Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add JeffBrines/openfpa --skill fpa-learn-businessgit clone --depth 1 https://github.com/JeffBrines/openfpaWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/jeffbrines/openfpa/fpa-learn-business)<a href="https://agentmods.dev/skills/jeffbrines/openfpa/fpa-learn-business"><img src="https://agentmods.dev/badge/skills/jeffbrines/openfpa/fpa-learn-business/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/jeffbrines/openfpa/fpa-learn-business"><img src="https://agentmods.dev/badge/skills/jeffbrines/openfpa/fpa-learn-business.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00056 | $0.01088 |
| Opus 5 | $0.00028 | $0.00544 |
| Sonnet 5 | $0.00011 | $0.00218 |
| Haiku 4.5 | $0.00006 | $0.00109 |
Grade A, and why
fpa-learn-business scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 87 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Learn the Business (Phase 0)
Overview
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.
When to use
- A new company is being onboarded into openfpa
- You're asked to "build us a model" / "understand our business" before forecasting
- An existing
.fpa/business-profile.mdis missing or stale
Do not force this workflow when the user asks for a narrow task that can be completed without understanding the whole company.
Workflow
-
Check and initialize the workspace. Run
openfpa status <company-root>. If it is uninitialized, runopenfpa init <company-root> --business-name "<name>". Then runopenfpa doctor <company-root>. The CLI emits JSON. If the console script is unavailable in a source checkout, usepython3 -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 withopenfpa 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, callopenfpa intake-recordwith--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.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 87 lines · 56 tokens per session scan A df82a7540d8a
fpa-learn-business is a skill published in the GitHub repository JeffBrines/openfpa (6 stars, last pushed 2mo ago), licensed MIT. It adds 56 tokens to every session and 1,088 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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