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Loop Execution Evaluator

Community
Ibrahim-3d
loop-execution-evaluator

Evaluate-Loop Step 4: EVALUATE EXECUTION. This is the dispatcher agent — it determines the track type and invokes the correct specialized evaluator. Does NOT run a generic checklist. Instead dispatches to: eval-ui-ux (screens/design), eval-code-quality (features/infrastructure), eval-integration (APIs/auth/payments), eval-business-logic (generator/rules/state). Triggered by: 'evaluate execution', 'review implementation', 'check build', '/phase-review'. Always runs after loop-executor.

Overview

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

Use it in TypingMind

Enable Loop Execution Evaluator 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 Execution Evaluator 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 Execution Evaluator 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 Execution Evaluator — Step 4: Dispatcher

This agent does NOT evaluate directly. It determines the track type and dispatches the correct specialized evaluator.

Why Specialized Evaluators?

Different track types need fundamentally different checks:

  • A UI track needs design system adherence, visual consistency, responsive checks
  • A feature track needs build integrity, type safety, code patterns
  • An integration track needs API contracts, auth flows, error recovery
  • A business logic track needs product rules, edge cases, state transitions

A generic checklist misses critical issues specific to each type.

Dispatch Logic

read_file the track's metadata.json and spec.md to determine the track type, then dispatch:

Track TypeKeywords in spec/metadataEvaluator
UI / Design"screen", "component", "design system", "layout", "visual", "UI shell"eval-ui-ux
Feature / Code"implement", "feature", "refactor", "infrastructure", "hook", "store"eval-code-quality
Integration"Supabase", "Stripe", "Gemini", "API", "auth", "database", "webhook"eval-integration
Business Logic"generation", "lock", "dependency", "pricing", "tier", "pipeline", "download"eval-business-logic

Multi-Type Tracks

Some tracks need multiple evaluators. For example:

  • A generator logic track → eval-business-logic + eval-code-quality
  • An auth/DB integration track → eval-integration + eval-code-quality
  • A UI shell track → eval-ui-ux only

When multiple evaluators apply, run them all. The track passes only if ALL evaluators pass.

Dispatch Workflow

1. read_file track metadata.json + spec.md
2. Determine track type(s)
3. Dispatch evaluator(s):
   → eval-ui-ux         (if UI track)
   → eval-code-quality   (if code/feature track)
   → eval-integration    (if integration track)
   → eval-business-logic (if logic track)
4. Collect results from all dispatched evaluators
5. Aggregate into final verdict

Structural Checks (Always Run)

Regardless of track type, always verify these baseline checks:

CheckMethod
plan.md updatedAll completed tasks marked [x] with commit SHA and summary
Scope alignmentNo unplanned work added without documentation
No skipped tasksAll [ ] tasks either completed or documented as intentionally deferred
Build passesnpm run build exits 0
Business docs in syncIf track made pricing/model/business decisions, verify docs are flagged for Step 5.5 sync

Business Doc Sync Check

If the track made any business-impacting changes, verify:

  1. The executor's summary includes Business Doc Sync Required: Yes
  2. Affected documents are listed
  3. This flags the Conductor to run Step 5.5 (Business Doc Sync) before marking complete

What counts as business-impacting:

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

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

Aggregated Verdict

markdown
## Execution Evaluation Report

**Track**: [track-id]
**Evaluator**: loop-execution-evaluator (dispatcher)
**Date**: [YYYY-MM-DD]

### Evaluators Dispatched
| Evaluator | Reason | Verdict |
|-----------|--------|---------|
| eval-ui-ux | Track builds P0 screens | PASS ✅ / FAIL ❌ |
| eval-code-quality | Track implements features | PASS ✅ / FAIL ❌ |

### Structural Checks
- plan.md updated: YES / NO
- Scope alignment: YES / NO
- Build passes: YES / NO
- Business doc sync needed: YES / NO (if YES, list affected docs)

### Final Verdict: PASS ✅ / FAIL ❌
All evaluators must PASS for the track to pass.

[If FAIL, aggregate all fix actions from all evaluators]

Metadata Checkpoint Updates

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

On Start

json
{
  "loop_state": {
    "current_step": "EVALUATE_EXECUTION",
    "step_status": "IN_PROGRESS",
    "step_started_at": "[ISO timestamp]",
    "checkpoints": {
      "EVALUATE_EXECUTION": {
        "status": "IN_PROGRESS",
        "started_at": "[ISO timestamp]",
        "agent": "loop-execution-evaluator"
      }
    }
  }
}

On PASS

json
{
  "loop_state": {
    "current_step": "BUSINESS_SYNC",
    "step_status": "NOT_STARTED",
    "checkpoints": {
      "EVALUATE_EXECUTION": {
        "status": "PASSED",
        "completed_at": "[ISO timestamp]",
        "verdict": "PASS",
        "evaluators_run": [
          { "evaluator": "eval-code-quality", "verdict": "PASS", "issues": [] },
          { "evaluator": "eval-business-logic", "verdict": "PASS", "issues": [] }
        ],
        "business_sync_required": true
      },
      "BUSINESS_SYNC": {
        "status": "NOT_STARTED",
        "required": true
      }
    }
  }
}

On FAIL

json
{
  "loop_state": {
    "current_step": "FIX",
    "step_status": "NOT_STARTED",
    "checkpoints": {
      "EVALUATE_EXECUTION": {
        "status": "FAILED",
        "completed_at": "[ISO timestamp]",
        "verdict": "FAIL",
        "evaluators_run": [
          { "evaluator": "eval-code-quality", "verdict": "PASS", "issues": [] },
          { "evaluator": "eval-business-logic", "verdict": "FAIL", "issues": ["Business rule violation found"] }
        ],
        "failure_items": [
          "Fix business rule enforcement in resolver",
          "Add test coverage for edge case"
        ]
      },
      "FIX": {
        "status": "NOT_STARTED",
        "cycle": 1
      }
    }
  }
}

Update Protocol

  1. read_file current metadata.json
  2. Update loop_state.checkpoints.EVALUATE_EXECUTION with results
  3. If PASS + business sync needed: Set current_step to BUSINESS_SYNC
  4. If PASS + no sync needed: Set current_step to COMPLETE
  5. If FAIL: Set current_step to FIX, increment fix_cycle_count in loop_state
  6. write_file back to metadata.json

Handoff

  • ALL PASS + No Business Doc Sync → Conductor marks track complete (Step 5)
  • ALL PASS + Business Doc Sync Needed → Conductor runs Step 5.5 (Business Doc Sync) before marking complete
  • ANY FAIL → Conductor dispatches loop-fixer with combined fix list

Frequently asked questions

What does the Loop Execution Evaluator AI skill do?

Evaluate-Loop Step 4: EVALUATE EXECUTION. This is the dispatcher agent — it determines the track type and invokes the correct specialized evaluator. Does NOT run a generic checklist. Instead dispatches to: eval-ui-ux (screens/design), eval-code-quality (features/infrastructure), eval-integration (APIs/auth/payments), eval-business-logic (generator/rules/state). Triggered by: 'evaluate execution', 'review implementation', 'check build', '/phase-review'. Always runs after loop-executor.

Why use Loop Execution Evaluator on TypingMind?

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

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

Which AI models can use Loop Execution Evaluator?

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 Execution Evaluator?

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

Is the Loop Execution Evaluator 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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