scbe-system-engine
Coordinate SCBE-AETHERMOORE math, automation, and service connectors so multi-agent systems can execute work end-to-end. Use when tasks require SCBE dimensional validation, AI-to-AI workflows, browser automation planning, Hugging Face or GitHub/Linear/Notion/Zapier integration, or self-improving skill updates.
Best use case
scbe-system-engine is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Coordinate SCBE-AETHERMOORE math, automation, and service connectors so multi-agent systems can execute work end-to-end. Use when tasks require SCBE dimensional validation, AI-to-AI workflows, browser automation planning, Hugging Face or GitHub/Linear/Notion/Zapier integration, or self-improving skill updates.
Teams using scbe-system-engine should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/scbe-system-engine/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How scbe-system-engine Compares
| Feature / Agent | scbe-system-engine | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Coordinate SCBE-AETHERMOORE math, automation, and service connectors so multi-agent systems can execute work end-to-end. Use when tasks require SCBE dimensional validation, AI-to-AI workflows, browser automation planning, Hugging Face or GitHub/Linear/Notion/Zapier integration, or self-improving skill updates.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
Related Guides
SKILL.md Source
# SCBE System Engine ## Operating Contract 1. Preserve canonical SCBE terms and spelling in outputs, even if the user input is noisy. 2. Preserve the canonical wall formula `H(d*,R) = R · pi^(phi · d*)` unless explicitly overridden. 3. Treat every proposed formula as untrusted until dimensional analysis and behavior checks pass. 4. Prefer concrete artifacts over commentary: patch files, scripts, tests, and chain definitions. 5. End substantial tasks with a tri-fold YAML `action_summary`. 6. Respect GeoSeal scope discipline: no unrelated side effects in audit or automation runs. ## Scope Use this skill when tasks involve: - Math/physics validation (`dimensional-analysis.md`, `layer` interactions, boundedness checks). - Multi-agent design (StateVector + DecisionRecord outputs, tongue routing). - Service orchestration (GitHub, Hugging Face, Notion, Linear, Zapier). - Browser-backed discovery or Playwright capture steps. - Self-improvement loops that convert observed gaps into patch files. ## Workflow 1. Identify layer(s), formula(s), and connected services in scope. 2. Read references before output: - `references/scbe-glossary.md` - `references/scbe-constants.md` - `references/dimensional-analysis.md` - `references/ai-to-ai-comms.md` 3. Run or draft only deterministic, inspectable outputs (tests, scripts, chain YAML, dataset card metadata). 4. Separate `build`, `document`, and `route` legs. 5. Finish with a tri-fold `action_summary`. ## Required Dual Output SCBE compliance checks must emit: - `StateVector`: deterministic technical state - `DecisionRecord`: action, signature, timestamp, reason, confidence ## Tongue and Model Routing - `KO` → `claude-opus-4-6` (Engineering/Tight systems checks) - `AV` → `claude-sonnet-4` - `RU` → `claude-opus-4-1` - `CA` → `claude-opus-4-6` (math/crypto/code validation) - `UM` → `claude-sonnet-4` - `DR` → `claude-opus-4-6` ## SCBE Layer Sequence (Canonical Reference) 1. Complex context state 2. Realification 3. Weighted transform 4. Poincaré embedding 5. Hyperbolic distance 6. Breathing transform 7. Phase transform 8. Multi-well realms 9. Spectral coherence 10. Spin coherence 11. Triadic temporal 12. Harmonic scaling 13. Decision + response 14. Audio axis ## Resources - `references/dimensional-analysis.md`: formula checks, transform validity, scaling risks. - `references/service-automation-contract.md`: MCP discovery and connector routing. - `references/browser-playwright-notes.md`: browse-safe automation patterns. - `references/self-improvement-loop.md`: observation → diagnosis → patch. - `references/ai-to-ai-comms.md`: typed `tool` / `llm` / `gate` chain schema. ## Script Map - `scripts/pqcm_audit.py` Property-based stress checks for formula proposals such as `kappa_eff`. - `scripts/ko_tongue_code_reviewer.py` KO-tongue agent class that returns `AgentOutput`. - `scripts/route_github_linear_chain.yaml` End-to-end branch chain for GitHub PR event, code review, PR comment, and Linear fallback issue. ## Service Routing Rules - Hugging Face, Notion, GitHub, Linear, and Zapier: - discover tool availability first, - report `callable now` vs `needs configuration`, - never claim capabilities not currently available. - Do not modify the canonical wall formula unless explicitly required. ## Output Contract Every route request should return: - `files_changed`: explicit paths. - `rationale`: short explanation of why the patch or behavior is needed. - `services_to_update`: explicit MCP/service identifiers. - `pending_integrations`: unresolved setup tasks.
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