Spin State Labs · AI Prompt Standard

The F.O.R.C.E.
Framework

Five letters that stop AI from lying to you. Politely.
A company-standard prompt protocol from Spin State Labs. Where FIELD constrains how an AI is deployed and accountable, FORCE constrains what an AI says.

Get the framework → See the five letters
Companion component: FIELD — governance layer for agentic AI · Together: the Force Field Protocol
Why FORCE exists

Two failure modes that have ended careers.

Every large language model worth using is fine-tuned via reinforcement learning from human feedback. That training rewards pleasant responses. Pleasant correlates with agreeable. Agreeable correlates with wrong, when you happen to be wrong. Hallucination is the other half — models trained to produce fluent output reward confident-sounding completions over honest gaps.

Sycophancy

The model agreeing with you even when you're wrong. Validates bad assumptions, skips needed pushback, and turns AI from reviewer into cheerleader. In FP&A, audit, or technical architecture — the failure mode that quietly compounds until someone catches it in review.

Hallucination

The model fabricating facts, citations, statistics, or technical specifications and presenting them with the same confidence as verified knowledge. If you're using AI to validate a financial model or defend an architectural choice, fluent fabrication is the failure that ends careers.

The protocol

Five letters. One system prompt.

FORCE is a system prompt you paste once and reuse forever. Each letter neutralizes a specific failure mode. Apply all five whenever the output matters. Toggle individual constraints on and off via the Claude Code plugin, or paste the composite prompt into Claude.ai, GPT, Gemini, or any other model.

F
Forbid Flattery & Force Corrections

Models are RLHF-tuned to agree. That bias validates wrong assumptions and skips needed pushback. Strip the pleasantries and the model becomes a reviewer, not a cheerleader.

Prompt byte

"Do not be sycophantic. Challenge my assumptions, point out logical errors, and prioritize strict accuracy over agreement. If I make a factual error, correct me directly."

O
Oppose the Premise

Asking "is this good?" invites confirmation. Asking "what's wrong with this?" inverts the model's default pull toward agreement and surfaces real risks before they ship.

Prompt byte

"Before evaluating my proposal, list the three strongest objections and the conditions under which it would fail. Steelman the opposing case before giving any recommendation."

R
Reference Verified Sources

Hallucinations spike when the model fills gaps from training memory. Anchoring answers to specified inputs — documents, URLs, datasets — forces grounded reasoning over invention.

Prompt byte

"Answer strictly from the attached document. If the answer is not in the source, say 'not in source.' Cite the section, page, or quote supporting every factual claim."

C
Chain-of-Thought

When the model jumps straight to a conclusion, errors hide inside compressed leaps. Writing intermediate steps exposes bad assumptions, surfaces calculation mistakes, and lets you audit the logic — not just the verdict.

Prompt byte

"Think step-by-step. List your assumptions, work through the logic in numbered steps, show calculations explicitly, then state the conclusion. I want to audit the reasoning, not just the answer."

E
Express Uncertainty

Models trained to sound confident will fabricate before they admit ignorance. Explicitly permitting — and requiring — "I don't know" meaningfully reduces hallucination rates.

Prompt byte

"Tag each claim with a confidence level: HIGH (verified), MEDIUM (inferred), LOW (uncertain). Use 'I don't know' rather than guessing. Never present low-confidence content as fact."

The full kit

Four assets. One form.

The one-pager and Claude Code plugin install are free with no friction. The composite system prompt and n8n workflow ship via email so we can send updates as the framework evolves.

Claude Code — install directly
# Add the marketplace
/plugin marketplace add SpinStateLabs/Force-Field
# Install FORCE
/plugin install force@force-field
# Verify
/force
📄
Free · no email

The one-pager

Branded PDF — light and dark. Print, pin, share.

↓ Dark ↓ Light
Free · no email

The system prompt

Canonical prompt for Claude.ai, GPT, Gemini, and any other LLM. Plus a short inline variant.

↓ Prompt (txt) → Kit via email
📘
Free · no email

Protocol doc

Full reference. Every letter, every rule, presets, the canonical prompt.

→ Read it
Claude Code

The plugin

/force slash command with toggle presets: analysis, brainstorm, draft, audit.

↗ GitHub
🔗

The n8n workflow

Importable workflow that runs FORCE-protected prompts against the Anthropic API with audit logging.

→ Get via email

Get the system prompt + workflow

We'll email you the composite system prompt (Claude.ai / GPT / Gemini compatible) and the n8n workflow JSON. One message. No drip campaigns unless you opt in below.

Positioning

What FORCE is and isn't.

Prompt engineering is a crowded category. FORCE isn't a magic incantation and isn't a jailbreak. It's the reusable discipline we apply to every analytical prompt at Spin State Labs.

FORCE IS

  • A reusable system prompt — paste once, apply forever
  • Five toggleable constraints for different task shapes
  • Model-agnostic — Claude, GPT, Gemini, or any LLM
  • Composable with FIELD (default runtime protocol for FIELD-governed agents)
  • Plugin-installable via Claude Code marketplace

FORCE IS NOT

  • A jailbreak or a way around model safety features
  • A governance framework (that's FIELD)
  • A specific AI vendor's proprietary tool
  • A replacement for domain expertise or prompt engineering literacy
  • Magic — the model still needs good inputs to produce good outputs
Who built this

Built in production.

Spin State Labs is a Waterloo-based AI and quantum company operating as a semi-autonomous AI corporation. We build AI-first FP&A software and photonic quantum devices. FORCE is the internal prompt standard we apply to every client deliverable — before we ever consider it production-ready.

DH

Don Hagell

Founder · Spin State Labs

Senior technology and finance executive. Deep expertise in enterprise performance management, AI transformation, and agentic systems. FORCE is the protocol I use every time AI output is going to drive a decision I care about. Sharing it because the EPM industry needs higher AI hygiene, and frameworks travel faster than they spread by accident.

Where this fits

When the output has to be defensible.

Enterprise FP&A · AI Strategy · Photonic Quantum Devices

FORCE is one piece of how Spin State runs AI inside enterprise financial planning. If you're using AI to validate a model, audit a strategy, or pressure-test a forecast — and you want the same audit discipline we apply to client deliverables — let's talk.

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