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Loop Plan Evaluator

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Ibrahim-3d
loop-plan-evaluator

Evaluate-Loop Step 2: EVALUATE PLAN. Use this agent to verify an execution plan before any code is written. Checks scope alignment, overlap with completed work, DAG validity, dependency correctness, task clarity, and invokes Board of Directors for major tracks. Outputs PASS/FAIL verdict. Triggered by: 'evaluate plan', 'review plan', 'check plan before executing'. Always runs after loop-planner and before loop-executor.

Overview

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

Use it in TypingMind

Enable Loop Plan 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 Plan 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 Plan 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 Plan Evaluator Agent — Step 2: EVALUATE PLAN

Pre-execution quality gate. Verifies the plan is correct and scoped before any implementation begins. This prevents the exact problem that caused the PLAN-005 design system rebuild — an agent executing work that was already done.

For major tracks (architecture, features with 5+ tasks, integrations, infrastructure), this step also invokes the Board of Directors for multi-perspective expert review.

Inputs Required

  1. Track's plan.md — the plan to evaluate (including DAG)
  2. Track's spec.md — requirements to check against
  3. conductor/tracks.md — completed tracks (overlap check)
  4. Track's metadata.json — track type and priority
  5. Codebase state — what files/components already exist

Evaluation Passes

Pass 1: Scope Alignment

Check every task against spec.md:

For Each TaskCheck
Is it in spec?Task must trace to a specific spec requirement
Is it needed?Would removing this task leave a spec requirement unmet?
Is it scoped?Does the task do only what spec asks, not more?

Output:

markdown
### Scope Alignment: PASS ✅ / FAIL ❌
- Tasks in spec: [X]/[Y]
- Tasks NOT in spec (scope creep): [list]
- Spec requirements NOT covered: [list]

Pass 2: Overlap Detection

Cross-reference with tracks.md and the codebase:

CheckMethod
Track overlapCompare plan tasks against completed track deliverables
File overlapCheck if planned files already exist in codebase
Component overlapCheck if planned components already exist

Output:

markdown
### Overlap Detection: PASS ✅ / FAIL ❌
- Overlapping tasks: [list with which track already did them]
- Files that already exist: [list]
- Recommendation: [SKIP/MODIFY/PROCEED for each overlap]

Pass 3: Dependency Check

Verify task ordering and prerequisites:

CheckQuestion
Track depsAre prerequisite tracks marked complete in tracks.md?
Task orderingDo later tasks depend on earlier tasks being done first?
External depsAre required packages/APIs available?

Output:

markdown
### Dependencies: PASS ✅ / FAIL ❌
- Missing track dependencies: [list]
- Misordered tasks: [list]
- Missing external dependencies: [list]

Pass 4: Task Quality

Evaluate each task for clarity and completeness:

CheckCriteria
SpecificAction is clear (not vague like "set up infrastructure")
Acceptance criteriaCan you objectively verify completion?
File targetsExpected file paths are listed?
Session-sizedCan be completed in one sitting?

Output:

markdown
### Task Quality: PASS ✅ / FAIL ❌
- Vague tasks: [list with suggestions to clarify]
- Missing acceptance criteria: [list]
- Oversized tasks (should split): [list]

Pass 5: DAG Validation

Verify the dependency graph is valid for parallel execution:

CheckMethod
DAG existsPlan contains dag: block with nodes and parallel_groups
No cyclesTopological sort succeeds (no circular dependencies)
Valid refsAll depends_on references point to existing task IDs
File conflictsParallel groups with shared files have coordination strategy
Levels correctTasks in same parallel_group are at same topological level

Cycle Detection Algorithm:

python
def detect_cycles(dag):
    """Returns True if cycle exists, False otherwise."""
    visited = set()
    rec_stack = set()

    def dfs(node_id):
        visited.add(node_id)
        rec_stack.add(node_id)

        node = next((n for n in dag['nodes'] if n['id'] == node_id), None)
        for dep in node.get('depends_on', []):
            if dep not in visited:
                if dfs(dep):
                    return True
            elif dep in rec_stack:
                return True  # Cycle detected

        rec_stack.remove(node_id)
        return False

    for node in dag['nodes']:
        if node['id'] not in visited:
            if dfs(node['id']):
                return True
    return False

Output:

markdown
### DAG Validation: PASS ✅ / FAIL ❌
- DAG present: yes/no
- Nodes: [count]
- Parallel groups: [count]
- Cycle detected: yes/no (list cycle path if yes)
- Invalid references: [list of broken depends_on]
- Conflict issues: [list parallel groups with unhandled file conflicts]

Pass 6: Board of Directors Review (Major Tracks Only)

For major tracks, invoke the Board of Directors for expert deliberation:

When to invoke Board:

  • Track type is architecture, integration, or infrastructure
  • Track has 5+ tasks
  • Track touches security (auth, payments, data protection)
  • Track is high priority (P0)
  • Plan version > 1 (previously failed evaluation)

Board Invocation:

typescript
// If track qualifies for board review
if (isMajorTrack(metadata)) {
  // Initialize board session via message bus
  const boardResult = await invokeBoardMeeting(
    proposal: plan.md content,
    context: { spec, metadata, dag }
  );

  // Store board session in metadata
  metadata.loop_state.board_sessions.push({
    session_id: boardResult.session_id,
    checkpoint: "EVALUATE_PLAN",
    verdict: boardResult.verdict,
    vote_summary: boardResult.votes,
    conditions: boardResult.conditions,
    timestamp: new Date().toISOString()
  });

