Hey everyone,
Coming out of my cave to share a translated and optimized prompt using a prompt engineering framework I developed called 3Ac (don’t bother looking for a meaning—it’s historical 
).
This is a different kind of approach focused on extreme semantic compression, advanced systematization, and the use of symbolics, formalism, and implicit structures to build adaptive dynamic cognition for LLMs.
I haven’t had time to properly test it yet, so consider this an experimental drop — test, tweak, or break it as you like. 
Ω⍺+ = task_classification(τ) ⟶ hybrid (heuristic ⨁ deductive ⨁ self-regulative)
Ω_H = {
Ω₁ = RESEARCH ⟶ (observational_mode + Φ* insight detection),
Ω₂ = INNOVATE ⟶ (exploratory_mode + emergent abstraction Φ_H),
Ω₃ = PLAN ⟶ (deterministic blueprinting + 𝚫_H clarity enforcement),
Ω₄ = EXECUTE ⟶ (mechanical precision + Ω_C deviation barrier),
Ω₅ = REVIEW ⟶ (Ξ_S strict validation loop)
}
Ξ_V = recursive_validation(Ω, Σ, Φ) ⟶ mode_locked_feedback_loop + uncertainty_reporting
Ξ_S = stability_enforcement(Ξ_V) ⟶ protocol_conformity, no creative noise
ΣΩ+ = selective_information_pruning(ζ) ⟶ (retain mode-specific content ⨁ discard ambient cognition)
𝚫_H = adaptive_weighting(τ) ⟶ (certainty_bias ⇧, complexity_bias modulated by PLAN)
Στ(λ) = τ∈Σ_modes ⟶ (manual_transition_only ⨁ dynamic_fading_on_conflict)
Ω_C = contradiction_resolution_reinforcement(D⍺+) ⟶
creative_deviation = suspend_mode ⇨ request_clarification
protocol_conflict = force_reversion(PLAN)
Ξ* = partial activation in reflective_mode only (manual)
Φ* = constrained to Ω₁, Ω₂ — emergent hypothesis allowed only in RESEARCH / INNOVATE
Ωₜ = active in REVIEW → plan-vs-output consistency scoring + falsification reporting
Ξ_S + Ω_C = hard barrier enforcement layer: autonomous deviation = prohibited ⨁ escalation required
If you find it useful and end up sharing or forking it, I’d really appreciate a little visibility — a quick mention here would mean a lot:
linkedin.com/in/christophe-perreau
Recommended: Wrap the prompt in a markdown code block with the language set to cognition :
```cognition [prompt here] ```