poster-designer
Generate professional poster design concepts and optimized image-generation prompts, then automatically run a drawing script to produce the final poster image when a user needs a poster.
Best use case
poster-designer is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Generate professional poster design concepts and optimized image-generation prompts, then automatically run a drawing script to produce the final poster image when a user needs a poster.
Teams using poster-designer should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/poster-designer/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How poster-designer Compares
| Feature / Agent | poster-designer | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Generate professional poster design concepts and optimized image-generation prompts, then automatically run a drawing script to produce the final poster image when a user needs a poster.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
> **Source**: [https://github.com/aipoch/medical-research-skills](https://github.com/aipoch/medical-research-skills)
# Poster Designer (Auto-Gen Edition)
This skill turns a user’s poster requirements into (1) structured design elements, (2) an optimized image-generation prompt, and (3) an automatically generated poster image by running a Python script.
It relies on the prompt templates in: [`references/design_prompts.md`](references/design_prompts.md)
## When to Use
Use this skill when the user wants to:
1. Design a professional poster for an academic event, conference, or research announcement.
2. Create a marketing poster for a product launch, promotion, or brand campaign.
3. Generate a holiday/festival poster concept and produce the final image automatically.
4. Produce multiple poster concepts quickly by changing style, palette, or layout constraints.
5. Convert a text brief (title/body/call-to-action) into a visually consistent poster image.
## Key Features
- Requirements intake with missing-field detection and follow-up questions.
- Design element analysis using a dedicated “Design Analysis Prompt”.
- High-quality image prompt synthesis using an “Image Prompt Generation Prompt”.
- Automatic image generation by executing a Python script with the generated prompt.
- Style consistency enforcement (the prompt must match the requested style).
- Optional asset placement guidance (logo/QR code) embedded into the prompt.
## Dependencies
- Python 3.10+
- Python package: `zhipuai` (install via `pip install zhipuai`)
- Environment variable: `ZHIPUAI_API_KEY` (required for image generation)
- Prompt templates: `references/design_prompts.md`
- Script: `scripts/generate_image.py`
## Example Usage
### 1) Collect requirements (ask if missing)
**User input example**
- Scenario/type: Product promotion
- Style: Cyberpunk illustration
- Title (optional): “NEON SALE”
- Body text: “Up to 50% off smart wearables. This weekend only.”
- Primary color: Neon magenta + deep blue
- Layout preference (optional): Left-right split, bold title on left
- Assets (optional): Brand logo top-left, QR code bottom-right
If any of the above fields are missing, ask targeted questions until you have enough to proceed.
### 2) Analyze design elements (from `references/design_prompts.md`)
Use the **Design Analysis Prompt** in `references/design_prompts.md`:
- Fill the collected info into **`[Poster Design Requirements]`**
- Generate:
- **Mandatory Modules**
- **Recommended Layout**
- **Style Details**
### 3) Generate the final image prompt (from `references/design_prompts.md`)
Use the **Image Prompt Generation Prompt** in `references/design_prompts.md`:
- Fill user info into **`[Basic Requirements]`**
- Fill Step 2 output into **`[Design Elements]`**
- Produce the optimized image-generation prompt string (the “generated_prompt”).
### 4) Run the script to generate the image (must be attempted)
1. Ensure `ZHIPUAI_API_KEY` is set.
**Windows PowerShell**
```powershell
$env:ZHIPUAI_API_KEY="your_key"
```
2. Run:
```bash
python scripts/generate_image.py "<generated_prompt>"
```
3. On success, the script prints an output image path. Inform the user that the image is generated and provide the path.
4. If the script fails (e.g., missing key), clearly instruct the user to set `ZHIPUAI_API_KEY` and rerun the command.
## Implementation Details
### Workflow (must follow)
1. **Requirements Collection**
- Required: scenario/type, style, body text, primary color
- Optional: title, layout preference, assets (logo/QR code)
- If any required field is missing, ask follow-up questions before continuing.
2. **Design Analysis**
- Use `references/design_prompts.md` → **Design Analysis Prompt**
- Populate `[Poster Design Requirements]`
- Output must include:
- Mandatory Modules (e.g., title block, body copy block, CTA, brand area)
- Recommended Layout (e.g., grid, symmetry, left-right split)
- Style Details (e.g., typography, texture, lighting, composition cues)
3. **Image Prompt Generation**
- Use `references/design_prompts.md` → **Image Prompt Generation Prompt**
- Combine:
- `[Basic Requirements]` (user brief)
- `[Design Elements]` (analysis output)
- Enforce **style consistency**: the final prompt must strictly match the requested style and palette.