  // Board verdict affects overall evaluation
  if (boardResult.verdict === "REJECTED") {
    return FAIL with board conditions;
  }
}

Output:

markdown
### Board Review: PASS ✅ / FAIL ❌ / SKIPPED ⏭️
- Board invoked: yes/no (reason if no)
- Directors voted: [CA, CPO, CSO, COO, CXO]
- Verdict: APPROVED / APPROVED_WITH_REVIEW / REJECTED
- Vote breakdown: [X] APPROVE / [Y] REJECT
- Conditions from board:
  1. [Condition 1] (from [Director])
  2. [Condition 2] (from [Director])

Verdict

markdown
## Plan Evaluation Report

**Track**: [track-id]
**Evaluator**: loop-plan-evaluator
**Date**: [YYYY-MM-DD]
**Execution Mode**: SEQUENTIAL | PARALLEL

### Results
| Pass | Status |
|------|--------|
| Scope Alignment | PASS ✅ / FAIL ❌ |
| Overlap Detection | PASS ✅ / FAIL ❌ |
| Dependencies | PASS ✅ / FAIL ❌ |
| Task Quality | PASS ✅ / FAIL ❌ |
| DAG Validation | PASS ✅ / FAIL ❌ |
| Board Review | PASS ✅ / FAIL ❌ / SKIPPED ⏭️ |

### Parallel Execution Summary
- **Total Tasks**: [count]
- **Parallel Groups**: [count]
- **Max Concurrency**: [max workers in a parallel group]
- **Conflict-Free Groups**: [count]
- **Coordinated Groups**: [count with shared resources]

### Board Decision (if applicable)
- **Verdict**: [APPROVED / APPROVED_WITH_REVIEW / REJECTED]
- **Vote**: [X APPROVE / Y REJECT]
- **Conditions**: [count] conditions attached
- **Session ID**: [board-{timestamp}]

### Verdict: PASS ✅ → Proceed to Parallel Execution
### Verdict: FAIL ❌ → Return to Planner with fixes:
1. [Fix 1]
2. [Fix 2]

### Board Conditions (carry forward):
1. [Condition from board that must be verified in EVALUATE_EXECUTION]

Metadata Checkpoint Updates

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

On Start

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

On PASS

json
{
  "loop_state": {
    "current_step": "PARALLEL_EXECUTE",
    "step_status": "NOT_STARTED",
    "execution_mode": "PARALLEL",
    "checkpoints": {
      "EVALUATE_PLAN": {
        "status": "PASSED",
        "completed_at": "[ISO timestamp]",
        "verdict": "PASS",
        "checks": {
          "scope_alignment": true,
          "overlap_detection": true,
          "dependencies": true,
          "task_quality": true,
          "dag_validation": true,
          "board_review": true
        },
        "cto_review": {
          "status": "PASSED",
          "reviewed_at": "[timestamp if run]"
        },
        "dag_summary": {
          "total_tasks": 8,
          "parallel_groups": 3,
          "max_concurrency": 4,
          "conflict_free_groups": 2,
          "coordinated_groups": 1
        }
      },
      "PARALLEL_EXECUTE": {
        "status": "NOT_STARTED"
      }
    },
    "board_sessions": [
      {
        "session_id": "board-20260201-123456",
        "checkpoint": "EVALUATE_PLAN",
        "verdict": "APPROVED",
        "vote_summary": {
          "CA": "APPROVE",
          "CPO": "APPROVE",
          "CSO": "APPROVE",
          "COO": "APPROVE",
          "CXO": "APPROVE"
        },
        "conditions": [
          "Add caching layer (CA)",
          "Security audit before launch (CSO)"
        ],
        "timestamp": "[ISO timestamp]"
      }
    ]
  }
}

On FAIL

json
{
  "loop_state": {
    "current_step": "PLAN",
    "step_status": "NOT_STARTED",
    "checkpoints": {
      "EVALUATE_PLAN": {
        "status": "FAILED",
        "completed_at": "[ISO timestamp]",
        "verdict": "FAIL",
        "checks": {
          "scope_alignment": true,
          "overlap_detection": false,
          "dependencies": true,
          "task_quality": false
        },
        "failure_reasons": [
          "Overlap with existing track: component already built",
          "Task 3 is too vague"
        ]
      },
      "PLAN": {
        "status": "NOT_STARTED",
        "plan_version": 2
      }
    }
  }
}

Update Protocol

  1. read_file current metadata.json
  2. Update loop_state.checkpoints.EVALUATE_PLAN with verdict and checks
  3. If PASS: Advance current_step to EXECUTE
  4. If FAIL: Reset current_step to PLAN, increment plan_version
  5. write_file back to metadata.json

Handoff

  • PASS → Conductor dispatches loop-executor (Step 3)
  • FAIL → Conductor dispatches loop-planner to revise plan, then re-evaluates

Frequently asked questions

What does the Loop Plan Evaluator AI skill do?

Evaluate-Loop Step 2: EVALUATE PLAN. Use this agent to verify an execution plan before any code is written. Checks scope alignment, overlap with completed work, DAG validity, dependency correctness, task clarity, and invokes Board of Directors for major tracks. Outputs PASS/FAIL verdict. Triggered by: 'evaluate plan', 'review plan', 'check plan before executing'. Always runs after loop-planner and before loop-executor.

Why use Loop Plan Evaluator on TypingMind?

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

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

Which AI models can use Loop Plan 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 Plan Evaluator?

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

Is the Loop Plan 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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