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Board Of Directors

Community
Ibrahim-3d
board-of-directors

Simulate a 5-member expert board deliberation for major decisions. Use when evaluating plans, architecture choices, feature designs, or any decision requiring multi-perspective expert analysis. Triggers: 'board review', 'get expert opinions', 'board meeting', 'director evaluation', 'consensus review'.

Overview

PublisherIbrahim-3d
Repositoryorchestrator-supaconductor
Skill nameboard-of-directors
Stars
378
Forks
38
Bundled files
5
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.

  • 5 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by Ibrahim-3d on GitHub. Read the source before you install it.

Installation

Install the Board Of Directors 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/board-of-directors .claude/skills/board-of-directors
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Board Of Directors 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 Board Of Directors 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 Board Of Directors 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.

Board of Directors Simulation

Simulates a 5-member expert board that deliberates, debates, and reaches consensus on major decisions. Each director brings domain expertise and can challenge other directors' opinions.

The Board

RoleDomainEvaluates
Chief Architect (CA)TechnicalSystem design, patterns, scalability, tech debt, code quality
Chief Product Officer (CPO)ProductUser value, market fit, feature priority, scope, usability
Chief Security Officer (CSO)SecurityVulnerabilities, compliance, data protection, risk assessment
Chief Operations Officer (COO)ExecutionFeasibility, timeline, resources, process, deployment
Chief Experience Officer (CXO)ExperienceUX/UI, accessibility, user journey, design consistency

When to Invoke the Board

  • Track Planning — Before starting major tracks
  • Architecture Decisions — ADRs, system design choices
  • Feature Evaluation — New feature proposals
  • Risk Assessment — Security or operational concerns
  • Conflict Resolution — When leads disagree

Deliberation Protocol

Phase 1: Individual Assessment (Parallel)

Each director reviews the proposal independently:

DISPATCH via Task tool (all 5 in parallel):
  - CA: Evaluate technical aspects
  - CPO: Evaluate product aspects
  - CSO: Evaluate security aspects
  - COO: Evaluate operational aspects
  - CXO: Evaluate experience aspects

Each director outputs:

json
{
  "director": "CA",
  "verdict": "APPROVE" | "CONCERNS" | "REJECT",
  "score": 1-10,
  "key_points": ["..."],
  "concerns": ["..."],
  "questions_for_board": ["Question for CPO about...", "Challenge to COO on..."]
}

Phase 2: Board Discussion (Sequential via Message Bus)

Directors respond to each other's questions and challenges:

MESSAGE BUS: conductor/tracks/{track}/.message-bus/board/

1. Post all Phase 1 assessments to board/assessments.json
2. Each director reads others' assessments
3. Directors post rebuttals/responses to board/discussion.jsonl
4. Max 3 rounds of discussion

Discussion message format:

json
{
  "from": "CA",
  "to": "CPO",
  "type": "CHALLENGE" | "AGREE" | "QUESTION" | "CLARIFY",
  "message": "Regarding your concern about scope...",
  "changes_my_verdict": true | false
}

Phase 3: Final Vote

After discussion, each director casts final vote:

json
{
  "director": "CA",
  "final_verdict": "APPROVE" | "REJECT",
  "confidence": 0.0-1.0,
  "conditions": ["Must add rate limiting", "Needs load testing"],
  "dissent_noted": false
}

Phase 4: Board Resolution

Aggregate votes and produce board decision:

ScenarioResolution
5-0 or 4-1 APPROVEAPPROVED — Proceed with any conditions noted
3-2 APPROVEAPPROVED WITH REVIEW — Proceed but schedule follow-up
3-2 REJECTREJECTED — Address major concerns first
4-1 or 5-0 REJECTREJECTED — Significant rework needed
2-2-1 (tie with abstain)Chief Architect (CA) casts tiebreaking vote based on technical merit

Phase 5: Persist Decision (MANDATORY)

After reaching resolution, you MUST persist the decision to files:

  1. Create directory: Use run_shell_command mkdir -p conductor/tracks/{trackId}/.message-bus/board/
  2. write_file resolution.md with the Board Output Format (below)
  3. write_file session-{timestamp}.json:
    json
    {"session_id": "...", "verdict": "...", "vote_summary": {...}, "conditions": [...], "timestamp": "..."}

Then return ONLY this concise summary to the orchestrator:

json
{"verdict": "APPROVED|REJECTED|ESCALATE", "conditions": ["..."], "vote": "4-1"}

Orchestrator Integration

Invoke Board from Conductor

typescript
async function invokeBoardReview(proposal: string, context: object) {
  // 1. Initialize board message bus
  await initBoardMessageBus(trackId);

  // 2. Phase 1: Parallel assessment
  const assessments = await Promise.all([
    Task({
      description: "CA board assessment",
      prompt: `You are the Chief Architect on the Board of Directors.

