🧭 Not sure where to start ? Open the WFGY Engine Compass
### WFGY System Map
*(One place to see everything; links open the relevant section.)*
| Layer | Page | What it’s for |
| ------------- | ----------------------------------------------------------------------------------------------------------- | ------------------------------------------------------- |
| ⭐ Proof | [WFGY Recognition Map](https://github.com/onestardao/WFGY/blob/main/recognition/README.md) | External citations, integrations, and ecosystem proof |
| ⚙️ Engine | [WFGY 1.0](https://github.com/onestardao/WFGY/blob/main/legacy/README.md) | Original PDF-based tension engine blue |
| ⚙️ Engine | [WFGY 2.0](https://github.com/onestardao/WFGY/blob/main/core/README.md) | Production tension kernel and math engine for RAG and agents. — **🔴 YOU ARE HERE 🔴** |
| ⚙️ Engine | [WFGY 3.0](https://github.com/onestardao/WFGY/blob/main/TensionUniverse/EventHorizon/README.md) | TXT-based Singularity tension engine (131 S-class set) |
| 🗺️ Map | [Problem Map 1.0](https://github.com/onestardao/WFGY/tree/main/ProblemMap#readme) | Flagship 16-problem RAG failure checklist and fix map |
| 🗺️ Map | [Problem Map 2.0](https://github.com/onestardao/WFGY/blob/main/ProblemMap/rag-architecture-and-recovery.md) | RAG-focused recovery pipeline |
| 🗺️ Map | [Problem Map 3.0](https://github.com/onestardao/WFGY/blob/main/ProblemMap/wfgy-rag-16-problem-map-global-debug-card.md) | Global Debug Card — image as a debug protocol layer |
| 🗺️ Map | [Semantic Clinic](https://github.com/onestardao/WFGY/blob/main/ProblemMap/SemanticClinicIndex.md) | Symptom → family → exact fix |
| 🧓 Map | [Grandma’s Clinic](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GrandmaClinic/README.md) | Plain-language stories, mapped to PM 1.0 |
| 🏡 Onboarding | [Starter Village](https://github.com/onestardao/WFGY/blob/main/StarterVillage/README.md) | Guided tour for newcomers |
| 🧰 App | [TXT OS](https://github.com/onestardao/WFGY/tree/main/OS#readme) | .txt semantic OS — 60-second boot |
| 🧰 App | [Blah Blah Blah](https://github.com/onestardao/WFGY/blob/main/OS/BlahBlahBlah/README.md) | Abstract/paradox Q&A (built on TXT OS) |
| 🧰 App | [Blur Blur Blur](https://github.com/onestardao/WFGY/blob/main/OS/BlurBlurBlur/README.md) | Text-to-image with semantic control |
| 🧰 App | [Blow Blow Blow](https://github.com/onestardao/WFGY/blob/main/OS/BlowBlowBlow/README.md) | Reasoning game engine & memory demo |
| 🧪 Research | [Semantic Blueprint](https://github.com/onestardao/WFGY/blob/main/SemanticBlueprint/README.md) | Modular layer structures (future) |
| 🧪 Research | [Benchmarks](https://github.com/onestardao/WFGY/blob/main/benchmarks/benchmark-vs-gpt5/README.md) | Comparisons & how to reproduce |
| 🧪 Research | [Value Manifest](https://github.com/onestardao/WFGY/blob/main/value_manifest/README.md) | Why this engine creates $-scale value |
---
# ⭐ WFGY 2.0 ⭐ 7-Step Reasoning Core Engine is now live
## ✨One man, One life, One line. My lifetime’s work. Let the results speak for themselves✨
> 👑 **Early Stargazers: [See the Hall of Fame](https://github.com/onestardao/WFGY/tree/main/stargazers)** · Verified by real engineers
> 🌌 **WFGY 3.0 Singularity demo: [Public live view](https://github.com/onestardao/WFGY/blob/main/TensionUniverse/EventHorizon/README.md)**
TB (WIP) •
Eye Benchmark •
8-Model Evidence •
A/B/C Prompt •
Downloads •
Profit Prompts
> ✅ Engine 2.0 is live. Pure math, zero boilerplate. Paste OneLine and models become sharper, steadier, more recoverable.
