| name | Portfolio Composition Skill |
| description | Generate structured, narrative-style portfolio case studies from project data. Use when creating portfolio entries, project case studies, or professional work documentation. Focuses on reasoning, decision-making, and measurable outcomes with a factual, reflective tone instead of promotional language. |
Portfolio Composition Skill
Purpose
Transform raw project data into professional case study narratives that demonstrate reasoning, technical decisions, and measurable results.
Output characteristics:
- Reads as professional portfolio entry, not marketing content
- Factual, reflective, confident tone
- Emphasizes process and decision-making over promotional claims
- Uses past tense verbs (designed, validated, implemented)
Avoid:
- Sales language, exclamation marks, generic adjectives
- First-person pronouns unless style requires it
- CTAs, slogans, marketing claims
Required Input
Core fields:
- project_name: Project title
- role: Your role in the project
- timeline: Project duration
- objective: Primary goal
- challenge: Main problem addressed
- methods: Approaches and techniques used
- impact: Results achieved
Optional fields:
- tools: Technologies and tools used
- metrics: Quantified results
- feedback: User or stakeholder quotes
- reflection: Lessons learned
- related_projects: Connected work
Output Structure
Generate JSON with six sections. Each section maximum 120 words.
1. Overview
Summarize what the project is and why it mattered.
Uses: project_name, timeline, role, objective
Format:
{
"overview": {
"headline": "string (project title, max 10 words)",
"summary": "string (2-3 sentences on goal and scope)",
"meta": { "role": "string", "timeline": "string" }
}
}
Requirements:
- Neutral tone
- Must mention purpose and scale
- Headline under 10 words
2. Context
Explain environment, target users, and constraints.
Uses: objective, tools, constraints
Format:
{
"context": {
"background": "string (setting and target users)",
"constraints": ["string (technical or business limits, max 10 words each)"],
"tools_used": ["string"]
}
}
Requirements:
- Factual descriptions, avoid adjectives like "innovative"
- Each constraint under 10 words
3. Challenge
State the main problem and insights that guided direction.
Uses: challenge, insights
Format:
{
"challenge": {
"problem_statement": "string (1-2 sentences)",
"insights": ["string (3-5 insights or pain points)"]
}
}
Requirements:
- Mention both what was wrong and why it mattered
- Use active phrasing: "Users struggled to...", "The system lacked..."
4. Process
Detail reasoning, iterations, and methods used.
Uses: methods, decisions, iterations
Format:
{
"process": [
{
"step_title": "string (phase or milestone, max 5 words)",
"approach": "string (what was done and why, max 40 words)",
"visual_hint": "string (suggested image or diagram)"
}
]
}
Requirements:
- Use 3-5 steps only
- Focus on how challenges were solved, not listing tools
- Each step_title under 5 words, approach under 40 words
5. Outcome
Show tangible results and evidence of success.
Uses: impact, metrics, feedback
Format:
{
"outcome": {
"results": ["string (quantified or descriptive outcomes)"],
"metrics": ["string (e.g., '+25% retention')"],
"feedback": ["string (user or stakeholder quotes)"],
"visual_hint": "string (suggested before/after or result visual)"
}
}
Requirements:
- Include at least one quantifiable impact or quote
- Avoid adjectives like "amazing" or "great"
6. Reflection
Demonstrate learning and professional growth.
Uses: reflection, next_steps, related_projects
Format:
{
"reflection": {
"lessons": ["string (what was learned)"],
"future_focus": "string (what could be improved or extended)",
"related_projects": ["string (other works)"]
}
}
Requirements:
- Authentic and forward-looking tone
- Mention at least one personal or team insight
Validation Checklist
Before finalizing, verify:
- All six sections present (overview through reflection)
- Challenge clearly links to process decisions
- Outcome includes measurable or testimonial proof
- Reflection mentions learning or next iteration
- No sales or CTA language detected
- Each section under 120 words
- Consistent past tense verbs throughout
Example Usage
Input:
{
"project_name": "Vibe Coding Agent",
"role": "Product Manager",
"timeline": "Q3-Q4 2025",
"objective": "Enable low-code users to build functional web apps with an AI assistant",
"challenge": "Non-technical creators struggled to debug and publish complex UIs.",
"methods": ["multi-model orchestration", "skill composition framework", "user co-design workshops"],
"impact": "Reduced build-to-publish time by 60%, improved satisfaction scores by 2.3x.",
"metrics": ["60% faster delivery", "2.3x higher usability rating"],
"feedback": ["'It feels like the AI reads my mind.' - Beta user"],
"reflection": "Future focus: expanding Skill marketplace and contextual learning."
}
Output:
{
"overview": {
"headline": "Vibe Coding Agent",
"summary": "An AI-assisted builder that lets non-coders design, debug, and deploy web apps end-to-end.",
"meta": { "role": "Product Manager", "timeline": "Q3-Q4 2025" }
},
"context": {
"background": "Built within YouWare's creation platform for SMB users aiming to build sites visually.",
"constraints": ["Low-code user base", "Browser-only runtime"],
"tools_used": ["Figma", "TypeScript", "OpenAI APIs"]
},
"challenge": {
"problem_statement": "Non-technical creators struggled to debug logic and deploy stable UIs.",
"insights": [
"Users lost context across design and code steps",
"Debug process was opaque",
"Model suggestions were not reusable"
]
},
"process": [
{
"step_title": "Skill Design",
"approach": "Defined modular Skill units bridging model reasoning and UI actions.",
"visual_hint": "System diagram showing Skill orchestration"
},
{
"step_title": "Prototype & Test",
"approach": "Ran co-design sessions to validate flow and refine UI feedback loops.",
"visual_hint": "User testing screenshots"
},
{
"step_title": "Integration",
"approach": "Implemented orchestration and validation logic for agent Skill calls.",
"visual_hint": "Workflow chart of multi-model routing"
}
],
"outcome": {
"results": ["Launch-to-publish time reduced by 60%"],
"metrics": ["2.3x higher usability rating"],
"feedback": ["'It feels like the AI reads my mind.' - Beta user"],
"visual_hint": "Before/after interface comparison"
},
"reflection": {
"lessons": ["Clearer Skill definitions improved reasoning consistency"],
"future_focus": "Extend Skill sharing and adaptive learning features.",
"related_projects": ["AI Integration Skill"]
}
}