Techniques and patterns for crafting effective prompts across LLMs, image generators, and video models.
Works with
Covers LLM prompting fundamentals: role assignment, task clarity, chain-of-thought reasoning, few-shot examples, output format specification, and constraint setting
Image generation structure includes subject description, style keywords, composition control, quality modifiers, and negative prompt usage
Video prompting guidance covers shot types, camera movement, action description,
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionprompt-engineeringExecute the skills CLI command in your project's root directory to begin installation:
Fetches prompt-engineering from inferen-sh/skills 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-engineering. Access via /prompt-engineering 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
0
total installs
0
this week
304
GitHub stars
0
upvotes
Run in your terminal
0
installs
0
this week
304
stars
Master prompt engineering for AI models via inference.sh CLI.

Requires inference.sh CLI (
infsh). Install instructions
infsh login
# Well-structured LLM prompt
infsh app run openrouter/claude-sonnet-45 --input '{
"prompt": "You are a senior software engineer. Review this code for security vulnerabilities:\n\n```python\nuser_input = request.args.get(\"query\")\nresult = db.execute(f\"SELECT * FROM users WHERE name = {user_input}\")\n```\n\nProvide specific issues and fixes."
}'
[Role/Context] + [Task] + [Constraints] + [Output Format]
infsh app run openrouter/claude-sonnet-45 --input '{
"prompt": "You are an expert data scientist with 15 years of experience in machine learning. Explain gradient descent to a beginner, using simple analogies."
}'
# Bad: vague
"Help me with my code"
# Good: specific
"Debug this Python function that should return the sum of even numbers from a list, but returns 0 for all inputs:
def sum_evens(numbers):
total = 0
for n in numbers:
if n % 2 == 0:
total += n
return total
Identify the bug and provide the corrected code."
infsh app run openrouter/claude-sonnet-45 --input '{
"prompt": "Solve this step by step:\n\nA store sells apples for $2 each and oranges for $3 each. If someone buys 5 fruits and spends $12, how many of each fruit did they buy?\n\nThink through this step by step before giving the final answer."
}'
infsh app run openrouter/claude-sonnet-45 --input '{
"prompt": "Convert these sentences to formal business English:\n\nExample 1:\nInput: gonna send u the report tmrw\nOutput: I will send you the report tomorrow.\n\nExample 2:\nInput: cant make the meeting, something came up\nOutput: I apologize, but I will be unable to attend the meeting due to an unforeseen circumstance.\n\nNow convert:\nInput: hey can we push the deadline back a bit?"
}'
infsh app run openrouter/claude-sonnet-45 --input '{
"prompt": "Analyze the sentiment of these customer reviews. Return a JSON array with objects containing \"text\", \"sentiment\" (positive/negative/neutral), and \"confidence\" (0-1).\n\nReviews:\n1. \"Great product, fast shipping!\"\n2. \"Meh, its okay I guess\"\n3. \"Worst purchase ever, total waste of money\"\n\nReturn only valid JSON, no explanation."
}'
infsh app run openrouter/claude-sonnet-45 --input '{
"prompt": "Summarize this article in exactly 3 bullet points. Each bullet must be under 20 words. Focus only on actionable insights, not background information.\n\n[article text]"
}'
[Subject] + [Style] + [Composition] + [Lighting] + [Technical]
# Bad: vague
"a cat"
# Good: specific
infsh app run falai/flux-dev --input '{
"prompt": "A fluffy orange tabby cat with green eyes, sitting on a vintage leather armchair"
}'
infsh app run falai/flux-dev --input '{
"prompt": "Portrait photograph of a woman, shot on Kodak Portra 400 film, soft natural lighting, shallow depth of field, nostalgic mood, analog photography aesthetic"
}'
infsh app run falai/flux-dev --input '{
"prompt": "Wide establishing shot of a cyberpunk city skyline at night, rule of thirds composition, neon signs in foreground, towering skyscrapers in background, rain-slicked streets"
}'
photorealistic, 8K, ultra detailed, sharp focus, professional,
masterpiece, high quality, best quality, intricate details
infsh app run falai/flux-dev --input '{
"prompt": "Professional headshot portrait, clean background",
"negative_prompt": "blurry, distorted, extra limbs, watermark, text, low quality, cartoon, anime"
}'
[Shot Type] + [Subject] + [Action] + [Setting] + [Style]
infsh app run google/veo-3-1-fast --input '{
"prompt": "Slow tracking shot following a woman walking through a sunlit forest, golden hour lighting, shallow depth of field, cinematic, 4K"
}'
infsh app run google/veo-3-1-fast --input '{
"prompt": "Close-up of hands kneading bread dough on a wooden surface, flour dust floating in morning light, slow motion, cozy baking aesthetic"
}'
slow motion, timelapse, real-time, smooth motion,
continuous shot, quick cuts, frozen moment
infsh app run openrouter/claude-sonnet-45 --input '{
"system": "You are a helpful coding assistant. Always provide code with comments. If you are unsure about something, say so rather than guessing.",
"prompt": "Write a Python function to validate email addresses using regex."
