Investment Skills Lexicon: How great investors think Finterm AI released an open-source Investment Skills Lexicon that distills Warren Buffett's investment framework from 72 primary documents (950,000 words) into 65 evidence-bounded decision cards for use by AI agents. The project processed roughly 3.4 billion tokens to extract 3,809 insights, enabling agents to apply Buffett's mental models to financial analysis without loading the full corpus. The framework supports three workflows—financial analysis, memo review, and thesis evaluation—and is available via the Agent Skills standard for tools like Claude Code and Codex. How great investors think, distilled from primary sources. The primary Buffett and Berkshire corpus runs 72 documents and about 950,000 words. This project extracts, analyzes, and condenses it 3,809 extracted insights and roughly 3.4 billion tokens of processing into 65 decision cards that an agent loads 5–12 at a time. You can always ask an agent to “analyze this like Warren Buffett.” But the goal is not to role play an investor’s voice. Rather, we want an agent to apply the underlying frameworks and mental models used by that investor to real filings, memos, and theses without searching or reading the whole corpus into context. The idea of this project is to spend the tokens and do heavy thinking once, then reuse the insights in a context-efficient way. This works with Claude Code, Codex, or any agent that reads the open Agent Skills https://agentskills.io standard. The Buffett investment framework distills ~2,300 pages of primary Buffett and Berkshire writing into 65 evidence-bounded decision cards an agent can use. It is an agent skill for evidence-bounded financial analysis, investment-memo review, and thesis evaluation . It offers three focused workflows that surface assumptions, counterarguments, missing evidence, and invalidation conditions. The framework helps an agent reason about a business. It does not issue buy, sell, hold, entry-price, position-size, or trade instructions. The complete agent instructions are in SKILL.md /finterm-ai/investment-skills/blob/main/skills/buffett-investment-framework/SKILL.md , which is self-contained once installed. | Stage | Contents | Scale | |---|---|---| | Source corpus | 48 Berkshire shareholder letters 1977–2024 , Greg Abel’s 2025 transition letter, the 1957–1970 Buffett Partnership letter compilation, other Buffett and Berkshire writings, The Essays of Warren Buffett, and Buffett: The Making of an American Capitalist | 72 documents 2,300 pages, 950,000 words | | Extraction | Source-native insights distilled from the corpus | 3,809 insights | | Processing | Extraction and synthesis ran across Codex Sol and Claude Fable | about 3.4 billion tokens | | Skill lexicon | Final task-facing output | 65 cards · 8 modules · about 51 pages | This “lexicon” is a growing collection of insights. Decades of investment thinking sit in writings, interviews, and filings that few people or even agents have time to analyze deeply. So we’re extracting these piece by piece. The first piece is the Buffett framework. Queued next: Munger, Graham, Marks, Fisher, Lynch, Sleep and Zakaria, Grantham, Greenblatt, and Druckenmiller. Check back here as we’re adding a new one each week Primary sources and framework focus for each are in the roadmap /finterm-ai/investment-skills/blob/main/docs/roadmap.md . Install with the skills CLI https://github.com/vercel-labs/skills more options under Other Install Methods other-install-methods : npx skills add finterm-ai/investment-skills --skill buffett-investment-framework --yes Then ask in your own words and attach the evidence. The skill picks the workflow and loads only the cards that fit: What are the real owner earnings in these filings? Review this investment memo and tell me which claims hold up. Here is my thesis on this company. What would prove it wrong? Most agents load the skill from its description, so naming it is optional. In Codex, type $ to mention it explicitly. | Workflow | Starting load | What it returns | |---|---|---| | Financial analysis | F01 , F02 , F05 , F06 , F07 | Filing inventory, reported-to-owner bridge, normalized segments, returns, obligations, assumptions, and blocked calculations | | Memo review | D01 , D03 , B02 , V01 , V05 , V06 , R01 , R07 | One dispositioned row per material claim, with counterevidence and missing evidence | | Thesis evaluation | D02 , D03 , B01 , B06 , V01 , V04 , R01 , R07 | Component map, mechanism and valuation tests, owner-harm paths, and invalidation conditions | The router starts with 5–8 cards, adds only material management, allocation, financing, or specialized overlays. It caps at 12 cards in one pass to preserve context and stay focused. | Module | Cards | Decision use | |---|---|---| | D01 – D09 Business economics /finterm-ai/investment-skills/blob/main/skills/buffett-investment-framework/references/02-business-economics.md B01 – B07 Management and governance /finterm-ai/investment-skills/blob/main/skills/buffett-investment-framework/references/03-management-governance.md M01 – M08 Financial reality /finterm-ai/investment-skills/blob/main/skills/buffett-investment-framework/references/04-financial-reality.md F01 – F07 Valuation /finterm-ai/investment-skills/blob/main/skills/buffett-investment-framework/references/05-valuation.md V01 – V06 Capital allocation /finterm-ai/investment-skills/blob/main/skills/buffett-investment-framework/references/06-capital-allocation.md C01 – C09 Risk and monitoring /finterm-ai/investment-skills/blob/main/skills/buffett-investment-framework/references/07-risk-monitoring.md R01 – R07 Specialized overlays /finterm-ai/investment-skills/blob/main/skills/buffett-investment-framework/references/08-specialized-overlays.md S01 – S12 Every card uses the same contract: decision question, guidance, use condition, analytical actions, observable output, limits, readable source basis, and abbreviated corroboration citations. Every completed analysis reports: - Question, horizon, scope, and exclusions. - Evidence received and material missing inputs. - The 5–12 cards loaded and why. - Sourced calculations, bridges, and mechanism tests. - Supported, challenged, or unresolved findings. - Counterevidence and alternate mechanisms. - Limits and blocked branches. - Monitoring evidence and invalidation conditions. The result ends with an analytical summary. The framework is an editorial synthesis of published Buffett and Berkshire writings , not a transcription or an attempt to imitate Buffett’s voice. The development corpus drew primarily from Berkshire Hathaway annual letters, Buffett’s 2015 50th-anniversary essay, and “The Superinvestors of Graham-and-Doddsville.” The material was distilled in four steps: - Extract claims, definitions, analytical tactics, examples, and source references while preserving their source identity. - Inventory the recurring decision questions before applying a product taxonomy. - Reconcile overlapping questions by analytical consequence, splitting items when their evidence needs or failure conditions differ. - Project the result into 65 consistent cards, eight modules, and three task workflows. flowchart LR corpus "