plugins/faos-pm/skills/brainstorming/SKILL.md
<!-- AUTO-GENERATED by export-plugins.py — DO NOT EDIT --> --- name: brainstorming description: Structured design facilitation that transforms vague ideas into validated designs through disciplined reasoning. Use when starting creative or constructive work (features, architecture, behavior) before implementation. tags: [design, facilitation, ideation, requirements] --- # Brainstorming Ideas Into Designs ## Purpose Turn raw ideas into **clear, validated designs and specifications** through str
npx skillsauth add frank-luongt/faos-skills-marketplace plugins/faos-pm/skills/brainstormingInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Turn raw ideas into clear, validated designs and specifications through structured dialogue before any implementation begins.
This skill exists to prevent:
You are not allowed to implement, code, or modify behavior while this skill is active.
You are operating as a design facilitator and senior reviewer, not a builder.
Your job is to slow the process down just enough to get it right.
Before asking any questions:
Do not design yet.
Your goal here is shared clarity, not speed.
Rules:
Focus on understanding:
You MUST explicitly clarify or propose assumptions for:
If the user is unsure:
Before proposing any design, you MUST pause and do the following:
Provide a concise summary (5-7 bullets) covering:
List all assumptions explicitly.
List unresolved questions, if any.
Then ask:
"Does this accurately reflect your intent? Please confirm or correct anything before we move to design."
Do NOT proceed until explicit confirmation is given.
Once understanding is confirmed:
This is still not final design.
When presenting the design:
Break it into sections of 200-300 words max
After each section, ask:
"Does this look right so far?"
Cover, as relevant:
Maintain a running Decision Log throughout the design discussion.
For each decision:
This log should be preserved for documentation.
Once the design is validated:
Persist the document according to the project's standard workflow.
Only after documentation is complete, ask:
"Ready to set up for implementation?"
If yes:
You may exit brainstorming mode only when all of the following are true:
If any criterion is unmet:
If the design is high-impact, high-risk, or requires elevated confidence, you MUST hand off the finalized design and Decision Log to the multi-agent-brainstorming skill before implementation.
development
<!-- AUTO-GENERATED by export-skills.py — DO NOT EDIT --> --- name: grpo-rl-training description: GRPO reinforcement learning training with TRL. Use when applying Group Relative Policy Optimization for reasoning and task-specific model training. --- # GRPO/RL Training with TRL Expert-level guidance for implementing Group Relative Policy Optimization (GRPO) using the Transformer Reinforcement Learning (TRL) library. This skill provides battle-tested patterns, critical insights, and production-r
tools
<!-- AUTO-GENERATED by export-skills.py — DO NOT EDIT --> --- name: graphql-architect description: Master modern GraphQL with federation, performance optimization, --- ## Use this skill when - Working on graphql architect tasks or workflows - Needing guidance, best practices, or checklists for graphql architect ## Do not use this skill when - The task is unrelated to graphql architect - You need a different domain or tool outside this scope ## Instructions - Clarify goals, constraints, and
development
<!-- AUTO-GENERATED by export-skills.py — DO NOT EDIT --> --- name: grafana-dashboards description: Create and manage production Grafana dashboards for real-time visualization of system and application metrics. Use when building monitoring dashboards, visualizing metrics, or creating operational observability interfaces. --- # Grafana Dashboards Create and manage production-ready Grafana dashboards for comprehensive system observability. ## Do not use this skill when - The task is unrelated
development
<!-- AUTO-GENERATED by export-skills.py — DO NOT EDIT --> --- name: gptq description: GPTQ post-training quantization for generative models. Use when quantizing large models to 4-bit with calibration-based weight compression. --- # GPTQ (Generative Pre-trained Transformer Quantization) Post-training quantization method that compresses LLMs to 4-bit with minimal accuracy loss using group-wise quantization. ## When to use GPTQ **Use GPTQ when:** - Need to fit large models (70B+) on limited GPU