Frame non-trivial problems using an Abduction–Deduction–Induction cycle.
npx skills add https://github.com/random1st/groundwork --skill fpf-random1st
Please help me install this Agent Skill. Skill: fpf Source: https://github.com/random1st/groundwork/tree/main/plugins/fpf Command: npx skills add https://github.com/random1st/groundwork --skill fpf-random1st
FPF provides a disciplined framing approach to complex problems to improve reasoning quality and auditability.
Apply the FPF workflow to frame a non-trivial problem and drive the ADI cycle before solving.
High-intent search queries and answers about installing and using this skill.
Use the Abduction–Deduction–Induction cycle to structure reasoning for root-cause investigations across complex systems. This method separates observed, described, and specified aspects to ensure traceability and improve auditability.
Frame complex engineering problems for design reviews by applying a disciplined lifecycle framing approach. Separating observed, described, and specified aspects guides evidence gathering and enables repeatable decision-making.
Yes, you need python3 and curl installed to run the FPF workflow. The built-in builder uses these dependencies to pull upstream specs and customize your local corpus for problem framing.
Evidence tagging works during engineering audits by applying calibration tags within a modular corpus. This separates observed data from described aspects and specified requirements to ensure explicit traceability.
Yes, you can use this approach to audit complex systems without losing decision context. The modular corpus and calibration tags maintain explicit lifecycle framing to capture and preserve reasoning throughout the analysis.
Problem framing fails without separating observed, described, and specified data because reasoning becomes entangled. The I/D/S discipline enforces clear boundaries to prevent assumptions from corrupting the evidence gathering process.
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