plugins/d-nd-core/skills/scenario-projector/SKILL.md
Structural scenario projection from tensions. Use when facing complex decisions with 5+ competing factors, when cross-checking a strategy or thesis, when prioritizing with limited resources, or when mapping risks.
npx skillsauth add grazianoguiducci/d-nd-seed scenario-projectorInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Not a prediction tool. A structural lens. The structure contains the answer — don't interpose numbers between structure and decision.
Every tension is a dipole D(claim, anti-claim). The anti-claim isn't negation — it's the other pole. Dipoles resonate (binary: yes/no). The trajectory follows maximum potential.
4 structural lenses:
| Lens | What it reveals | |------|-----------------| | Focus | Where tensions converge — natural action points | | Leverage | Structural pillars — invest here, it propagates | | Risk | Anti-claim contradicts a neighbor — crack in the thesis | | Blind spot | Isolated from the field — unexplored or noise |
import sys
# Find scenario_projector.py in the seed scripts directory
# Typical location: d-nd-seed/plugins/d-nd-core/scripts/
sys.path.insert(0, '/path/to/d-nd-seed/plugins/d-nd-core/scripts')
from scenario_projector import ScenarioProjector
# From tensions directly
sp = ScenarioProjector(seed_data={
'context': 'startup_strategy', # enables domain-specific language
'direction': 'What we are trying to achieve',
'tensions': [
{'id': 'TENSION_ID', 'claim': 'The thesis but the counter-thesis'},
# ... 5-15 tensions
]
})
# The 4 lenses
checks = sp.cross_check() # per-tension structural verdict
strat = sp.strategy() # focus, leverage, risks, blind spots
plan = sp.action_plan() # prioritized actions with domain language
result = sp.explore(verbose=True) # full trajectory + passages + field
# Cross-check the field
python scenario_projector.py --seed path/to/seed.json --cross-check
# Strategy insights
python scenario_projector.py --seed path/to/seed.json --strategy
# Action plan
python scenario_projector.py --seed path/to/seed.json --action-plan
# Full exploration
python scenario_projector.py --seed path/to/seed.json --explore
Seeds in d-nd-seed/plugins/d-nd-core/scripts/examples/:
startup_strategy.json — Series A founder decisionsproduct_roadmap.json — Feature prioritizationdue_diligence.json — Investment thesis cross-checkrisk_assessment.json — Digital transformation risksportfolio_management.json — Multi-asset rebalancingSee automation_pattern.py in examples/ for the full pattern:
DATA SOURCE → TensionGenerator → ScenarioProjector → ActionConsumer → EXECUTION
The projector is the cognitive middleware: domain-agnostic structural analysis between domain-specific input (tension generation) and output (action execution).
| Tool | Role | When | |------|------|------| | CEC | How to think about each tension | During — deepens individual analysis | | Domandatore | Generates tensions from a question | Before the projector — creates the input | | Godel | Inverts a specific claim (det=-1) | On contested tensions — flip the assumption | | Projector | Maps the field of all tensions | After — sees the structural whole |
Natural flow: Domandatore → Projector → CEC on focus areas → Godel on contradictions.
| Verdict | Meaning | Action | |---------|---------|--------| | confirmed | Structural pillar with high support | Amplify — investment propagates | | supported | Backed by neighbors, not a pillar | Monitor — it's healthy | | contested | Anti-claim matches a neighbor's claim | Investigate — there's a crack | | weak | Few connections, not isolated | Strengthen or deprioritize | | unverifiable | Isolated — no neighbors to verify | Explore — could be hidden potential |
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.