CEO Audit — Dual-Model C-Level Council

Then re-run the find command to get the path. Store it in $CODEX for all subsequent GPT calls.

If found: proceed immediately. No setup needed.

Phase 1: Scan Project Context

Before suggesting experts, understand the project:

1. Read CLAUDE.md (or README.md if absent)
2. Scan .claude/rules/ for domain context
3. Glance at top-level file structure

Based on findings, generate 4-6 expert roles tailored to this specific project. Roles must reflect the project's actual domain, challenges, and current stage — not generic defaults.

Role Examples by Project Type

Don't copy these — generate fresh roles based on actual context:

Phase 2: Assemble the Council

Always ask the user before proceeding. Do not pick roles yourself.

2a. Choose Model Mode

Ask via AskUserQuestion (single-select):

2b. Choose Roles

Use AskUserQuestion with multiSelect: true. Show the 4-6 role options you generated in Phase 1 with short descriptions of their focus. The user can always write custom roles via "Other".

Minimum 2 experts. If the user picks only 1, suggest adding one more — productive disagreement requires at least two perspectives.

Phase 3: Collect Project Data

Gather project state to feed all experts. Prepare this block once — it must be identical for every expert regardless of model.

Read:

• Key metrics and data files identified during Phase 1
• Strategy and planning documents
• Recent decisions (e.g., git log --oneline -10)
• Previous council analyses (if any exist in docs/)

Skip: GitHub stars, clone counts, contributor lists — vanity metrics add noise without strategic signal.

Store the result as DATA_BLOCK. Every prompt in Phase 4 uses the same DATA_BLOCK verbatim.

Phase 4: Generate Expert Prompts

Claude prompts (natural language)

You are the [ROLE] for [PROJECT NAME]. Analyze the data below from a [DOMAIN] perspective.

Focus on:
- [3-6 specific focus areas relevant to role and project]

Data:
[DATA_BLOCK — identical for all experts]

[Role-specific personality instruction: "show the math", "be the contrarian",
"prioritize by effort/impact ratio", etc.]

Respond in the same language as the data provided.

GPT-5.4 prompts (XML-structured)

Only in Cross-model and GPT-only modes.

For each role, generate the XML prompt using gpt-prompt-template.md. Write each to a temp file:

cat > /tmp/ceo-audit-gpt-{role-slug}.txt << 'PROMPT'
[XML prompt content — see gpt-prompt-template.md for structure]
PROMPT

Rules:

• Same DATA_BLOCK, same focus areas, same personality instruction as the Claude version
• Only the format differs (natural language vs XML-structured)
• Use --prompt-file flag to avoid shell quoting issues with long prompts

Phase 5: Execute — ALL Experts in ONE Message

Launch everything simultaneously. One message = true parallelism.

Claude-only mode

Task(subagent_type: "general-purpose", model: "opus", prompt: "<CFO prompt>", description: "CFO analysis")
Task(subagent_type: "general-purpose", model: "opus", prompt: "<CTO prompt>", description: "CTO analysis")

Cross-model mode

Write GPT prompt files first (via Bash), then launch all experts in a single message:

# Claude experts
Task(subagent_type: "general-purpose", model: "opus", prompt: "<CFO Claude prompt>", description: "CFO (Claude Opus)")
Task(subagent_type: "general-purpose", model: "opus", prompt: "<CTO Claude prompt>", description: "CTO (Claude Opus)")

# GPT-5.4 experts
Bash(command: 'node "$CODEX" task --json --prompt-file /tmp/ceo-audit-gpt-cfo.txt',
     description: "CFO (GPT-5.4)", timeout: 300000)
Bash(command: 'node "$CODEX" task --json --prompt-file /tmp/ceo-audit-gpt-cto.txt',
     description: "CTO (GPT-5.4)", timeout: 300000)

GPT-only mode

Same as above but only the Bash calls (no Task tool calls).

Fallback

If Codex is unavailable (auth expired, CLI missing, network error):

⚠️ GPT-5.4 unavailable: [reason]
Switching to Claude-only mode.

Wait for all experts to return results before proceeding to Phase 6.

Phase 6: Synthesize

The synthesis is the entire value of the council. Never skip it.

Claude-only synthesis

# Council Session: [DATE]

## Council Members
[List of selected experts and their focus]

## Context
[Current state snapshot — brief]

## [Expert 1]
[Key findings and recommendations]

## [Expert 2]
[Key findings and recommendations]

## Consensus (all agree)
1. ...

## Disagreements
| Expert | Position | Argument |
|--------|----------|----------|

## Decisions
_To be filled after discussion._

Cross-model synthesis

GPT responses arrive as JSON — extract the rawOutput field for each expert.

For each role, compare Claude vs GPT findings side by side:

# Cross-Model Council Session: [DATE]

## Council Members
| Role | Claude Opus | GPT-5.4 |
|------|-------------|---------|
| CFO | ✅ | ✅ |
| CTO | ✅ | ✅ |

## Context
[Current state snapshot — brief]

## Per-Role Comparison

### [ROLE] Analysis

**Claude Opus:**
[Key findings — 3-5 bullets]

**GPT-5.4:**
[Key findings — 3-5 bullets]

**Alignment:** 🟢 Agree / 🟡 Partial / 🔴 Contradict
**Details:** [what matches, what diverges]

---

## Cross-Model Synthesis

### 🟢 Overlapping Findings (both models agree)
> High confidence — act on these first

1. [Finding] — both models converge
2. ...

### 🔵 Claude-Only Findings
> Potential GPT blind spots — verify before dismissing

1. [Finding] — Claude sees, GPT misses
2. ...

### 🟣 GPT-Only Findings
> Potential Claude blind spots — mandatory check

1. [Finding] — GPT sees, Claude misses
2. ...

### 🔴 Direct Contradictions
| Topic | Claude position | GPT position | Recommendation |
|-------|----------------|-------------|----------------|

## Model Agreement Score
- Full overlap: N findings
- Partial: N findings
- Contradictions: N findings
- **Agreement rate:** X%

## Top Recommendations (by confidence)
1. [Overlapping — highest confidence]
2. ...

## Decisions
_To be filled after discussion._

Phase 7: Save

Save to docs/council-[DATE].md (or a project-specific path if context suggests one).

Common Mistakes