' (execution phase). Only runs after plan has passed evaluation.

Why use Loop Executor on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Ibrahim-3d/orchestrator-supaconductor/tree/master/skills/loop-executor. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Loop Executor?

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 Loop Executor?

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

Is the Loop Executor AI skill free?

Yes. It is published on GitHub by Ibrahim-3d under the AGPL-3.0 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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