kling-3-prompting▌
aedev-tools/kling-3-prompting-skill · updated May 18, 2026
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Kling 3.0 is a unified multimodal video model. It understands cinematic direction, not keyword lists. Write prompts like a director — describe what the audience sees, hears, and feels over time.
Overview
Kling 3.0 is a unified multimodal video model. It understands cinematic direction, not keyword lists. Write prompts like a director — describe what the audience sees, hears, and feels over time.
Core shift: Description → Direction. Think "direct a scene" not "describe an image."
Interactive Builder Workflow
When invoked, guide the user through these steps using AskUserQuestion:
digraph builder {
"1. Generation mode?" [shape=diamond];
"Text-to-Video" [shape=box];
"Image-to-Video" [shape=box];
"Multi-Shot Sequence" [shape=box];
"Keyframe Transition" [shape=box];
"2. Gather scene details" [shape=box];
"3. Assemble prompt" [shape=box];
"4. Present & refine" [shape=box];
"1. Generation mode?" -> "Text-to-Video";
"1. Generation mode?" -> "Image-to-Video";
"1. Generation mode?" -> "Multi-Shot Sequence";
"1. Generation mode?" -> "Keyframe Transition";
"Text-to-Video" -> "2. Gather scene details";
"Image-to-Video" -> "2. Gather scene details";
"Multi-Shot Sequence" -> "2. Gather scene details";
"Keyframe Transition" -> "2. Gather scene details";
"2. Gather scene details" -> "3. Assemble prompt";
"3. Assemble prompt" -> "4. Present & refine";
}
Step 1: Determine Generation Mode
Ask the user which mode:
- Text-to-Video — prompt from scratch
- Image-to-Video — animate a reference image
- Multi-Shot Sequence — 2-6 shot storyboard (up to 15s)
- Keyframe Transition — start frame → end frame with interpolated motion
Step 2: Gather Scene Details
Ask about each element (adapt questions to mode):
| Element | Question | Why it matters |
|---|---|---|
| Subject | Who/what is the focus? Specific appearance details? | Anchors consistency — define distinguishing traits early |
| Action | What happens? Describe the timeline (first → then → finally) | Kling 3.0 excels at sequential action over 15s arcs |
| Environment | Where? Be specific (not "a street" but "narrow Tokyo alley, steam from grates") | Grounds the scene physically |
| Camera | Shot type and movement? (See camera reference below) | Cinematic language produces far better results |
| Lighting | What light sources? Name them specifically | "Flickering neon" beats "dramatic lighting" |
| Mood/Emotion | What should the audience feel? | Drives color grade, pacing, music |
| Audio | Dialogue? Ambient sound? Music? | Kling 3.0 generates native audio + lip-sync |
| Duration | How long? (3-15s) | Longer = describe progression over time |
| Aspect Ratio | 16:9 / 9:16 / 1:1 / 21:9? | 16:9 cinematic, 9:16 social, 21:9 ultra-wide |
Image-to-Video: Focus on how the scene evolves from the image — movement, camera motion, environmental change. The model preserves identity/layout from the source.
Keyframes: Ask for start and end frame descriptions. Frames should match in color, style, and lighting. Prompt sparingly — Kling infers motion well.
Multi-Shot: Define each shot separately with its own framing, subject, action, and duration. Label shots explicitly.
Step 3: Assemble the Prompt
Use the Master Formula:
[Scene/Environment] + [Subject & Appearance] + [Action Timeline] + [Camera Movement] + [Audio & Atmosphere] + [Technical Specs]
Writing rules:
- Use cinematic motion verbs: dolly push, whip-pan, crash zoom, rack focus, tracking shot — NOT "moves" or "goes"
- Name real light sources: neon signs, candlelight, golden hour, LED panels — NOT "dramatic lighting"
- Include texture for credibility: grain, lens flares, condensation, fabric sheen, smoke, sweat
- Describe temporal flow: beginning → middle → end
- Keep to 1-3 rich sentences per shot (specificity > length)
- For dialogue: use character labels, assign voice tone/emotion, use transitional words ("Immediately," "Pause")
Step 4: Present & Refine
Present the assembled prompt. Ask if they want to:
- Adjust any element
- Add a negative prompt
- Generate variations (different duration, different camera, different mood)
Quick Reference
Camera Movements
| Movement | Effect | Example phrase |
|---|---|---|
| Dolly push-in | Builds intimacy/tension | "slow dolly push-in toward her face" |
| Dolly zoom | Vertigo/dramatic reveal | "dolly zoom creating disorienting depth shift" |
| Tracking shot | Follows subject laterally | "camera tracks alongside as she walks" |
| Whip-pan | Energy/surprise | "whip-pan to reveal the door" |
| Crash zoom | Shock/emphasis | "sudden crash zoom on the object" |
| Rack focus | Shift attention | "rack focus from foreground hand to background figure" |
| Handheld/shoulder-cam | Raw/documentary feel | "handheld shoulder-cam with subtle sway" |
| Static tripod | Composed/observational | "locked-off static tripod, wide shot" |
| FPV drone | High-energy immersion | "dynamic FPV drone shot chasing through corridor" |
| Low-angle tracking | Heroic/imposing | "low-angle tracking shot, subject towers above" |
| Truck left/right | Lateral reveal | "camera trucks right revealing the cityscape" |
| Tilt up/down | Vertical reveal | "slow tilt up from boots to face" |
Lens & Film Stock
| Phrase | Effect |
|---|---|
| "Shot on 35mm film" | Warm grain, organic texture |
| "Macro 85mm lens" | Tight detail, shallow depth of field |
| "Wide-angle steadicam" | Smooth, immersive, spatial |
| "Handheld camcorder" | Raw VHS energy, nostalgic |
| "Anamorphic lens flare" | Cinematic horizontal streaks |
