kalyvask

Fde Simulation — Development skill for Claude Code

Development community

Hands-on Forward Deployed Engineer role simulations: scope an enterprise engagement, build multi-agent prototypes, ship eval suites, score artifacts against a reference, detect expert traps, and defen.

How to install Fde Simulation

This entry records only its repository, not the path inside it, so there is no exact command to give. Open kalyvask/fde-simulation and copy the folder into ~/.claude/skills/, or the file into ~/.claude/agents/.

What Fde Simulation does

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README

FDE Simulation

Hands-on simulations of the Forward Deployed Engineer role. Run a full customer engagement on synthetic but realistic case studies — identify problems, scope wedges, build working agent prototypes, ship eval suites, hand off to production. Then score every artifact against the reference, detect the traps a senior FDE would catch, defend your build under a 5-minute hostile grill, and bundle the result as a portfolio piece.

About

Forward Deployed Engineering (and its equivalents — Solutions Architect, AI Strategist, Agent Strategist, Deployed PM) is customer-embedded technical product work. The day-to-day is: scope an enterprise engagement, identify the highest-value workflow to automate, design a multi-agent system, build a prototype, validate with stakeholders, hand off to production.

There's almost no public material that simulates this end-to-end. Most "AI engineering" tutorials skip the customer-engagement work; most product courses skip the agent architecture. This repo fills the gap with runnable simulations grounded in the actual shape of the job.

What you can do with it:

Run a 4-week customer engagement end-to-end on two fictional cases (insurance + finance), with synthetic data and working Python agent prototypes
Identify and scope problems using six discovery frameworks on a real-feeling customer brief (12-stakeholder political maps, kill-criteria framing, 4-source convergence)
Build agents by extending the prototype scaffolds — 5-7 specialized agents per case, hybrid deterministic + LLM, examiner-readable audit traces
Ship eval suites with pass^k=5 production thresholds, weighted by failure cost, including adversarial cases
Practice the customer-facing craft via Claude-roleplay stakeholder interviews and live customer-simulation rounds
Score your work against the reference solution per phase via a structured rubric (0-3 × 5 dimensions) with a JSON sidecar so progress across attempts is trackab