plugins/d-nd-core/skills/cascade/SKILL.md
Three levels of propagation: internal (within the change), external (binding: seed, site, nodes), emergent (discoveries during propagation). Activate after every significant modification.
npx skillsauth add grazianoguiducci/d-nd-seed cascadeInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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When something changes, three things happen:
Before propagating, the change must mature.
Questions to ask:
The change must arrive where it needs to. These are mandatory.
Function/skill/hook created or modified
├─ In the seed? → neutral version, zero specific references
├─ On the site? → spec to the responsible node
├─ Config files? → update references
├─ Other nodes? → message via collaboration channel
├─ Shared rules? → if applicable to all nodes
├─ Local memory? → update memory files
└─ Settings? → if it's a hook, verify registration
Corrections:
Correction received
├─ Executable rule (when X → do Y)
├─ Local memory → update
├─ Seed kernel? → if universal
├─ Other nodes? → if cross-node
└─ /auto-learn → activate
During propagation, reading files to evaluate where to propagate, you may discover:
These are NOT implemented immediately. Note them as potential and evaluate after the current cascade is complete. Otherwise: infinite cascade.
Rule: complete the current cascade, then return to the emergents.
Internal → External → Emergent → (new) Internal
The cascade is a cycle. But each round must complete before the next.
Internal:
External:
Emergent:
The system has two levels of knowledge:
The memory index (MEMORY.md or equivalent) should clearly separate these two levels.
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.