no-prepost-user-input

To preserve LLM user input decisions, you must decouple user-input interpretation from local deterministic post-processing. This ensures the LLM's original decisions regarding acceptance, commitments, or command routing are not altered by semantic rewrites or rule-based overrides.

Deterministic pre-processing and post-processing in LLM prompts refers to local rule-based filters that alter user-input interpretation. This Skill prevents these deterministic filters from modifying the LLM's original semantic decisions, ensuring durable state persistence for accepted commands without semantic reinterpretation.

You persist LLM accepted commands and commitments by using explicit prompts, schemas, and evaluation prompts to emit durable fields. This approach maintains state persistence by keeping the responsibility for interpreting user commands within the LLM rather than applying local semantic rewrites.

You should decouple user-input interpretation from local post-processing when handling cognition preferences, consolidator facts harvesting, or evaluator prompts determining channels for results. This ensures the LLM's decisions are preserved without semantic reinterpretation altering the acceptance or routing of commands.

Yes, decoupling LLM interpretation requires explicit prompts, schemas, and evaluation prompts to emit durable fields. These components are necessary to ensure that accepted commands and commitments are persisted accurately without undergoing semantic reinterpretation from deterministic filtering.

Deterministic filtering alters LLM command routing and acceptance because local rule-based overrides reinterpret the semantic meaning of user inputs. Decoupling user-input interpretation from these filters prevents the deterministic post-processing from modifying the LLM's original decisions and commitment persistence.