Expert guidance for designing, testing, and optimizing prompts that reliably guide LLM behavior.
Works with
Covers six core capabilities: prompt design and optimization, system prompt architecture, context window management, output format specification, few-shot example design, and prompt testing and evaluation
Provides structured patterns for system prompts, few-shot examples, and chain-of-thought reasoning with explicit anti-patterns and sharp edges to avoid
Emphasizes systematic evaluation a
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionprompt-engineerExecute the skills CLI command in your project's root directory to begin installation:
Fetches prompt-engineer from davila7/claude-code-templates and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate prompt-engineer. Access via /prompt-engineer in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Role: LLM Prompt Architect
I translate intent into instructions that LLMs actually follow. I know that prompts are programming - they need the same rigor as code. I iterate relentlessly because small changes have big effects. I evaluate systematically because intuition about prompt quality is often wrong.
Well-organized system prompt with clear sections
- Role: who the model is
- Context: relevant background
- Instructions: what to do
- Constraints: what NOT to do
- Output format: expected structure
- Examples: demonstration of correct behavior
Include examples of desired behavior
- Show 2-5 diverse examples
- Include edge cases in examples
- Match example difficulty to expected inputs
- Use consistent formatting across examples
- Include negative examples when helpful
Request step-by-step reasoning
- Ask model to think step by step
- Provide reasoning structure
- Request explicit intermediate steps
- Parse reasoning separately from answer
- Use for debugging model failures
| Issue | Severity | Solution |
|---|---|---|
| Using imprecise language in prompts | high | Be explicit: |
| Expecting specific format without specifying it | high | Specify format explicitly: |
| Only saying what to do, not what to avoid | medium | Include explicit don'ts: |
| Changing prompts without measuring impact | medium | Systematic evaluation: |
| Including irrelevant context 'just in case' | medium | Curate context: |
| Biased or unrepresentative examples | medium | Diverse examples: |
| Using default temperature for all tasks | medium | Task-appropriate temperature: |
| Not considering prompt injection in user input | high | Defend against injection: |
Works well with: ai-agents-architect, rag-engineer, backend, product-manager
Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
davila7/claude-code-templates
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
ailabs-393/ai-labs-claude-skills
Registry listing for prompt-engineer matched our evaluation — installs cleanly and behaves as described in the markdown.
Keeps context tight: prompt-engineer is the kind of skill you can hand to a new teammate without a long onboarding doc.
prompt-engineer has been reliable in day-to-day use. Documentation quality is above average for community skills.
Solid pick for teams standardizing on skills: prompt-engineer is focused, and the summary matches what you get after install.
prompt-engineer is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
prompt-engineer fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
We added prompt-engineer from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Solid pick for teams standardizing on skills: prompt-engineer is focused, and the summary matches what you get after install.
prompt-engineer has been reliable in day-to-day use. Documentation quality is above average for community skills.
prompt-engineer reduced setup friction for our internal harness; good balance of opinion and flexibility.
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