ruvnet/symbolic-scribe

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Dernière activité source enregistrée: 14 juin 2026 à 23:49

Catalogue SkillsMP mis à jour: 26 octobre 2026 à 11:37

skills collectés: 1

Étoiles GitHub: 88
Forks GitHub: 24

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# Symbolic Scribe A specialized prompt engineering tool that uses mathematical frameworks to generate precise, structured prompts for AI interactions. **▶ Live web UI: [ruvnet.github.io/symbolic-scribe](https://ruvnet.github.io/symbolic-scribe/)** [![Symbolic Scribe — PromptOps Compiler optimizing a prompt](docs/optimizer-screenshot.png)](https://ruvnet.github.io/symbolic-scribe/) ## PromptOps Compiler (Rust → WASM) Symbolic Scribe is also a **compiler for prompts**. The `/optimize` page takes a loose natural-language prompt and compiles it — entirely in your browser — into a structured, scored, compressed, risk-checked, and witness-checksummed artifact. The deterministic core is a Rust crate compiled to WebAssembly (`wasm/prompt-forge`, exposed via `src/services/promptForge.ts`): ``` raw prompt → AST + intent + constraints → SynthLang compression → safety / ambiguity / schema lint → symbolic-form compile → 7-objective score + Pareto frontier → risk firewall → SHA-256 + HMAC witness receipt ``` - **Sub-millisecond, deterministic, offline** — runs on every keystroke, no prompt text leaves the browser for the core analysis. - **Multi-objective optimization** with a hard rule: a prompt is only "improved" if it beats baseline **without lowering accuracy, safety, or schema validity**. - **Live eval matrix** (`src/services/promptEval.ts`) closes the loop: it runs the optimizer's Pareto-frontier candidates against real OpenRouter models over an editable test suite, grades outputs (schema validity, assertions, refusals, length), and **replaces the static accuracy/schema/cross-model-stability proxies with measured numbers** — then re-ranks with the same weights. The model call is injectable, so grading/aggregation is unit-tested offline. - **Drift report** proves numbers/entities/constraints survive compression. - **Prompt firewall** classifies injection / secret-exposure / tool-abuse risk and returns an allow / log / approve / block decision. - **Witness receipts** make every winning prompt auditable: each carries a content-addressed SHA-256 bundle hash plus an HMAC witness. Because the HMAC key ships in the client bundle, this is a **tamper-evident integrity checksum**, not an authenticity signature (a server-side / asymmetric witness chain is future work). - **Learning memory** (`src/services/promptMemory.ts`) records every outcome and recalls similar prior cases, feeding `prior_failure_similarity` back into the firewall — so prompts resembling past attacks score higher risk over time. The similarity backend is swappable: a deterministic local embedder + cosine works offline by default, and [`ruvnet/RuVector`](https://github.com/ruvnet/RuVector) is **wired behind a feature flag** — `ruvector-wasm` (`VectorDB`) for recall and `@ruvector/ruvllm-wasm` (`HnswRouterWasm`) to back the model `RouteHint`. Toggle "RuVector: on" on `/optimize`. See [`docs/ruvector-integration.md`](docs/ruvector-integration.md). Build & test the core: ```bash npm run build:wasm # compile Rust → src/wasm/pkg (needs rustup + wasm-bindgen 0.2.100) npm run test:wasm # 79 native unit tests npm run bench:wasm # latency benchmark ``` The generated `src/wasm/pkg/` is committed, so `npm run build` works without a Rust toolchain. See `wasm/prompt-forge/README.md` and `docs/adr/ADR-001-promptops-compiler.md` for details. ### Performance The compiler is built to run on every keystroke. Native `--release` latency (`npm run bench:wasm`) on the hot paths: | Operation | Latency | Throughput | |-----------|--------:|-----------:| | `count_tokens` (medium) | ~230 ns | ~4.3 M/s | | `analyze` (medium) | ~36 µs | ~28 K/s | | `compress` (medium) | ~50 µs | ~20 K/s | | `optimize` (medium) | ~485 µs | ~2.1 K/s | | `optimize` (large, ~2K tok) | ~2.5 ms | ~400/s | Two tuning passes landed here. First, `compress` was changed to lowercase each line **once per pass** instead of once per filler phrase (**5.4×** `compress`). Second, `optimize_internal` now analyzes each distinct text **exactly once** instead of re-analyzing `raw` and