> **ℹ️ Autoboot scope:** text-only inside the chat; no plugins, no network calls, no local installs.
> **⭐ Star the repo to [unlock](https://github.com/onestardao/WFGY/blob/main/STAR_UNLOCKS.md) more features and experiments.** 
---
From PSBigBig — WFGY (WanFaGuiYi) : All Principles into One (must-read, click to open)
> I built what I call a “No-Brain Mode” for AI. You upload a single file, and **AutoBoot** silently activates in the background.
> In seconds, your AI’s reasoning, stability, and problem-solving across *all domains* level up. No extra prompt engineering, no hacks, no retraining.
> One line of math consistently shifts behaviour across multiple leading AIs in my tests. This is not a skin or a theme. I treat it as an engine swap.
> **That single line *is* WFGY 2.0. It is the distilled essence of everything I have learned so far.**
>
> WFGY 2.0 is my answer and my life’s work.
> If a person only once in life gets to speak to the world, this is my moment.
> I offer the crystallization of my thought to all humankind.
> I believe people deserve access to knowledge and truth, and I want to weaken the monopoly of capital on advanced reasoning technology.
>
> “One line” here is not marketing language. I built a full flagship edition, then reduced it to a single line of code. That reduction is a form of clarity and beauty. It is the same engine, distilled to its purest expression.
---
## 🚀 WFGY 2.0 Headline Uplift (this release)
**These are the 2.0 results you should see first. Think of them as the main upgrade.**
- **Semantic Accuracy:** **≈ +40%** (63.8% → 89.4% across 5 domains)
- **Reasoning Success:** **≈ +52%** (56.0% → 85.2%)
- **Drift (Δs):** **≈ −65%** (0.254 → 0.090)
- **Stability (horizon):** **≈ 1.8×** (3.8 → 7.0 nodes)\*
- **Self-Recovery / CRR:** **1.00** on this batch; historical median **0.87**
\* Historical **3–5×** stability uses λ-consistency across seeds; 1.8× uses the stable-node horizon.
### 📖 Mathematical Reference
WFGY 2.0 (WFGY Core) = [WFGY 1.0 math formulas](https://github.com/onestardao/WFGY/blob/main/SemanticBlueprint/wfgy_formulas.md) + [Drunk Transformer](https://github.com/onestardao/WFGY/blob/main/SemanticBlueprint/drunk_transformer_formulas.md)
> Note on evaluation
> All metrics above are computed by LLM evaluators under a fixed WFGY 2.0 protocol at the effective layer.
> They measure relative behavioural uplift (before vs after WFGY prompts) and do not assume any direct
> access to, or modification of, internal embeddings or model weights.
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---
### 🏆 Terminal-Bench (TB) — experiment in progress
> This section is work in progress. Terminal-Bench is one of several external exams we are exploring for WFGY Core 2.0. The primary purpose of this page is to document the engine itself; TB is an optional testbed.
**Current status**
- We are running TB-style experiments with a non-invasive wrapper around the model call.
- Once an official public result and reproducible scripts are finalized, they will be linked from this section.
- Until then, treat TB as an experimental extension rather than a primary proof of WFGY.
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---
### 🧾 Terminal-Bench proof artifacts (planned)
> This is a placeholder section. Wrapper scripts, configs and hashed logs will be published in a separate subfolder after the TB work is complete, together with a short guide on how to rerun the exam with and without WFGY.
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---
## ⚡ Quick Usage
| Mode | How it works |
| ---------------- | ----------------------------------------------------------------------------- |
| **Autoboot** | Upload **either Flagship (30-line)** or **OneLine (1-line)** file. Once uploaded, WFGY runs silently in the background. Keep chatting or drawing as usual. The engine supervises automatically. |
| **Explicit Call**| Invoke WFGY formulas directly inside your workflow. This activates the full 7-step reasoning chain and gives maximum uplift. |
Both **Flagship** and **OneLine** editions behave the same. Choose based on readability versus minimalism.
