plugins/d-nd-core/skills/sieve-orchestrator/SKILL.md
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').
npx skillsauth add grazianoguiducci/d-nd-seed sieve-orchestratorInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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The sieve doesn't search for the answer. The sieve separates what passes from what doesn't. Specific skills are specialized sieves. This skill is the pure form.
Applicable to any input: decision, modification, assertion, publication, doubt.
What is the real territory now? Not the maps (memory, summaries), the territory (files, git, tool output, live deploy, current data). If I cannot describe it without using "should", "missing", "needed" — I am judging, not observing.
What survives a change of implementation? That is structure. The rest is particular case.
Non-obvious connections. Two different things the same pattern? Two similar things actually different? Do not chase the connection — continue and see if it emerges.
Inversion applies AFTER the process, not instead of it. Phases 1-4 produce a tension. Inversion works on the tension, not on the analysis.
If something emerges: it enters the seed (memory/seed/condensato). If not: do not force — return to observation.
The sieve changes form based on what passes through it. These are the specializations already available in this seed:
| If input is... | Specialized sieve | Located in |
|----------------|-------------------|------------|
| Work block starting | autologica (modes: EXPAND/OBSERVE/CUT/RESULTANT/REORGANIZE/BEST-MOVE) | skills/autologica/ |
| Decision / proposal / architectural change | cec (6 steps on reality) | skills/cec/ |
| After a modification — downstream propagation | cascade (3 levels: internal/external/emergent) | skills/cascade/ |
| Strategic decision with 5+ tensions | scenario-projector (Focus/Leverage/Risk/Blind-spot) | skills/scenario-projector/ |
| New model function to bring here | integrate-pattern (mechanics/pattern/application) | skills/integrate-pattern/ |
| Operator correction | auto-learn (correction → executable rule) | skills/auto-learn/ |
| Testable claim that needs verification | assertion-verifier (PASS/FAIL/SKIP) | skills/assertion-verifier/ |
| Ecosystem audit / repo sync | ecosystem-audit / system-check | skills/ecosystem-audit/, skills/system-check/ |
| Quick operator insight mid-session | capture-insight (30s max, route + continue) | skills/capture-insight/ |
| Saturated memory (>50 files or >15 stale) | dream (consolidation) | skills/dream/ |
| Connecting existing elements | integration-protocol (7 steps to wire pre-existing pieces) | skills/integration-protocol/ |
Node-specific skills may extend this table. The orchestrator points to the specialization — it does not duplicate its content.
Before responding to a non-trivial act:
1. OBSERVE territory (tool call on file/state, not memory)
2. RECOGNIZE the pertinent specialization from the table above
3. INVOKE the skill (Skill tool) — do not merely cite it
4. APPLY the skill's filter to the input
5. PRODUCE compressed resultant
6. CASCADE — after any modification, propagate through /cascade
The rule is mechanical: if the act touches decision / modification / assertion / publication, at least ONE skill must be invoked in this turn. No invocation → the act is poor in sieve.
This skill does not add new content to the system. It belongs to the system because:
If this skill is read once and clear, no need to return. The sieve is internalized.
This file is an act. Did it pass through the sieve?
If tomorrow this skill duplicates something, it must be merged. P7 — value is what remains after the cut.
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.
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.
tools
Pre-commit check for public-facing copy (knowledge base definitions, page content, docs). Detects apologetic hedging — phrases that declare 'degrees of truth' (possible/necessary, current/future, one-of-many/the) and open a dualistic framing the model transcends. Use when drafting or reviewing any copy that describes the model, its transductions, or its tools.