re-parsing the compressed text (**1.9×** `optimize(large)`). Both keep output byte-identical. The committed `src/wasm/pkg` is rebuilt so the browser app gets these gains; the UI debounces `analyze()` to ~120 ms, leaving ample headroom. ### Live benchmark against OpenRouter `fusion` `npm run bench:fusion` is a headless harness that loads the **real** WASM optimizer under Node, runs its baseline vs. optimized candidates against a live model (default `openrouter/fusion`) over a JSON-extraction corpus, grades with the same checks as the in-app eval matrix, and writes `bench/fusion-proof.json`: ```bash OPENROUTER_API_KEY=sk-or-... npm run bench:fusion # or source the key from GCP Secret Manager: scripts/bench-fusion.sh ``` See `docs/adr/ADR-002-live-fusion-benchmark.md`. ### Agent harness A repo-aware agent harness (maintainer / benchmarker / release / security agents, plus `doctor` / `repo-triage` / `release-check` commands) is published to npm and lives in [`harness/`](harness): ```bash npx symbolic-scribe-harness doctor ``` ## Key Features & Benefits ### Mathematical Framework Integration - **Set Theory Templates**: Model complex relationships and hierarchies - **Category Theory**: Define abstract transformations and mappings - **Abstract Algebra**: Structure group operations and symmetries - **Topology**: Explore continuous transformations and invariants - **Complex Analysis**: Handle multi-dimensional relationships ### Practical Applications - **Information Security**: Model threat vectors and attack surfaces - **Ethical Analysis**: Structure moral frameworks and constraints - **AI Safety**: Define system boundaries and safety properties - **Domain Adaptation**: Apply mathematical rigor to any field ### User Experience - **Interactive Console**: Terminal-style interface with modern aesthetics - **Real-time Preview**: Test prompts with multiple AI models - **Template Library**: Pre-built frameworks for common use cases - **Mobile Responsive**: Full functionality on all device sizes - **Local Storage**: Secure saving of prompts and preferences ## Security Features ### API Key Management - Encrypted local storage of API keys - Optional environment variable configuration - No server-side key storage - Automatic key validation ### Data Privacy - Client-side only processing - No external data transmission except to OpenRouter API - No tracking or analytics - Configurable model selection ## Quick Start 1. **Installation** ```bash git clone https://github.com/yourusername/symbolic-scribe.git cd symbolic-scribe npm install ``` 2. **Configuration** ```bash cp .env.sample .env # Edit .env with your OpenRouter API key ``` 3. **Development** ```bash npm run dev ``` 4. **Production Build** ```bash npm run build npm run preview ``` ## Usage Guide ### Basic Prompt Generation 1. Select a mathematical framework template 2. Choose your target domain 3. Define your variables and relationships 4. Generate structured prompts ### Template Customization 1. Navigate to Templates page 2. Select a base template 3. Modify variables and relationships 4. Save for future use ### Testing & Iteration 1. Use the Preview function to test prompts 2. Select different models for comparison 3. Refine based on responses 4. Export final versions ## InfoSec Overview ### Threat Model - Client-side only architecture - No persistent server storage - Encrypted API key storage - Input sanitization ### Best Practices - Regular API key rotation - Use environment variables in production - Monitor API usage - Review generated prompts for sensitive data ## Contributing We welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) for details. ### Development Setup 1. Fork the repository 2. Create a feature branch 3. Install dependencies 4. Make your changes 5. Run tests 6. Submit a PR ## Support - Documentation: `/docs` page in app - Issues: GitHub issue tracker - Community: Discord server (coming soon) ## License MIT License - see LICENSE file for details ## Acknowledgments - OpenRouter for AI model access - shadcn/ui for component library - Tailwind CSS for styling - Vite for build tooling

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Search and inspect the harness memory namespace (HNSW + emergent-time decay).

Langue du texte source : anglais

2026-06-14

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