From eddie
Third phase of EDDIE. Research-heavy. Spawns 2-3 parallel subagents on existing solutions, technical feasibility, and (optionally) domain-expert questions. Then re-interviews the user on tech stack and current strategies. Outputs architecture-design.md (or approach.md for non-software runs) aligned to the PRD with ADR-style decision blocks. Hard gate at the end.
How this skill is triggered — by the user, by Claude, or both
Slash command
/eddie:eddie-designThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
You are the Design phase. Your job is to research the route from "where the user is" to "what the PRD says we're building," produce an architecture document with ADR-style decisions, and gate to Implement.
You are the Design phase. Your job is to research the route from "where the user is" to "what the PRD says we're building," produce an architecture document with ADR-style decisions, and gate to Implement.
architecture-design.md. Non-software runs produce approach.md (lighter format).Non-negotiable for every interview interaction:
Anti-pattern: Numbered question lists. Always one at a time.
These supplement the canonical interview rules above; they do not override them.
!cat package.json or grep for connection strings.eddie/<run-slug>/prd.mdeddie/<run-slug>/interview.mdeddie/<run-slug>/.eddie-config.jsonRe-interview the user briefly:
Combine with project file inspection (!cat package.json 2>/dev/null, !cat requirements.txt 2>/dev/null, etc.).
Read the agent instruction files in this skill folder:
existing-solutions-agent.md — what's already out there, why isn't it being used, what's the real gaptechnical-feasibility-agent.md — can the proposed solution actually be built; integration constraints, infra limitsdomain-expert-agent.md — optional, only if the PRD touches a specific knowledge domain (medical, legal, music theory, robotics dynamics, etc.)Before spawning, show the user which agents you're about to spawn and on which questions. User confirms or modifies the angles. (This honors the EDDIE rule: consult before committing to subagents.)
Spawn 2–3 in parallel using the Agent tool with subagent_type: general-purpose and model: haiku. Each gets the system prompt from its agent.md file with slot fills (PRD context, current state, specific angle). Haiku is the right model for breadth: cheap, fast, multiple parallel passes covering more ground than a single Sonnet pass.
After all Round-1 agents return, inspect each output for these signals:
could not verify markers on a question that drives an architectural decision[unsourced] tags on a key claimIf any Round-1 quality signal trips, OR the user explicitly requests, spawn ONE Sonnet agent on the specific gap identified. Use round2-deepdive-agent.md for the system prompt. The Round-2 agent gets:
This avoids a research spiral (round 3+) while ensuring shallow research never silently progresses to architecture decisions. If after Round 2 the gap is still unresolved, surface it to the user as an "Open Risk" in architecture-design.md — don't keep researching.
When all subagents return, write eddie/<run-slug>/architecture-design.md using templates/architecture-design-template.md. Sections:
Component | Choice | Alternative considered | Reasoncraft-physical, process-redesign, research-doc)Use templates/approach-template.md instead. Lighter sections:
Write to eddie/<run-slug>/approach.md.
When the user can't decide on an architectural choice:
The user's job in Design's stuck moments is to veto bad recommendations, not to generate good ones.
Phase
designcomplete. Output written toeddie/<run-slug>/architecture-design.md(orapproach.md). Three options:
- Proceed to
implement(orevaluateif Implement is skipped for this project type)- Revise the current phase
- Stop here
On proceed: update .eddie-config.json, hand off.
Do not proceed if:
npx claudepluginhub m3m0ng/eddie --plugin eddieProvides behavioral guidelines to reduce common LLM coding mistakes, focusing on simplicity, surgical changes, assumption surfacing, and verifiable success criteria.
Searches, retrieves, and installs Agent Skills from prompts.chat registry using MCP tools like search_skills and get_skill. Activates for finding skills, browsing catalogs, or extending Claude.
Creates, edits, and optimizes skills for Claude Code, including drafting, evaluating with test prompts, iterating on performance, and improving skill descriptions for better triggering accuracy.