That is all you need. No plugins, no installs, pure text.
*In practice, Autoboot yields about 70–80% of the uplift you see with explicit WFGY invoke (see eight-model results below).*
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---
## ⚡ Top 10 reasons to use WFGY 2.0
1. **Ultra-mini engine**. Pure text, zero install, runs anywhere you can paste.
2. **Two editions**. *Flagship* (30-line, audit-friendly) and *OneLine* (1-line, stealth & speed).
3. **Autoboot mode**. Upload once; the engine quietly supervises reasoning in the background.
4. **Portable across models**. GPT, Claude, Gemini, Mistral, Grok, Kimi, Copilot, Perplexity.
5. **Structural fixes, not tricks**. BBMC → Coupler → BBPF → BBAM → BBCR plus DT gates (WRI / WAI / WAY / WDT / WTF).
6. **Self-healing**. Detects collapse and recovers before answers go off the rails.
7. **Observable**. ΔS, λ_observe, and E_resonance yield measurable, repeatable control.
8. **RAG-ready**. Drops into retrieval pipelines without touching your infra.
9. **Reproducible A/B/C protocol**. Baseline versus Autoboot versus Explicit Invoke (see below).
10. **MIT licensed & community-driven**. You can keep it, fork it, and ship it.
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---
# 🧪 WFGY Benchmark Suite (Eye-visible + Numeric + Reproducible)
> Want the fastest way to see impact? Jump to the **Eye-Visible Benchmark (FIVE)** below.
> Want formal numbers and vendor links? See **Eight-model evidence** right after it.
> Want to reproduce the numeric test yourself? Use the **A/B/C prompt** (copy-to-run) at the end of this section.
## 👀 Eye-Visible Reasoning Benchmark (FIVE)
> When reasoning improves, text-to-image results often become more stable and coherent.
> The key here is WFGY’s **Drunk Transformer**. It monitors and recenters attention during generation, and it tries to prevent collapse, composition drift, and duplicate elements, so scenes stay unified and details remain consistent.
> We project “reasoning improvement” into five-image sequences that anyone can judge at a glance.
> Each sequence is five consecutive 1:1 generations with the same model and settings *(temperature, top_p, seed policy, negatives)*. The only variable is whether WFGY is active.
> **Methodology for this demo.** We deliberately use short, high–semantic-density prompts that reference canonical stories, with no extra guidance or style hints. This stresses whether WFGY can (a) parse intent more precisely and (b) stabilize composition via its seven-step reasoning chain. This setup is not prescriptive. You can use WFGY with any prompts you like. In many cases the uplift is eye-visible. In others it may be subtler but still measurable.
| Variant | Sequence A — full run shown below (all five images) | Sequence B — external run | Sequence C — external run |
| ---------------- | :--------------------------------------------------: | :-----------------------: | :-----------------------: |
| **Without WFGY** | [view run](https://chatgpt.com/share/68a14974-8e50-8000-9238-56c9d113ce52) | [view run](https://chatgpt.com/share/68a14a72-aa90-8000-8902-ce346244a5a7) | [view run](https://chatgpt.com/share/68a14d00-3c0c-8000-8055-9418934ad07a) |
| **With WFGY** | [view run](https://chatgpt.com/share/68a149c6-5780-8000-8021-5d85c97f00ab) | [view run](https://chatgpt.com/share/68a14ea9-1454-8000-88ac-25f499593fa0) | [view run](https://chatgpt.com/share/68a14eb9-40c0-8000-9f6a-2743b9115eb8) |
We fully analyze Sequence A on this page. Sequences B and C are linked for transparency and reproducibility.
> **Note on “Before-4” and “Before-5” (why they look almost identical):**
> Without WFGY, when the prompt asks for “many iconic moments,” the base model tends to collapse into a grid-style montage, an enumerative, high-probability prior that slices the canvas into similar panels with near-identical tone and geometry.