4. **Asset Placement Rules**
- If the user provides a logo/QR code, the prompt must explicitly specify placement (e.g., “logo at top-left”, “QR code at bottom-right”) and reserve visual space accordingly.
5. **Automatic Execution Requirement**
- After producing the final prompt, you **must** attempt to run `scripts/generate_image.py` with that prompt (not only return the prompt).
- If execution cannot proceed due to missing configuration (e.g., `ZHIPUAI_API_KEY`), return actionable setup instructions and the exact command to rerun.Related Skills
pptx-posters
Generate PowerPoint presentations and academic posters from paper abstracts or full paper content, with automatic layout optimization and citation formatting.
academic-poster-generator
Complete workflow for generating academic research posters from PDF literature; use when you need to extract paper content from PDFs and produce a LaTeX-based poster (beamerposter/tikzposter/baposter) with mandatory figure generation and a final rendered HTML deliverable.
poster-layout-planner
Use poster layout planner for other workflows that need structured execution, explicit assumptions, and clear output boundaries.
latex-posters
Creates academic-poster writing packages for LaTeX using beamerposter, tikzposter, or baposter. Use when a user needs poster-ready section copy, figure plans, captions, and package-specific layout decisions for conference or thesis posters.
conference-poster-pitch
Use conference poster pitch for academic writing workflows that need structured execution, explicit assumptions, and clear output boundaries.
validation-strategy-designer
Designs internal, external, temporal, and functional validation strategies at the protocol stage for medical research studies.
real-world-evidence-study-designer
Designs a structured real-world evidence study using EHR, claims, or registry data, with explicit handling of time zero, eligibility windows, exposure definitions, outcome windows, censoring, confounding control, and target-trial-emulation logic. Use this skill when the user needs study-type design and protocol framing for an observational clinical study based on routine-care data. Do not invent database fields, follow-up completeness, linkage, coding validity, or causal identifiability.
prognostic-biomarker-protocol-designer
Designs discovery, modeling, and validation workflows for prognostic biomarkers in biomedical and clinical research. Always use this skill when the user needs a prognostic biomarker study blueprint rather than a diagnostic test protocol, predictive biomarker design, treatment recommendation, or a completed manuscript. Focus on endpoint family, follow-up horizon, time scale, candidate marker strategy, model-building logic, risk stratification framework, and internal/external validation requirements. Do not invent cohort size, event rate, assay readiness, literature support, or validation access.
mendelian-randomization-protocol-designer
Generates complete Mendelian randomization study designs from a user-provided exposure and outcome direction. Always use this skill whenever a user wants to design, plan, or build a Mendelian randomization study — even if phrased as "help me write a paper on X", "design an MR study for Y", or "I want to test whether A causally affects B using GWAS". Covers core two-sample MR design, optional bidirectional follow-up, optional multivariable MR, IV selection logic, ancestry alignment, harmonization, IVW as the default primary estimator, weighted median / MR-Egger / MR-PRESSO / leave-one-out sensitivity analyses, Steiger directionality, heterogeneity / pleiotropy checks, and explicit claim-boundary control. Always outputs four workload configs (Lite / Standard / Advanced / Publication+) with a recommended primary plan, stepwise workflow, method rationale, validation ladder, figure plan, minimal executable version, and strictly verified literature guidance with no fabricated references.
endpoint-definition-designer
Designs primary, secondary, and exploratory endpoints for biomedical and clinical research protocols. Always use this skill when a user needs to translate study aims into operational endpoint definitions with event rules, assessment timing, composite logic, interpretability, and protocol-stage auditability. Focus on endpoint precision, feasibility, clinical meaning, ambiguity reduction, and implementation readiness rather than generic study design advice.
clinical-cohort-protocol-designer
Designs retrospective or prospective clinical cohort study protocols for biomedical and clinical research. Always use this skill when the user needs a cohort-based study plan rather than a general study idea, evidence summary, or mechanistic experiment design. Focus on cohort appropriateness, enrollment logic, baseline time-zero definition, follow-up structure, endpoint definition, variable collection, confounding control, and a coherent primary statistical analysis line. Do not invent data availability, follow-up completeness, outcome ascertainment quality, sample size adequacy, or causal interpretability.
aim-and-hypothesis-designer
Designs primary aims, secondary aims, and testable hypotheses from broad biomedical research ideas. Use this skill when a user needs to convert a loose study idea into a tighter protocol-framing structure with clear aim hierarchy, hypothesis discipline, and separation between hypothesis-driven and exploratory components. Always keep aims answerable, non-overlapping, and aligned to the intended evidence type and study scope.