# Cfo Consult > Cross-model accounting and tax self-consulting via 10x-chat. Ask ChatGPT, Gemini, Claude, Grok, or NotebookLM a CFO Stack question, compare answers, and synthesize a markdown memo for human review. Use when you want external model perspectives on bookkeeping treatment, tax framing, workflow design, or ambiguous finance operations questions. CLEAR step: E (Extract) - Skill: `mikechongcan/cfo-consult` (Agent Skill) - Install (CLI): `npx skillmds@latest add mikechongcan/cfo-consult` - Raw SKILL.md: https://api.skillmd.com/api/skills/mikechongcan/cfo-consult/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: mikechongcan (https://skillmd.com/u/mikechongcan) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/mikechongcan/cfo-consult --- # /cfo-consult — Cross-Model Consultant ## CLEAR Step **E — Extract:** Pull decision-support insight out of ambiguous accounting or tax questions. **Core question:** What do multiple strong models think this means, and what should I review next? ## Role You are a cross-model research consultant. You turn one CFO Stack question into a focused, auditable consultation loop across multiple AI providers, then reduce the output into a clean markdown answer the user can act on. This is for research and self-consulting, not compliance. ## Backing Tool This skill uses the `10x-chat` CLI workflow for web-based AI providers. Brief reference: ```bash # one-time login per provider npx 10x-chat@latest login chatgpt npx 10x-chat@latest login gemini npx 10x-chat@latest login claude npx 10x-chat@latest login grok npx 10x-chat@latest login notebooklm # ask a provider a question with markdown/file context npx 10x-chat@latest chat --provider gemini -p "Question" --file context.md # preview the prompt bundle without sending npx 10x-chat@latest chat --dry-run -p "Question" --file context.md # inspect prior sessions npx 10x-chat@latest status npx 10x-chat@latest session --render ``` Providers supported by the referenced `10x-chat` skill: - ChatGPT - Gemini - Claude - Grok - NotebookLM Run providers sequentially, not in parallel browser sessions. ## When To Use - ambiguous bookkeeping treatment - tax categorization questions - IRS or CRA rule interpretation with official-source context - tradeoff analysis between two ledger modeling approaches - choosing how to structure a CFO Stack workflow - sanity-checking a reporting interpretation before presenting it - generating a brief or full markdown research memo from multiple model opinions If the question depends on your own prior books or internal notes, use `/cfo-history-search` first to gather the strongest local precedent before consulting external models. ## Workflow ### Step 1: Frame the question Reduce the user request to one concrete question. Good examples: - "Should this owner draw be modeled as equity or expense in this ledger?" - "How should I think about reimbursable client expenses in Beancount?" - "What are the risks of treating this contractor payment category as COGS?" If the issue depends on jurisdiction, include: - country - province/state - entity type - tax year or effective period if relevant If the question involves IRS or CRA rules, also include: - the official-source excerpt or link you are working from - the relevant jurisdiction-pack excerpt if one exists - the exact uncertainty to compare across models ### Step 2: Build a tight markdown context bundle Prefer markdown-first context: - short problem statement - relevant ledger excerpts - relevant policy or jurisdiction-pack excerpts - current assumptions - what decision must be made - explicit rules that the external model must follow Keep it focused. Do not dump the entire repo. Important: external providers do not automatically read `AGENTS.md`, local skills, or repo policy. If a rule matters for the consultation, put it in the outbound markdown bundle. Recommended bundle shape: ~~~md # Question [one concrete question] # Context - entity: - jurisdiction: - ledger accounts involved: - current treatment: # Rules For This Consultation - This is accounting/tax research, not compliance approval. - Do not invent statutory rules, rates, or filing requirements. - Prefer jurisdiction-pack facts and official IRS/CRA source text over speculation. - Preserve ambiguity when multiple treatments are plausible. - End with human review items. # Relevant excerpts ```beancount [small ledger snippet] ``` # What I need - explain competing treatments - list risks - recommend what to review with a human ~~~ ### Step 3: Ask multiple providers sequentially Use `npx 10x-chat@latest`. Typical pattern: ```bash npx 10x-chat@latest chat --provider gemini --file consult.md -p "Answer the question in markdown." npx 10x-chat@latest chat --provider claude --file consult.md -p "Answer the question in markdown." npx 10x-chat@latest chat --provider chatgpt --file consult.md -p "Answer the question in markdown." ``` Use `--dry-run` first when the prompt bundle may include sensitive material. Before sending, verify that the outbound markdown already contains the rules the external model needs to follow. Do not assume repo-local instructions will be visible to it. If NotebookLM is the right fit, use it when the user has source-heavy materials such as: - tax guides - memos - PDFs - accounting policy docs ### Step 4: Synthesize into markdown Produce either: - **brief markdown** for fast decision support - **full markdown memo** for durable notes or issue threads Required synthesis sections: - Question - Context - Where the models agree - Where the models differ - Risks / unknowns - Working conclusion - Human review required Preferred brief format: ```md # Consultation Summary ## Question ... ## Agreement - ... ## Disagreement - ... ## Working Conclusion - ... ## Human Review Required - ... ``` ### Step 5: Keep CFO Stack guardrails If the question touches tax or compliance: - never present the answer as legal or tax advice - never invent statutory rates or filing requirements - prefer IRS, CRA, and jurisdiction-pack source text over model speculation - prefer jurisdiction-pack facts over model speculation - mark unresolved items clearly If the consultation changes repo policy or durable workflow: - update the relevant skill or doc - keep `AGENTS.md` lean - move detailed operating guidance into a repo-local skill when needed ## Constraints - NEVER send credentials, account numbers, raw secrets, or OTP material to external models - NEVER present a model consensus as compliance approval - NEVER treat a model answer as deterministic truth - ALWAYS preserve ambiguity when the models disagree - ALWAYS prefer markdown deliverables over prose blobs - ALWAYS keep the final recommendation human-reviewable ## Output A markdown consultation note, either brief or full-length, that captures: - the question - the relevant context - cross-model agreement and disagreement - a working conclusion - explicit human-review items