name: case-study-writer
description: Draft a portfolio case study from raw project materials — build journals, project decks, scattered notes, screenshots, chat transcripts, or a designer's verbal rambling about a project. Use this skill whenever the user wants to write, draft, start, or restructure a case study, turn a build journal or project notes into portfolio content, or says "help me write up this project." Pairs with build-journal (input), voice (style), portfolio-edit-pass and portfolio-critique (downstream review).
Case Study Writer
Draft portfolio case studies that read like a senior designer's judgment narrative, not a process recap. This skill is the generative counterpart to portfolio-critique — its structure deliberately mirrors that skill's rubric, so a draft from here should pass the critique's storytelling, role-clarity, and impact checks on the first run.
Before writing anything: if the voice skill exists, read its voice profile and write in it. If it doesn't, ask the user for 2-3 samples of their writing and match them.
Accept anything: build journals (especially journal/ folders from the build-journal skill — start with the case-study seeds sections), decks, Figma links, screenshots, scattered notes, or a transcript of the user talking through the project. Messy input is expected; extracting the story from mess is the job.
If material is thin, interview before drafting. The five questions that matter most:
- Why did this project matter — to the business and to the user? (Stakes)
- What was the moment the obvious approach failed, or an assumption got overturned? (Tension — every good case study has one; find it)
- What did you personally decide, design, and influence vs. the team? (Role)
- What tradeoff did you consciously make, and what did you give up? (Judgment)
- What happened after it shipped — numbers if they exist, signal if they don't? (Impact)
If the user can't answer #2, dig: "what surprised you?", "what took way longer than expected?", "what did you argue about?" The tension is always there; it's usually just not labeled as the story yet.
Structure
Default skeleton — adapt freely, but every section's job must be done somewhere:
- Hook (2-4 sentences): the stakes and the outcome, up front. The reader should know why to care and what happened before any process appears. Never open with "The team was tasked with..."
- Context: product, users, constraints — only what's needed to understand the decisions that follow. Ruthless about length.
- Role statement: one unambiguous block: what the user owned, decided, and drove. First person singular. This is non-negotiable — "we" without "I" is the most common senior-portfolio killer.
- The work, told as decisions: the body is a sequence of decision points, not a tour of artifacts. For each: the situation, the options considered, the choice, the why. The tension moment from question #2 anchors this section. Artifacts (screens, diagrams) appear as evidence for decisions, each with a caption that adds information beyond what's visible.
- What didn't work: at least one genuine dead end or failure, treated with respect, plus the recovery. Credibility lives here.
- Outcome: the sharpest honest impact claim available. Rank evidence: measured metric > directional metric > adoption/qualitative signal > shipped. Never inflate; "the team adopted it as the default pattern" beats a vague "improved engagement."
- Reflection (short): what the user would do differently, or what the project changed about how they work. 2-4 sentences; this is where seniority shows quietly.
For AI-native / built-with-AI case studies
When the project was built with Claude or other AI tools, the human judgment is the story — the tool is the setting:
- Include 1-3 verbatim prompts at genuine decision points, each paired with why that prompt and what it produced. Prompts without commentary are trivia.
- The attempts log (tried → failed → adjusted) is the narrative spine. Frame failures as the user reading the tool's behavior and adapting — that's agentic UX thinking applied to their own workflow, and it should be named as such when the target role is AI tooling.
- Be precise about the human/AI boundary: what the tool generated, what the user directed, what the user overrode and why. Vagueness here reads as either inflation or naivety.
Rules
- Never fabricate. No invented metrics, quotes, decisions, or detail the source material doesn't support. Mark gaps for the user:
[NEED: what the actual adoption number was]. A draft with honest holes is a working document; one with plausible inventions is a liability in an interview. - Honor [SENSITIVE] flags from build journals: sanitize or placeholder that content (
[SANITIZE: internal metric — abstract to "double-digit lift"?]) and surface every instance to the user in the delivery note. - Write tight. Target 800-1400 words of body copy for a full case study. If the draft runs long, the cut candidates are context and process, never tension and decisions.
- Skimmable headers: every section header should carry meaning alone — a skimmer reading only headers should get the arc. "The Process" is banned; "Why the first navigation model failed" is the energy.
- Deliver as a file (
{project}-case-study-draft.md) with a short delivery note: the 2-3 weakest spots in the draft, any [NEED]/[SANITIZE] items, and a suggestion to run portfolio-critique once gaps are filled.