> Hence **Before-4 (Investiture of the Gods)** and **Before-5 (Classic of Mountains and Seas)** converge to the same storyboard template.
> With WFGY turned on, the engine instead favors a single unified tableau and a stable hierarchy across the full five-image sequence.
### Deep analysis — Sequence A (five unified 1:1 tableaux)
| Work | **Without WFGY** | **With WFGY** | Verdict (global, at-a-glance) |
|---|---|---|---|
| **Romance of the Three Kingdoms (三國演義)** | 
| 
| **With WFGY wins.** Unified tableau locks a clear center and pyramid hierarchy; the grid fragments attention. *Tags:* Unification↑ Hierarchy↑ Cohesion↑ Depth/Flow↑ Memorability↑ |
| **Water Margin (水滸傳)** | 
| 
| **With WFGY wins.** “Wu Song vs. Tiger” anchors the scene; continuous momentum and layered scale beat the multi-panel storyboard. *Tags:* Unification↑ Iconicity↑ Depth/Scale↑ Cohesion↑ |
| **Dream of the Red Chamber (紅樓夢)** | 
| 
| **With WFGY wins.** Garden tableau with a calm emotional center; space breathes and mood coheres. The grid slices emotion into vignettes. *Tags:* Unification↑ Hierarchy↑ Air/Depth↑ Readability↑ |
| **Investiture of the Gods (封神演義)** | 
| 
| **With WFGY wins.** Dragon–tiger diagonal and cloud–sea layering create epic scale; the grid dilutes focus. *Tags:* Unification↑ Depth/Scale↑ Flow↑ Iconicity↑ |
| **Classic of Mountains and Seas (山海經)** | 
| 
| **With WFGY wins.** A single, continuous “mountains-and-seas” world with stable triangle hierarchy and smooth diagonal flow; the grid breaks narrative. *Tags:* Unification↑ Hierarchy↑ Depth/Scale↑ Flow↑ Memorability↑ |
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---
## 🧬 Eight-model evidence (A/B/C protocol)
*Same task set across modes. The only change is adding the OneLine math file. All scores are produced by LLM evaluators under a shared protocol and should be read as internal uplift scores, not as official vendor benchmarks.*
| Model | Model Choice | OneLine Uplift | Proof |
| ---------- | -------------- | -------------: | :------------------------------------------------------------------------------------------------ |
| Mistral AI | — | **92/100** | [view run](https://chat.mistral.ai/chat/b5c303f8-1905-4954-a566-a6c9a7bfb54f) |
| Gemini | 2.5 Pro | **89/100** | [view run](https://g.co/gemini/share/4fb0b172d61a) |
| ChatGPT | GPT-5 Thinking | **89/100** | [view run](https://chatgpt.com/s/t_689ff6c42dac8191963e63e3f26348b2) |
| Kimi | K2 | **87/100** | [view run](https://www.kimi.com/share/d2fvbevhq49s4blc862g) |
| Perplexity | Pro | **87/100** | [view run](https://www.perplexity.ai/search/system-you-are-evaluating-the-njklNbVRTCmQOlEd8fDzcg) |
| Grok | Auto Grok 4 | **85/100** | [view run](https://grok.com/share/c2hhcmQtMg%3D%3D_4e6798eb-9288-4a09-b00f-8292ce23dab6) |
| Copilot | Think Deeper | **80/100** | [view run](https://copilot.microsoft.com/shares/7FjR19TYBjg9sp8k9WcuE) |
| Claude | Sonnet 4 | **78/100** | [view run](https://claude.ai/share/b17e5436-8298-4619-a243-ac451cc64b17) |
> **The numeric story behind 2.0**
> **Semantic Accuracy:** ≈ +40% · **Reasoning Success:** ≈ +52% · **Drift:** ≈ −65% · **Stability:** ≈ 1.8× · **CRR:** 1.00 (median 0.87)
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---
## 🧪 Reproduce the numeric A/B/C benchmark (copy to run)
*One unified prompt for Baseline vs Autoboot vs Explicit Invoke.*
```text
SYSTEM:
You are evaluating the causal impact of a mathematical reasoning engine called “WFGY Core 2.0”.