}'
infsh app run openrouter/claude-sonnet-45 --input '{
"prompt": "Extract information from this text and return as JSON:\n\n\"John Smith, CEO of TechCorp, announced yesterday that the company raised $50 million in Series B funding. The round was led by Venture Partners.\"\n\nSchema:\n{\n \"person\": string,\n \"title\": string,\n \"company\": string,\n \"event\": string,\n \"amount\": string,\n \"investor\": string\n}"
}'
# Start broad
infsh app run falai/flux-dev --input '{
"prompt": "A castle on a hill"
}'
# Add specifics
infsh app run falai/flux-dev --input '{
"prompt": "A medieval stone castle on a grassy hill"
}'
# Add style
infsh app run falai/flux-dev --input '{
"prompt": "A medieval stone castle on a grassy hill, dramatic sunset sky, fantasy art style, epic composition"
}'
# Add technical
infsh app run falai/flux-dev --input '{
"prompt": "A medieval stone castle on a grassy hill, dramatic sunset sky, fantasy art style by Greg Rutkowski, epic composition, 8K, highly detailed"
}'
# First: analyze
infsh app run openrouter/claude-sonnet-45 --input '{
"prompt": "Analyze this business problem: Our e-commerce site has a 70% cart abandonment rate. List potential causes."
}'
# Second: prioritize
infsh app run openrouter/claude-sonnet-45 --input '{
"prompt": "Given these causes of cart abandonment: [previous output], rank them by likely impact and ease of fixing. Format as a priority matrix."
}'
# Third: action plan
infsh app run openrouter/claude-sonnet-45 --input '{
"prompt": "For the top 3 causes identified, provide specific A/B tests we can run to validate and fix each issue."
}'
| Mistake | Problem | Fix |
|---|---|---|
| Too vague | Unpredictable output | Add specifics |
| Too long | Model loses focus | Prioritize key info |
| Conflicting | Confuses model | Remove contradictions |
| No format | Inconsistent output | Specify format |
| No examples | Unclear expectations | Add few-shot |
Review this [language] code for:
1. Bugs and logic errors
2. Security vulnerabilities
3. Performance issues
4. Code style/best practices
Code:
[code]
For each issue found, provide:
- Line number
- Issue description
- Severity (high/medium/low)
- Suggested fix
Write a [content type] about [topic].
Audience: [target audience]
Tone: [formal/casual/professional]
Length: [word count]
Key points to cover:
1. [point 1]
2. [point 2]
3. [point 3]
Include: [specific elements]
Avoid: [things to exclude]
[Subject with details], [setting/background], [lighting type],
[art style or photography style], [composition], [quality keywords]
# Video prompting guide
npx skills add inference-sh/skills@video-prompting-guide
# LLM models
npx skills add inference-sh/skills@llm-models
# Image generation
npx skills add inference-sh/skills@ai-image-generation
# Full platform skill
npx skills add inference-sh/skills@infsh-cli
Browse all apps: infsh app list
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.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
I recommend prompt-engineering for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Useful defaults in prompt-engineering — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
prompt-engineering is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Keeps context tight: prompt-engineering is the kind of skill you can hand to a new teammate without a long onboarding doc.
Useful defaults in prompt-engineering — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Useful defaults in prompt-engineering — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
prompt-engineering is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Keeps context tight: prompt-engineering is the kind of skill you can hand to a new teammate without a long onboarding doc.
prompt-engineering is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Useful defaults in prompt-engineering — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
showing 1-10 of 68