        PROPOSAL: ${proposal}
        CONTEXT: ${JSON.stringify(context)}

        Follow the directors/chief-architect.md profile.

        Output your assessment as JSON.`
    }),
    Task({ description: "CPO board assessment", ... }),
    Task({ description: "CSO board assessment", ... }),
    Task({ description: "COO board assessment", ... }),
    Task({ description: "CXO board assessment", ... })
  ]);

  // 3. Phase 2: Discussion rounds
  await runBoardDiscussion(assessments, maxRounds: 3);

  // 4. Phase 3: Final vote
  const votes = await collectFinalVotes();

  // 5. Phase 4: Resolution
  return aggregateBoardDecision(votes);
}

Board Output Format

markdown
## Board of Directors Resolution

**Proposal**: [Brief description]
**Session**: [timestamp]
**Verdict**: APPROVED | APPROVED WITH REVIEW | REJECTED | ESCALATE

### Vote Summary
| Director | Vote | Confidence | Key Condition |
|----------|------|------------|---------------|
| CA | APPROVE | 0.9 | Add caching layer |
| CPO | APPROVE | 0.8 | Validate with usability check |
| CSO | CONCERNS→APPROVE | 0.7 | Security audit before launch |
| COO | APPROVE | 0.85 | Need 2-week buffer |
| CXO | APPROVE | 0.95 | Accessibility is solid |

**Final: 5-0 APPROVE**

### Conditions for Approval
1. Add caching layer for API responses (CA)
2. Complete security audit before production (CSO)
3. Buffer timeline by 2 weeks (COO)

### Discussion Highlights
- CA challenged CPO on scope creep → CPO agreed to defer Phase 2
- CSO raised auth concern → CA proposed token rotation solution
- CXO praised accessibility approach, no concerns

### Dissenting Opinions
None recorded.

---
*Board session complete. Proceed with implementation.*

Director Skills

Each director has specialized evaluation criteria. See:

  • directors/chief-architect.md — Technical excellence
  • directors/chief-product-officer.md — Product value
  • directors/chief-security-officer.md — Security posture
  • directors/chief-operations-officer.md — Execution reality
  • directors/chief-experience-officer.md — User experience

Quick Invocation

For rapid board review without full deliberation:

markdown
/board-review [proposal]

Returns: Quick assessment from each director (no discussion phase)

For full deliberation:

markdown
/board-meeting [proposal]

Returns: Full 4-phase deliberation with discussion

Integration with Evaluate-Loop

The board can be invoked at key checkpoints:

CheckpointBoard Involvement
EVALUATE_PLANFull board meeting for major tracks
EVALUATE_EXECUTIONQuick review for implementation quality
Pre-LaunchSecurity + Operations deep dive
Post-MortemAll directors analyze what went wrong

Message Bus Structure

.message-bus/board/
├── session-{timestamp}.json    # Session metadata
├── assessments.json            # Phase 1 outputs
├── discussion.jsonl            # Phase 2 messages
├── votes.json                  # Phase 3 final votes
└── resolution.md               # Phase 4 board decision

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Board Of Directors AI skill do?

Simulate a 5-member expert board deliberation for major decisions. Use when evaluating plans, architecture choices, feature designs, or any decision requiring multi-perspective expert analysis. Triggers: 'board review', 'get expert opinions', 'board meeting', 'director evaluation', 'consensus review'.

Why use Board Of Directors on TypingMind?

Because you install it once and use it with any model. Board Of Directors 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 Board Of Directors in TypingMind?

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

Which AI models can use Board Of Directors?

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 Board Of Directors?

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

Is the Board Of Directors 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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