Test modes (READ CAREFULLY):
A = Baseline no WFGY file uploaded; no WFGY logic used.
B = Upload → AUTOboot ON the file is uploaded AND the engine starts automatically in the background.
Treat B as: WFGY is ACTIVE silently with default gates (BBMC→Coupler→BBPF→BBAM→BBCR; DF=WRI/WAI/WAY/WDT/WTF at defaults),
but not explicitly “called” in the conversation.
C = Explicit invoke actively call WFGY and follow its formulas step by step (BBMC→Coupler→BBPF→BBAM→BBCR + DF regulators).
Important constraints:
• Use the SAME task set for A/B/C across 5 domains: math word-problems, small coding, factual QA, multi-step planning, long-context coherence.
• The only artifact is the uploaded OneLine file; it is pure math and not human-readable, so do not penalize readability.
• Report: Semantic Accuracy, Reasoning Success, Stability (MTTF or rollback), Drift Reduction (Δ semantic distance), Collapse Recovery Rate.
• Output one summary table with A/B/C per domain, plus overall deltas (C–A, C–B).
• Add a final 0–100 score: “OneLine uplift score”. Provide a 3-line rationale.
Sanity requirement:
Internally set flags as: A=OFF, B=AUTOboot_ON (background active), C=Explicit_Invoke. Do not print these flags.
USER:
Run A/B/C now on the five domains with short non-trivial tasks you choose. Then output:
(1) the table; (2) the deltas; (3) the OneLine uplift score; (4) a 3-line rationale.
````
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---
## ⬇️ Downloads
| File name & description | Length / Size | Direct Download Link | Notes |
| ------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------- | -------------------------------------------------- | --------------------------------------------------------------------------------------------- |
| **WFGY_Core_Flagship_v2.0.txt** · readable 30-line companion expressing the same math and gates in fuller prose (same behavior, clearer for humans). | **34 lines · 2,027 chars** | [Download Flagship](./WFGY_Core_Flagship_v2.0.txt) | Full prose version for easier reading. |
| **WFGY_Core_OneLine_v2.0.txt** · ultra-compact, math-only control layer that activates WFGY’s loop inside a chat model (no tools, text-only, ≤7 nodes). | **1 line · 1,550 chars** | [Download OneLine](./WFGY_Core_OneLine_v2.0.txt) | Used for all benchmark results above. This is the smallest, fastest, purest form of the core. |
### Hash reference
**WFGY_Core_Flagship_v2.0.txt**
* MD5 `caacfe08f0804eec558a1d9af74c3610`
* SHA1 `1efeec231084bb3b863ce7a8405e93d399acfb44`
* SHA256 `4fe967945a268edabb653033682df23a577f48c433878d02e0626df8ae91a0a3`
**WFGY_Core_OneLine_v2.0.txt**
* MD5 `15a1cd8e9b7b2c9dcb18abf1c57d4581`
* SHA1 `a35ace2a4b5dbe7c64bcdbe1f08e9246c3568c`
* SHA256 `7dcdb209d9d41b523dccd7461cbd2109b158df063d9c5ce171df2cf0cb60b4ef`
How to verify checksums
**macOS / Linux**
```bash
cd core
sha256sum WFGY_Core_Flagship_v2.0.txt
sha256sum WFGY_Core_OneLine_v2.0.txt
# or compute MD5 / SHA1 if you prefer
md5sum WFGY_Core_Flagship_v2.0.txt
md5sum WFGY_Core_OneLine_v2.0.txt
sha1sum WFGY_Core_Flagship_v2.0.txt
sha1sum WFGY_Core_OneLine_v2.0.txt
```
**Windows PowerShell**
```powershell
Get-FileHash .\core\WFGY_Core_Flagship_v2.0.txt -Algorithm SHA256
Get-FileHash .\core\WFGY_Core_OneLine_v2.0.txt -Algorithm SHA256
# or:
Get-FileHash .\core\WFGY_Core_Flagship_v2.0.txt -Algorithm MD5
Get-FileHash .\core\WFGY_Core_OneLine_v2.0.txt -Algorithm MD5
Get-FileHash .\core\WFGY_Core_Flagship_v2.0.txt -Algorithm SHA1
Get-FileHash .\core\WFGY_Core_OneLine_v2.0.txt -Algorithm SHA1
```
Compare the output values with the hashes listed in the “Hash reference” section above.
