From craft-skills
Cost-optimized craft variant: uses local Qwen3.6 for implementation and reviews, Opus orchestrates, Sonnet as fallback. Requires LM Studio. Reduces API costs ~45-60% vs /craft.
How this skill is triggered — by the user, by Claude, or both
Slash command
/craft-skills:craft-aceThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Full design-first pipeline with Qwen3.6 as the primary implementer and reviewer. Opus orchestrates and handles integration. Sonnet serves as fallback for tasks the local model can't handle.
Full design-first pipeline with Qwen3.6 as the primary implementer and reviewer. Opus orchestrates and handles integration. Sonnet serves as fallback for tasks the local model can't handle.
Profile: claude+ace
Pipeline: Brainstorm -> Plan -> Develop -> Browser Test -> Report (same as /craft)
This wrapper writes a profile marker, then delegates to the canonical craft pipeline. The claude+ace profile triggers:
echo -n "claude+ace" > .craft-profile
Read skills/craft/SKILL.md from this plugin and follow every phase exactly as written. The profile gating in that file ensures:
llm-implement.sh instead of Claude agentsThe user input is: $ARGUMENTS
Pass the input through to the craft pipeline as if the user had invoked /craft directly.
${LLM_URL:-http://127.0.0.1:1234}| Activity | Savings vs /craft |
|---|---|
| Reviews (spec, plan, post-develop) | 100% (all local) |
| Data layer implementation (~40-50% LoC) | 100% (all local) |
| UI implementation (~30-40% LoC) | ~30-50% (Qwen3.6 first, Sonnet fallback) |
| Integration (~10-20% LoC) | 0% (stays on Opus) |
| Total estimated savings | ~45-60% |
npx claudepluginhub alexiolan/craft-skills --plugin craft-skillsOrchestrates a design-first feature development pipeline: brainstorm with user, plan, parallel agent development, then browser testing. Useful for complex features or unclear requirements.
Analyzes project requirements and recommends optimal Anthropic architectures using Skills, Agents, Prompts, and SDK primitives for scalable AI systems.
Routes Claude Code tasks to optimal models (Haiku, Sonnet, Opus) using decision matrices, cost tables, complexity signals, and subagent assignments for cost/quality tradeoffs.