Zum Hauptinhalt springen
Dieser Inhalt ist noch nicht in Ihrer Sprache verfügbar und wird auf Englisch angezeigt.

First Principles Framework (FPF) Plugin

Plugin Aktiv

First Principles Framework (FPF) for structured reasoning using workflow command pattern. Implements ADI (Abduction-Deduction-Induction) cycle via propose-hypotheses workflow with fpf-agent for hypothesis generation, logical verification, empirical validation, and auditable decision-making. Includes utility commands for status, query, decay, actualize, and reset.

6 Skills 0 MCPs
Zweck

To make AI decision-making transparent, auditable, and evidence-based by enforcing a systematic hypothesis-driven reasoning process.

Funktionen

  • Implements FPF ADI cycle for structured reasoning
  • Generates, verifies, and validates competing hypotheses
  • Audits trust scores for evidence-based validation
  • Provides commands for managing decision cycles and knowledge
  • Creates auditable decision rationale records (DRRs)

Anwendungsfälle

  • Making critical architectural or technical decisions with an auditable trail
  • Preventing premature conclusions by systematically evaluating alternatives
  • Building and leveraging project knowledge over time
  • Ensuring AI-assisted development processes are transparent and accountable

Nicht-Ziele

  • Making unsupervised AI decisions
  • Replacing human judgment in the decision-making process
  • Providing a general-purpose LLM interaction tool

Workflow

  1. Initialize FPF context with problem statement
  2. Generate competing hypotheses (Abduction)
  3. Allow user to add hypotheses
  4. Verify logic of each hypothesis (Deduction)
  5. Validate evidence for logically sound hypotheses (Induction)
  6. Audit trust scores for validated hypotheses
  7. Present decision comparison and select winner
  8. Record decision rationale (DRR) and present summary

Praktiken

  • Structured Reasoning
  • Knowledge Management
  • Auditability

Practical Utility

  • warning:Production readinessThe plugin's core functionality, especially its reliance on a large FPF specification (~600k tokens) loaded into a sub-agent with a Sonnet[1m] model, may quickly consume token limits and could hinder practical use in real workflows without careful management.

Installation

Zuerst Marketplace hinzufügen

/plugin marketplace add NeoLabHQ/context-engineering-kit
/plugin install fpf@context-engineering-kit

Qualitätspunktzahl

88 /100
Analysiert 1 day ago

Vertrauenssignale

Letzter Commit9 days ago
Sterne993
LizenzGPL-3.0-or-later
Status
Quellcode ansehen