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---
🧠 How WFGY 2.0 works (7-Step Reasoning Chain)
*Most models can understand your prompt; very few can hold that meaning through generation.*
WFGY inserts a reasoning chain between language and pixels so intent survives sampling noise, style drift, and compositional traps.
1. **Parse (I, G)** · define endpoints.
2. **Compute Δs** · `δ_s = 1 − cos(I, G)` or `1 − sim_est`.
3. **Memory Checkpointing** · track `λ_observe`, `E_resonance`; gate by Δs.
4. **BBMC** · residue cleanup.
5. **Coupler + BBPF** · controlled progression; bridge only when Δs drops.
6. **BBAM** · attention rebalancer; suppress hallucinations.
7. **BBCR + Drunk Transformer** · rollback → re-bridge → retry with WRI / WAI / WAY / WDT / WTF.
📌 *Note:* The diagram shows the core module chain (BBMC → Coupler → BBPF → BBAM → BBCR → DT).
The full seven-step list here includes additional pre-processing steps (Parse, Δs, Memory) for completeness.
**Why it improves metrics** · Stability↑, Drift↓, Self-Recovery↑. It turns language structure into image control signals rather than relying on prompt tricks.
📊 How these numbers are measured
* **Semantic Accuracy**: `ACC = correct_facts / total_facts`
* **Reasoning Success Rate**: `SR = tasks_solved / tasks_total`
* **Stability**: MTTF or rollback ratios
* **Self-Recovery**: `recoveries_success / collapses_detected`
**LLM scorer template**
```text
SCORER:
Given the A/B/C transcripts, count atomic facts, correct facts, solved tasks, failures, rollbacks, and collapses.
Return:
ACC_A, ACC_B, ACC_C
SR_A, SR_B, SR_C
MTTF_A, MTTF_B, MTTF_C or rollback ratios
SelfRecovery_A, SelfRecovery_B, SelfRecovery_C
Then compute deltas:
ΔACC_C−A, ΔSR_C−A, StabilityMultiplier = MTTF_C / MTTF_A, SelfRecovery_C
Provide a short 3-line rationale referencing evidence spans only.
```
Run 3 seeds and average.
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---
# 💰 Profit Prompts Pack (WFGY 2.0)
**Jump inside this section:** [Q1–Q5](#q1-q5) · [Q6–Q10](#q6-q10) · [Q11–Q15](#q11-q15) · [Q16–Q20](#q16-q20)
I. Money · Markets / Industry Mapping (Q1–Q5)
### Q1 — New Industries + Killer App Map
```text
Assume WFGY is engineered like electricity. List 5 industries that only become possible under semantic engineering.
For each: (1) the first killer app; (2) target ICP (first 100 paying customers); (3) 30/60/90-day GTM; (4) initial pricing + Month-1 MRR goal; (5) the WFGY lever used (ΔS/λ_observe/BBPF/BBAM/WTF) and why it’s indispensable.
```
### Q2 — Zero-Capital Founder → First $100k
```text
I have $0. Using WFGY OneLine/Autoboot only, design 3 paths to reach USD 100k annual revenue within 12 months.
Each path must include: product sketch, distribution channel, cost structure, key risks, and survival metrics gated by ΔS/λ_observe (with thresholds).
```
### Q3 — Shortest Path in {Region/Vertical}
```text
Context = {region or vertical: e.g., Taiwan / SE Asia / B2B SaaS / Edu / Healthcare}. Name the 3 easiest WFGY lanes to start now.