Lighting
Use specific sources, not adjectives:
- "Golden hour sun cutting through dusty warehouse windows"
- "Flickering neon casting magenta/cyan across wet pavement"
- "Single bare bulb swinging, casting moving shadows"
- "Cool blue LED panels reflecting off glass surfaces"
- "Candlelight warming skin tones, deep shadows beyond"
Color & Grade
- "Desaturated teal grade, crushed blacks"
- "Amber nightclub strobe cutting through smoke"
- "Cool blue haze filling the corridor"
- "Magenta neon reflecting off wet asphalt"
- "Overexposed highlights, blown-out whites"
Multi-Character Dialogue
| Rule | Do | Don't |
|---|---|---|
| Name characters | [Character A: Silver-haired CEO] |
[Man] says... |
| Anchor to action | Agent slams table. [Agent, angrily]: "Where is it?" | Just dialogue without visual action |
| Assign voice tone | [CEO, deep authoritative gravelly voice] |
Generic "says" |
| Control timing | "Immediately," "Pause," "After a beat" | Back-to-back dialogue without transitions |
Multi-Shot Structure
Shot 1 (0-5s): [Wide establishing shot description]
Shot 2 (5-10s): [Medium/close-up with action progression]
Shot 3 (10-15s): [Resolution/reaction with camera payoff]
Atmosphere: [Overall mood, color grade]
Audio: [Sound design, music, dialogue]
Label every shot. Assign durations. Describe framing + subject + motion per shot.
Start & End Frame Tips
- Frames should match in color palette, style, and lighting
- Identical start/end frames = seamless loop
- Prompt sparingly — Kling infers motion between frames well
- Simple camera directions: zoom in/out, pan left/right, tilt up/down
- 5s for dynamic transitions, 10s for complex transformations
- Start frame aspect ratio drives the whole clip
Negative Prompts
Use to prevent common AI defaults:
smiling, laughing, cartoonish, bright saturated colors, low resolution,
morphing, blurry text, disfigured hands, extra fingers, static pose,
frozen expression, stock photo aesthetic
Customize based on scene — remove items that conflict with your intent.
Weak → Strong
| Element | Weak | Strong |
|---|---|---|
| Camera | "Camera follows person" | "Handheld shoulder-cam drifts behind subject with subtle sway" |
| Subject | "A woman walking" | "Woman in red dress, heels clicking wet cobblestone" |
| Environment | "In a city" | "Narrow Tokyo alley, steam from grates, glowing vending machines" |
| Lighting | "Dramatic lighting" | "Flickering neon casting magenta/cyan across wet pavement" |
| Texture | "It looks realistic" | "Rain beading on leather jacket, condensation on glass, visible breath" |
| Motion | "She walks away" | "She turns slowly, hair catches light, disappears around corner" |
Common Mistakes
| Mistake | Fix |
|---|---|
| Keyword lists instead of scene direction | Write like directing a shot: subject + action + camera + environment |
| Vague motion ("moves," "goes") | Use cinematic verbs: dolly, track, whip-pan, crash zoom |
| Generic lighting ("dramatic") | Name the source: neon, candle, golden hour, LED panel |
| Overlong prompts | 1-3 rich sentences per shot; specificity > length |
| No temporal progression | Describe beginning → middle → end of the shot |
| Mismatched keyframes | Match color, lighting, and style between start/end frames |
| Unattributed dialogue | Label every speaker with name, tone, and emotion |
| Cramming multi-shot into one paragraph | Separate and label each shot with duration |
How to use kling-3-prompting on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add kling-3-prompting
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches kling-3-prompting from GitHub repository aedev-tools/kling-3-prompting-skill and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate kling-3-prompting. Access the skill through slash commands (e.g., /kling-3-prompting) or your agent's skill management interface.
Security & Verification Notice
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 development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
User Story & Requirements Generation
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
Competitive Analysis
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
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
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
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ 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.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.8★★★★★32 reviews- ★★★★★Pratham Ware· Dec 28, 2024
Keeps context tight: kling-3-prompting is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Yuki Sethi· Dec 20, 2024
kling-3-prompting fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Sakshi Patil· Nov 19, 2024
Registry listing for kling-3-prompting matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Chaitanya Patil· Oct 10, 2024
kling-3-prompting reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Oshnikdeep· Sep 25, 2024
We added kling-3-prompting from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Anika Verma· Sep 21, 2024
Keeps context tight: kling-3-prompting is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Evelyn Martinez· Sep 17, 2024
kling-3-prompting is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Piyush G· Sep 1, 2024
I recommend kling-3-prompting for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Shikha Mishra· Aug 20, 2024
Useful defaults in kling-3-prompting — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Ganesh Mohane· Aug 16, 2024
kling-3-prompting fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
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