AI Investment Workflow Skills

Evidence-disciplined AI workflow skills for investor-style company research, business model understanding, diligence planning, versioned updates, and evidence review.

中文镜像 / Chinese mirror

These workflow skills were originally designed from a primary-market technology investing context, but they are also useful for founders, analysts, strategy teams, product and business operators, and anyone who wants to understand a company through an investment research lens.

This is not a prompt collection. It is a small workflow package: each skill defines input boundaries, output structure, evidence discipline, prohibited wording, quality checks, sample inputs, sample outputs, and its relationship to an AI InvestOS-style investment workflow.

What This Is Not

• Not an investment recommendation engine.
• Not legal, financial, tax, securities trading, or investment advice.
• Not a substitute for professional diligence, human judgment, investment committee review, or business decision-making.
• Not a claim that AI can automatically decide whether to invest.
• Not based on real project data. All examples are fictional and require human review.

Who It Is For

Core users

• Primary-market investors and investment analysts.
• Venture capital, growth equity, CVC, strategic investment, and corporate strategy teams.
• FA / fundraising advisors and diligence teams.
• Technology, AI, robotics, AI infrastructure, and AI agent industry researchers.

Extended users

• Founders who want to understand how investors may read a company.
• Product, strategy, business operations, and commercial analysis teams.
• Readers who want to understand a company through project essence, business model, risk hypotheses, diligence questions, version changes, and evidence boundaries.

30-Second Overview

This repository helps turn company materials into structured investor-style research:

1. Triage source materials and evidence boundaries.
2. Produce a quick-look company analysis.
3. Map investment logic into risk hypotheses.
4. Convert risks into diligence questions and material requests.
5. Update the analysis when new materials arrive.
6. Audit the draft before an investment workpaper is circulated.

Commercial and business model understanding are core objects inside the investment research workflow. They are not positioned here as a generic business-analysis slogan.

Recommended Starting Points

Recommended first run:

1. primary-market-quick-look Reason: the best starting point for understanding how to analyze a company through an investment research lens.

2. investment-risk-radar Reason: converts highlights, assumptions, and evidence gaps into risk hypotheses and verification actions.

3. dd-question-map Reason: turns risk hypotheses into diligence questions, material requests, data checks, technical validation, customer references, and transaction review.

When new materials arrive:

• versioned-investment-update Reason: preserves V1/V2 judgment changes instead of simply overwriting the prior report.

Before circulating a workpaper:

• investment-evidence-audit Reason: checks evidence boundaries, overstatement risk, and required human review before circulation.

Workflow

flowchart LR
  A["Source material triage"] --> B["Primary-market quick look"]
  B --> C["Investment risk radar"]
  C --> D["DD question map"]
  D --> E["Versioned investment update"]
  E --> F["Investment evidence audit"]
  F --> G["Investment analysis workpaper"]

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Six Skills

Skill	Role	Main Output
source-material-triage	Classify project materials and evidence boundaries	Material inventory, maturity, missing materials
primary-market-quick-look	First-pass investor-style company view	Project essence, investment logic, highlights, counter-hypotheses
investment-risk-radar	Risk mapping from logic and evidence gaps	Linked risk hypotheses and verification actions
dd-question-map	Diligence execution map	Interview questions, material requests, data checks, technical and transaction validation
versioned-investment-update	Update analysis after new materials	V1/V2 judgment change summary
investment-evidence-audit	Evidence boundary quality gate	Overstatement flags and rewrite suggestions

Quick Start / How to Use

These skills do not require a full system to try. A user can copy a skill's skill.md, input_contract.md, output_schema.md, and quality_checklist.md into an AI assistant together with desensitized company materials.

Recommended first run:

1. primary-market-quick-look
2. investment-risk-radar
3. dd-question-map

When new materials arrive:

• versioned-investment-update

Before circulating a workpaper:

• investment-evidence-audit

For a more technical workflow, users may keep skills and project materials in local directories and invoke a selected skill through Codex or a script-like wrapper.

Pseudo-command only; this repository does not include a built-in production CLI.

Example pattern:

run-skill primary-market-quick-look \
  --project ./projects/demo-company \
  --out ./outputs/demo-company/quick_look_v1.md

The command above is an illustrative CLI pattern, not a real command shipped by this repo.

Using with Codex or Local File-Based Workflows

The skills define workflow contracts: what inputs are acceptable, how evidence should be labeled, what outputs should contain, and which claims require human review. Codex or similar AI coding agents can read local skill files and desensitized project materials, organize context, and generate Markdown workpapers.

Humans remain responsible for review, judgment, follow-up questions, and any business or investment decision. This repository does not include production orchestration code, model integrations, API connectors, or a built-in CLI.

Evidence Labels

Use these labels consistently:

• user_provided: material provided by the user.
• company_claim: company statement that has not been independently verified.
• interview_note: interview note or meeting summary.
• financial_snapshot: financial or operating data snapshot.
• third_party_unverified: third-party material that still requires verification.
• inferred: analytical inference made from available material.
• missing_evidence: required evidence not yet available.
• needs_human_review: item requiring human review before circulation.

Public Boundary

• All examples use the fully fictional company NovaCompute.
• All numbers, customers, materials, revenue, financing, valuation, and transaction references in examples are fictional examples.
• Outputs must be reviewed by humans before use.
• These workflows are designed for research organization and evidence discipline; they do not produce investment, legal, financial, tax, or securities trading advice.

End-to-End Example

See examples/nova_compute_end_to_end.md for a fictional walkthrough across all six skills.

Relationship to AI InvestOS

AI InvestOS is a broader workflow concept for organizing investment research, evidence discipline, risk mapping, diligence planning, versioned workpapers, and human review. This repository extracts a public, data-free workflow layer that can be reused independently.

Repository Structure

README.md
README.zh-CN.md
LICENSE
RELEASE_CHECKLIST.md
CONTRIBUTING.md
CHANGELOG.md
examples/
  nova_compute_end_to_end.md
  nova_compute_end_to_end.zh-CN.md
skills/
  source-material-triage/
  primary-market-quick-look/
  investment-risk-radar/
  dd-question-map/
  versioned-investment-update/
  investment-evidence-audit/

Each skill directory contains the English canonical files plus a zh-CN/ mirror.

How to Reference

Suggested neutral wording:

AI Investment Workflow Skills is an open workflow package for evidence-disciplined, investor-style company research and primary-market diligence. It includes six reusable skills covering source triage, quick-look analysis, risk mapping, DD question design, versioned updates, and evidence review.

License

See LICENSE.