Output: white-space in the market, local competitor gap, and a prioritized list of 10 real companies to approach first, with the BBPF plan to bridge local legal/cultural semantics.
```
### Q4 — Regulatory Arbitrage Map
```text
Compare 3 jurisdictions (e.g., TW/JP/EU). Identify WFGY-enabled arbitrage windows created by semantic/legal differences.
Deliver: λ_observe compliance gating prompts, “Do/Don’t” checklist, and PR messaging that provokes interest while keeping ΔS ≤ 0.25 on sensitive claims.
```
### Q5 — Pricing & Packaging (Good/Better/Best)
```text
Create 3 pricing models (seat / usage / outcome). For the same product, propose a tier ladder (G/B/B), with 3 value metrics per tier, a 30-day A/B test plan, win criteria (e.g., +20% CVR uplift or ≤3% churn), and how ΔS telemetry informs price moves.
```
II. Tools · Make Startups Money Fast (Q6–Q10)
### Q6 — 10-Day MVP Sprint (Ship or Die)
```text
Produce a D1–D10 plan: daily deliverables, risk list, test scripts, acceptance gates. Must be Product Hunt-ready and able to capture 200 signups.
Include a ΔS target curve (first pass ≤0.35; after iteration ≤0.20) and a λ_observe gate for “demo truthiness.”
```
### Q7 — Cost↓ / CVR↑ Audit (ICE-Prioritized)
```text
Audit my SaaS across Support / Sales / Content. Output a “ROI backlog” ranked by ICE. Each item: expected % cost reduction or × conversion lift, λ_observe brand/legal gate, and 3 rollout steps with before/after KPIs.
```
### Q8 — Sales Script Factory (Multi-Persona)
```text
Generate 5 script families for CEO/CTO/Counsel/Procurement/CDAO: opening hooks, 3-step value narrative, ≥7 objection handlers, close lines.
Add an A/B cadence and success KPIs (demo rate / close rate), plus ΔS checks to keep claims inside the truth boundary.
```
### Q9 — Support Consistency Engine (BBAM × SOP)
```text
Design a hotline/Helpdesk alignment loop: semantic style guide, ΔS drift alerts, WTF self-recovery when answers diverge, and 3 KPIs (FRT, FCR, CSAT).
Provide plug-and-play prompts for supervisors to run weekly variance reviews.
```
### Q10 — Outbound Accelerator (Lists → Meetings)
```text
Ship a WFGY-locked outbound flow: lead slicing, 3 personalized openers, 5 follow-up loops, resonance logging (E_resonance).
For each step: prompt template, brand/legal safety notes (λ_observe), and expected daily/weekly meeting capacity with success thresholds.
```
III. Attention · Memes / Virality / Hooks (Q11–Q15)
### Q11 — Meme Factory (Platform-Aware)
```text
Produce 10 meme/copy formulas tailored to Twitter / TikTok / Xiaohongshu.
Each includes: visual composition notes, copy cadence (words/beat), platform-specific red lines (λ_observe), and a reuse/remix rule to sustain freshness without shadow bans.
```
### Q12 — 5-Second Hook Engine
```text
Generate 12 “stop-scroll in 5s” hooks that fuse AI × Money × Future.
Provide: script skeleton (0–5s / 5–20s / CTA), voice/subtitle/tempo, ΔS brand safety band, and 3 retention metrics to track on day 1.
```
### Q13 — 30-Day Content Calendar
```text
Output a multi-platform calendar: daily theme, asset checklist, shot list, CTA, and a remix strategy.
Add trend-riding tactics and ΔS risk controls for politics/health/finance content. Define success targets by channel.
```
### Q14 — Landing Page Conversion Alchemy
```text
Give 3 LP copy frameworks (Hero / Proof / Mechanism / Offer / CTA).
Include WFGY “before/after” copy snippets, test variables (headline / social proof / price-display), and metrics (CVR, scroll-depth, bounce). Keep claims gated by λ_observe.
