菜单
Use when starting FP&A work for a new company, onboarding a business into openfpa, or asked to "understand my business / set up a model for us" before any forecasting - produces a durable business profile and generates business-specific skills.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Use when the user wants their model in Excel with real working formulas - "export this to Excel", "a workbook I can hand my board", "something that recalculates when I change an assumption". Produces a live-formula workbook generated from the model structure and verified against the engine before delivery.
Use when producing a board deck, investor update, or CFO briefing from an openfpa forecast - turning model output into a board-ready narrative and exportable artifacts.
Use when building a new openfpa forecast model from a company's financials - a trial balance, a P&L export, or a pasted income statement - and you need a runnable config to exist before any forecasting or analysis.
Use when modeling a multi-segment company that discloses segment net sales and segment Adjusted EBITDA (ASU 2023-07) but not segment COGS or operating income - rolls segment P&Ls into a consolidated forecast and reconciles total segment Adjusted EBITDA to the disclosed total. Generated for Fox Factory (PVG/AAG/SSG).
Use when you want the model to learn from how its past forecasts actually turned out - scoring forecasts against the company's real actuals, backtesting assumptions on history, and proposing ratified improvements. Runs at/after monthly close.
Use when a human reviewing a forecast catches something off ("December always spikes", "you're double-counting deferred revenue", "that Q3 number was a one-time contract") - captures it as a durable, typed correction in the company's memory so future forecasts are grounded by it.
| name | fpa-learn-business |
| description | Use when starting FP&A work for a new company, onboarding a business into openfpa, or asked to "understand my business / set up a model for us" before any forecasting - produces a durable business profile and generates business-specific skills. |
Before scaffolding any model, learn the business. This produces two artifacts: a durable business profile that every other openfpa skill reads first, and - where the standard skills don't fit - bespoke skills/agents generated for this specific company. The toolkit re-tools itself per business instead of forcing a generic template.
Core principle: A forecast is only as good as the business understanding behind it. Encode that understanding once, explicitly, so it grounds everything downstream.
.fpa/business-profile.md is missing or staleDo not force this workflow when the user asks for a narrow task that can be completed without understanding the whole company.
Check and initialize the workspace. Run
openfpa status <company-root>. If it is uninitialized, run
openfpa init <company-root> --business-name "<name>". Then run
openfpa doctor <company-root>. The CLI emits JSON. If the console script is
unavailable in a source checkout, use python3 -m pyfpa.cli.
Inspect local evidence first. Run openfpa inspect-data <data-root> for
every user-supplied folder, then read the relevant financials, operating
files, documentation, and existing model code before asking questions.
Record each fact with openfpa intake-record <company-root>, including file
references and confidence. Never access an external MCP/API system without
the user's approval.
Ask only what remains unknown. Run
openfpa intake-next <company-root> and ask that related round of at most
three questions. After every response, call openfpa intake-record with
--source-type user. Direct answers are confirmed immediately. Only ask the
user to resolve conflicting or low-confidence inferred facts.
Repeat short rounds until pyfpa.intake_ready(intake) is true. Do not ask
questions already answered by local evidence or earlier conversation.
Propose the company architecture. Build a pyfpa.ArchitectureProposal
covering the model objective, connectors, company-specific model components,
generated skills, risks, and validation checks. Call
pyfpa.write_onboarding_outputs(intake, workspace, proposal) to write:
.fpa/business-profile.md.fpa/decisions/initial-model-architecture.mdStop for approval. Summarize known facts, remaining unknowns, and the proposed architecture. Do not scaffold or generate artifacts until the user approves the proposal.
After approval, seed from the portfolio library. If one exists, start the model from what generalized across same-type clients. Priors are seeds; this client's learning loop refines them.
Identify gaps the standard skills don't cover, and propose bespoke skills/agents:
sku-profitability skillarr-waterfall / cohort-retention skilldriver-cost-scorecard skillGenerate approved artifacts using the repository's agent operating contract and local
skill format, into skills/generated/ (and agents/generated/). Company
models and connectors belong in models/generated/ and
connectors/generated/. Each generated artifact MUST cite the profile facts
that justify it and include a focused test or reconciliation check.
Apply existing corrections. Before forecasting, fold in human corrections: pyfpa.apply_corrections(cfg, pyfpa.load_corrections('.fpa/corrections')). Route any type: structural corrections through this skill's skill-generation path as pre-ratified proposals (the human already authored them - don't wait for backtest misses).
generated/ namespaces in the client's own repo - never the public openfpa template.pyfpa.Experiment files in
.fpa/experiments/; preserve rejected and reverted experiments.After architecture approval, use fpa-scaffold-model to build the runnable model. Consult fpa-cfo-judgment throughout.