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Merge pull request #16 from SebaSOFT/growth/njs-growth-07-trim-customer-proof-docs
Trim customer docs to user-facing proof; relocate production docs to the wiki
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README.md

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@@ -59,10 +59,9 @@ Measured throughput, cold-start, bundle-size, validation, and explanation overhe
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### Methodology and inspectability
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- Benchmark methodology and result contract: [`docs/benchmarks/methodology.md`](docs/benchmarks/methodology.md)
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- Explainability proof metadata and alt text: [`docs/benchmarks/assets/generated/explainability-trace-diagram.md`](docs/benchmarks/assets/generated/explainability-trace-diagram.md)
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- Visual proof system and publication guardrails: [`docs/benchmarks/visual-proof-system.md`](docs/benchmarks/visual-proof-system.md)
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- Playground README demo capture spec: [`docs/playground/readme-demo-capture.md`](docs/playground/readme-demo-capture.md)
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- Benchmark methodology (how each metric is measured): [`docs/benchmarks/methodology.md`](docs/benchmarks/methodology.md)
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- AI-rule safety (why AI-drafted rules need validation): [`docs/benchmarks/ai-rule-safety.md`](docs/benchmarks/ai-rule-safety.md)
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- Proof overview and explainability diagram: [`docs/benchmarks/`](docs/benchmarks/)
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- Runnable examples: [`examples/`](examples/) including n8n and LangGraph workflow automation recipes
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- JSON Schemas, validation, and explain output: [`docs/schemas-validation-explainability.md`](docs/schemas-validation-explainability.md)
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- Benchmark methodology, visual proof, and playground capture specifications: [`docs/benchmarks/`](docs/benchmarks/) and [`docs/playground/readme-demo-capture.md`](docs/playground/readme-demo-capture.md)
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- Measured benchmarks, methodology, and AI-rule-safety proof: [`docs/benchmarks/`](docs/benchmarks/)
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- Comparison and migration guides: [`docs/comparisons/`](docs/comparisons/) for choosing and migrating from json-rules-engine, JsonLogic, node-rules, and hand-written if/else
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- AI-readable docs: [`docs/ai-coding-assistants.md`](docs/ai-coding-assistants.md), [`docs/public/llms.txt`](docs/public/llms.txt), [`docs/public/llms-full.txt`](docs/public/llms-full.txt), and [`docs/public/skills/neuron-js/SKILL.md`](docs/public/skills/neuron-js/SKILL.md)
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docs/.vitepress/config.ts

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]
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},
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{
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text: 'Proof Assets',
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text: 'Benchmarks & Proof',
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items: [
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{ text: 'Benchmarks & Visual Proof', link: '/benchmarks/' },
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{ text: 'Overview', link: '/benchmarks/' },
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{ text: 'Benchmark Results', link: '/benchmarks/results' },
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{ text: 'Benchmark Methodology', link: '/benchmarks/methodology' },
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{ text: 'Visual Proof System', link: '/benchmarks/visual-proof-system' },
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{ text: 'Benchmark Visual Pack', link: '/benchmarks/benchmark-visual-pack' },
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{ text: 'Playground Capture Spec', link: '/playground/readme-demo-capture' },
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{ text: 'Prompt Kit', link: '/benchmarks/prompt-kit' },
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{ text: 'Asset Folder', link: '/benchmarks/assets/' }
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{ text: 'Methodology', link: '/benchmarks/methodology' },
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{ text: 'AI-Rule Safety', link: '/benchmarks/ai-rule-safety' }
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]
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},
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{

docs/benchmarks/ai-rule-safety.md

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# AI-rule safety
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LLMs are great at *drafting* JSON business rules — and that's exactly the risk. A
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plausible-looking rule can still encode the wrong field, the wrong threshold, or the wrong
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action. Neuron-JS exists so AI-drafted rules become **safe to run**: schema-validated,
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constrained to a developer-owned vocabulary, reviewable like code, and explainable after the
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fact.
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> AI drafts. Neuron-JS verifies.
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## 1. AI can draft rules
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A plausible JSON rule can still encode the wrong assumption, field, or action — it is not
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production-ready on its own.
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![A developer inspects an amber LLM-generated JSON rule card. A small rose warning marker highlights a risky business assumption, with text saying the draft is not production-ready.](./assets/generated/ai-rule-safety-carousel-1.svg)
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## 2. Validate before runtime
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Schema-first checks (`validateScript`) reject malformed scripts before they ever execute, and
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return the exact JSON path to fix.
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![A JSON rule passes through a schema validation gate. One invalid path is blocked in rose with an exact JSON path, while a corrected rule exits with an emerald pass marker.](./assets/generated/ai-rule-safety-carousel-2.svg)
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## 3. Constrain what can run
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A developer-owned registry defines the approved actions, conditions, parameters, and rules.
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Anything outside that vocabulary simply cannot execute — no arbitrary code.
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![A validated rule enters a developer-owned registry boundary. Approved action and condition tiles connect in cyan, while an unapproved action is blocked outside the boundary.](./assets/generated/ai-rule-safety-carousel-3.svg)
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## 4. Review like code
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Generated rules are serializable data, so they go through the same governance as code: tests,
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owner approval, and rollback — never automatic AI approval.
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![A generated rule card sits beside a review checklist showing tests, owner approval, and rollback snapshot. The visual emphasizes governance before production use.](./assets/generated/ai-rule-safety-carousel-4.svg)
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## 5. Then execute deterministically
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Synapse runs the approved rule path deterministically, and the explanation trace shows why the
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decision matched or failed — ready for audit, logs, or a support ticket.
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![An approved JSON rule flows into a Synapse execution node. A result card and violet explanation trace rows show why the rule matched and what action ran, with the caption "AI drafts. Neuron-JS verifies."](./assets/generated/ai-rule-safety-carousel-5.svg)
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## In short
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`Validate → constrain → test → approve → execute → explain.` Deterministic workflow logic with
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auditability is the guardrail that makes AI-assisted business rules safe to ship.

docs/benchmarks/assets/README.md

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