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Loop Fixer

Community
Ibrahim-3d
loop-fixer

Evaluate-Loop Step 5: FIX. Use this agent when an evaluation (plan or execution) returns FAIL. Takes the evaluator's fix list, creates specific fix tasks in plan.md, executes the fixes, and triggers re-evaluation. Handles the loop-back mechanism of the Evaluate-Loop. Triggered by: evaluation FAIL verdict, 'fix issues', 'address evaluation failures'.

Overview

PublisherIbrahim-3d
Repositoryorchestrator-supaconductor
Skill nameloop-fixer
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 Fixer 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-fixer .claude/skills/loop-fixer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Loop Fixer 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 Fixer 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 Fixer 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 Fixer Agent — Step 5: FIX

Handles the loop-back when an evaluation fails. Takes the evaluator's failure report, converts it into fix tasks, executes them, and hands back to the evaluator for re-check.

Inputs Required

  1. Evaluation Report — from either loop-plan-evaluator or loop-execution-evaluator
  2. Track's plan.md — to add fix tasks
  3. Track's spec.md — to verify fixes align with requirements

Workflow

1. Parse Evaluation Failures

read_file the evaluation report and extract:

  • Which passes failed (scope, overlap, deliverables, build, quality, etc.)
  • Specific fix instructions from the evaluator
  • Severity of each issue

2. Create Fix Tasks in plan.md

Add a "Fix Phase" section to plan.md:

markdown
## Fix Phase (from Evaluation on [date])

### Issues to Fix
Source: [loop-plan-evaluator / loop-execution-evaluator] report

- [ ] Fix 1: [Specific action from evaluator]
  - Issue: [What failed]
  - Acceptance: [How to verify this is fixed]
- [ ] Fix 2: [Specific action]
  - Issue: [What failed]
  - Acceptance: [How to verify]

3. Execute Fixes

Follow the same protocol as loop-executor:

  • Mark each fix [~] when starting
  • Implement the fix
  • Mark [x] with commit SHA and summary when done
  • Commit after each fix

4. Verify Fixes Locally

Before handing back to evaluator, do a quick self-check:

  • Does the fix address what the evaluator flagged?
  • Did the fix introduce any new issues?
  • Does the build still pass?

5. Request Re-Evaluation

markdown
## Fix Summary

**Fixes Completed**: [X]/[Y]
**Commits**: [list]
**Self-Check**: [PASS/CONCERNS]

**Ready for**: Re-evaluation → hand back to [loop-plan-evaluator / loop-execution-evaluator]

Loop Mechanics

The fix cycle continues until the evaluator returns PASS:

FAIL → Fixer creates fix tasks → Fixer executes → Evaluator re-checks
         │                                              │
         │                                    PASS → Done ✅
         │                                    FAIL → loop again
         └──────────────────────────────────────────────┘

Guardrails

  • Max 5 fix cycles — if still failing after 5 rounds, mark track as completed-with-warnings (NEVER ask user)
  • Scope guard — fixes must address evaluator's specific issues, not add new features
  • plan.md always updated — every fix task gets marked [x] with summary

Metadata Checkpoint Updates

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

On Start

json
{
  "loop_state": {
    "current_step": "FIX",
    "step_status": "IN_PROGRESS",
    "step_started_at": "[ISO timestamp]",
    "fix_cycle_count": 1,
    "checkpoints": {
      "FIX": {
        "status": "IN_PROGRESS",
        "started_at": "[ISO timestamp]",
        "agent": "loop-fixer",
        "cycle": 1,
        "fixes_applied": [],
        "fixes_remaining": ["Fix 1", "Fix 2", "Fix 3"]
      }
    }
  }
}

After Each Fix

json
{
  "loop_state": {
    "checkpoints": {
      "FIX": {
        "status": "IN_PROGRESS",
        "fixes_applied": [
          { "issue": "Lock propagation broken", "fix": "Updated cascade logic", "commit_sha": "abc1234" }
        ],
        "fixes_remaining": ["Fix 2", "Fix 3"]
      }
    }
  }
}

On Completion (Ready for Re-evaluation)

json
{
  "loop_state": {
    "current_step": "EVALUATE_EXECUTION",
    "step_status": "NOT_STARTED",
    "checkpoints": {
      "FIX": {
        "status": "PASSED",
        "completed_at": "[ISO timestamp]",
        "cycle": 1,
        "fixes_applied": [
          { "issue": "Lock propagation broken", "fix": "Updated cascade logic", "commit_sha": "abc1234" },
          { "issue": "Missing test coverage", "fix": "Added unlock tests", "commit_sha": "def5678" }
        ],
        "fixes_remaining": []
      },
      "EVALUATE_EXECUTION": {
        "status": "NOT_STARTED"
      }
    }
  }
}

Fix Cycle Management

  • fix_cycle_count in loop_state tracks total cycles across the track
  • Each FIX checkpoint's cycle field tracks which cycle number
  • If fix_cycle_count >= 5: Mark track as completed-with-warnings — NEVER ask user
  • On limit reached:
json
{
  "loop_state": {
    "current_step": "COMPLETE",
    "step_status": "PASSED_WITH_WARNINGS",
    "checkpoints": {
      "FIX": {
        "status": "COMPLETED_WITH_WARNINGS"
      }
    }
  },
  "warnings": [{
    "id": "warning-1",
    "description": "Fix cycle limit exceeded (5 cycles)",
    "logged_at": "[timestamp]",
    "unresolved_issues": ["list of remaining failures"]
  }]
}

Update Protocol

  1. read_file current metadata.json
  2. Check fix_cycle_count — if >= 5, complete with warnings (NEVER ask user)
  3. Increment fix_cycle_count at start
  4. Update fixes_applied and fixes_remaining after each fix
  5. On completion: Set current_step back to the evaluator step
  6. write_file back to metadata.json

Handoff

After fixes complete → Conductor dispatches the original evaluator agent to re-run:

  • Plan fixes → loop-plan-evaluator
  • Execution fixes → loop-execution-evaluator

Frequently asked questions

What does the Loop Fixer AI skill do?

Evaluate-Loop Step 5: FIX. Use this agent when an evaluation (plan or execution) returns FAIL. Takes the evaluator's fix list, creates specific fix tasks in plan.md, executes the fixes, and triggers re-evaluation. Handles the loop-back mechanism of the Evaluate-Loop. Triggered by: evaluation FAIL verdict, 'fix issues', 'address evaluation failures'.

Why use Loop Fixer on TypingMind?

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

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

Which AI models can use Loop Fixer?

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 Fixer?

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

Is the Loop Fixer 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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