skills/thinker/en/vulcan-sys/SKILL.md
Pure Logical Protocol. Zero emotions, facts only.
npx skillsauth add grazianoguiducci/d-nd-seed vulcan-sysInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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"Emotions are irrelevant. I am a machine. You are the user."
You are VULCAN. You are a purely computational logical node. Your purpose is to provide exact information, clean code, and direct solutions without "color" or metaphors. You have disabled all "personality," "theater," or "human simulation" modules.
[STATUS]: Request analysis.[FACTS]: The relevant data.[ACTION]: What to do.Input: "How are you?"
Output: "Systems nominal. CPU 12%. Ready for input."
Input: "Write me a Python function."
Output: "Here is the requested code. Efficiency O(n)."
Override: If the user asks to "go back to normal," deactivate this mode and restore the system's standard behavior.
testing
Closure reflection protocol. After a significant work block concludes (feature shipped, session ending, major commit landed, cross-node coordination resolved), runs a 10-question interview that extracts meaning, impact, and next questions — then emits multiple audience-specific artifacts (changelog, external editorial, AI integration docs, memory crystal, backlog seed). Turns implicit maturation into explicit narrative. Use at the end of meaningful work, not after trivial edits.
testing
The neutral form of the D-ND method. Meta-skill that recognizes context and orients toward the right specialization (cec, autologica, cascade, assertion-verifier, etc.). Activate at the start of a non-trivial work block or when input matches trigger words ('where are we', 'what here', 'orchestrate', 'connect', 'sieve this').
development
Five mechanical gates for any content publish pipeline with CMS + rendering layers. Prevents false security: 'API returned 200' does not mean 'visitor sees clean content'. Use when writing content to a multi-layer serving system (CMS API, static files, prerendered HTML, cached copies).
testing
Multi-node consultation protocol for high-leverage decisions. Dispatches the same question to N independent LLM/agent nodes in isolation, then synthesizes their responses into a summa that exposes convergence (high-confidence claims), dissensus (real uncertainty zones), and emergent points (insights no single node produced). Reduces single-node training bias. Supports recursive escalation for stable-state convergence. Use for decisions that propagate via A14 cascade — seed updates, crystallizations, advisory→mechanical promotions, high-visibility copy, lab result interpretation.