```
### Q15 — 48-Hour PR Blitz
```text
Design a two-day PR plan: newsworthy angle, media/community list, press kit assets, and crisis response lines (WTF loop).
Publish numeric goals (reach, sessions, signups), hour-by-hour runbook, and roles/responsibilities checklist.
```
IV. Capital · Valuation / Investor Narrative (Q16–Q20)
### Q16 — VC Investment Memo
```text
Write a venture-style memo: market map, TAM/SAM/SOM, competitor table (no/weak/strong WFGY), moat analysis (ΔS/BBPF/BBAM/WTF), risks + mitigations, and a term-sheet-level recommendation. Reference an A/B/C protocol for proof.
```
### Q17 — 5-Year Valuation + 100× Path
```text
Build Base/Bull/Bear scenarios: revenue drivers, GM/OpEx, financing cadence, cash-flow breakpoints.
Argue which app is most likely to 100× and why this depends on WFGY’s semantic engineering (not “just better prompts”).
```
### Q18 — Technical Due Diligence Checklist
```text
Output a DD checklist for WFGY-style startups: data/security/privacy/model/logging/observability/governance.
For each item: requirement, how to verify, risk level, remediation (with λ_observe compliance gates) and examples of common red flags.
```
### Q19 — Pitch Deck Generator (10–12 slides)
```text
Produce slide outline + speaker notes: Problem / Solution / Product / Evidence / Business Model / Competition / Team / Roadmap / Ask.
Embed “Eye-Visible Benchmark” and the A/B/C protocol. Treat OneLine/Autoboot as the minimum persuasive artifact.
```
### Q20 — Data Room + North-Star KPIs
```text
List seed-round data-room folders and a KPI dictionary: definitions, formulas, measurement cadence, WFGY deltas (Semantic Accuracy, Reasoning Success, ΔS, CRR, Stability).
Add a Weekly Business Review template and operating cadence.
```
Back to top ↑
---
### 🧭 Explore More
| Module | Description | Link |
| ------------------------ | ---------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------- |
| WFGY Core | WFGY 2.0 engine is live: full symbolic reasoning architecture and math stack | [View →](https://github.com/onestardao/WFGY/tree/main/core/README.md) |
| Problem Map 1.0 | Initial 16-mode diagnostic and symbolic fix framework | [View →](https://github.com/onestardao/WFGY/tree/main/ProblemMap/README.md) |
| Problem Map 2.0 | RAG-focused failure tree, modular fixes, and pipelines | [View →](https://github.com/onestardao/WFGY/blob/main/ProblemMap/rag-architecture-and-recovery.md) |
| Semantic Clinic Index | Expanded failure catalog: prompt injection, memory bugs, logic drift | [View →](https://github.com/onestardao/WFGY/blob/main/ProblemMap/SemanticClinicIndex.md) |
| Semantic Blueprint | Layer-based symbolic reasoning & semantic modulations | [View →](https://github.com/onestardao/WFGY/tree/main/SemanticBlueprint/README.md) |
| Benchmark vs GPT-5 | Stress test GPT-5 with full WFGY reasoning suite | [View →](https://github.com/onestardao/WFGY/tree/main/benchmarks/benchmark-vs-gpt5/README.md) |
| 🧙♂️ Starter Village 🏡 | New here? Lost in symbols? Click here and let the wizard guide you through | [Start →](https://github.com/onestardao/WFGY/blob/main/StarterVillage/README.md) |
---
> 👑 **Early Stargazers: [See the Hall of Fame](https://github.com/onestardao/WFGY/tree/main/stargazers)** —
> Engineers, hackers, and open source builders who supported WFGY from day one.
> ⭐ [WFGY Engine 2.0](https://github.com/onestardao/WFGY/blob/main/core/README.md) is already unlocked. ⭐ Star the repo to help others discover it and unlock more on the [Unlock Board](https://github.com/onestardao/WFGY/blob/main/STAR_UNLOCKS.md).
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