Klingai Image To Video

What it does

When your agent uses it

• Converting images to video
• Adding motion to stills
• Building I2V pipelines
• With phrases like klingai image to video

Example prompts

• “klingai image to video”
• “kling ai animate image”
• “klingai img2vid”
• “/klingai-image-to-video”

Requirements

• Python 3
• A credential in KLING_ACCESS_KEY
• A credential in KLING_SECRET_KEY
• Compatibility (from SKILL.md): Designed for Claude Code
• Pre-approved tools (allowed-tools): Read, Write, Edit, Bash(npm:*), Grep

Workflow steps

6 steps, taken from the first numbered list in SKILL.md.

1. 1Resolve the source and mask references from an approved allowlist; reject data from untrusted URLs, missing provenance, or assets…
2. 2Validate format, dimensions, feature mutual exclusivity, prompt length, and the requested duration before spending credits. Use a…
3. 3Deduplicate the request using a stable job key, submit only after the content-policy check passes, and keep the task and source in private…
4. 4Run one short, watermarked sandbox canary. Check motion, policy outcome, source fidelity, and the credit budget before requesting an owner…
5. 5On approval, promote the exact task result by digest. On failure or withdrawal, stop downstream publication, remove staged media and…
6. 6Record a redacted receipt containing only opaque job and asset digests, policy and approval outcomes, budget outcome, retention deadline…

What it can do on your machine

Read from SKILL.md and the folder at commit cfae287. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves these tools, so the agent can use them without asking each time:

  • Read
  • Write
  • Edit
  • Bash(npm:*)
  • Grep

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  No scripts in the folder and no shell commands in SKILL.md (its code samples are python).

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  Hosts in commands or code, which the agent is likely to contact:

  • api.klingai.com

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names these keys or tokens, usually read from environment variables:

  • KLING_ACCESS_KEY
  • KLING_SECRET_KEY

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

• Compatibility

  Designed for Claude Code

  From compatibility in the SKILL.md frontmatter.

Context cost

Klingai Image To Video loads about 2.1k tokens when it runs, and up to ~4.3k if it reads all its reference files. Until then it costs about 71 tokens; SKILL.md has 622 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~71

When it runs · the whole SKILL.md, loaded when a task matches

~2.1k

With references · SKILL.md plus every file in references/, read only if the agent opens them

~4.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from jeremylongshore/tons-of-skills-marketplace at commit cfae287, republished under its MIT licence (© jeremylongshore). 622 words, ~2,141 tokens.

name

klingai-image-to-video

description

Animate static images into video using Kling AI. Use when converting images to video, adding motion to stills, or building I2V pipelines. Trigger with phrases like 'klingai image to video', 'kling ai animate image', 'klingai img2vid', 'animate picture klingai'.

allowed-tools

Read, Write, Edit, Bash(npm:*), Grep

compatibility

Designed for Claude Code

version

1.18.0

license

MIT

author

Jeremy Longshore <jeremy@intentsolutions.io>

tags

saas, kling-ai, image-to-video, video-generation

import jwt, time, os, requests

BASE = "https://api.klingai.com/v1"

def get_headers():
    ak, sk = os.environ["KLING_ACCESS_KEY"], os.environ["KLING_SECRET_KEY"]
    token = jwt.encode(
        {"iss": ak, "exp": int(time.time()) + 1800, "nbf": int(time.time()) - 5},
        sk, algorithm="HS256", headers={"alg": "HS256", "typ": "JWT"}
    )
    return {"Authorization": f"Bearer {token}", "Content-Type": "application/json"}

# Animate a landscape photo
response = requests.post(f"{BASE}/videos/image2video", headers=get_headers(), json={
    "model_name": "kling-v2-1",
    "image": "https://example.com/landscape.jpg",
    "prompt": "Clouds slowly drifting across the sky, gentle wind rustling through trees",
    "negative_prompt": "static, frozen, blurry",
    "duration": "5",
    "mode": "standard",
})

task_id = response.json()["data"]["task_id"]

# Poll for result
while True:
    time.sleep(15)
    result = requests.get(
        f"{BASE}/videos/image2video/{task_id}", headers=get_headers()
    ).json()
    if result["data"]["task_status"] == "succeed":
        print(f"Video: {result['data']['task_result']['videos'][0]['url']}")
        break
    elif result["data"]["task_status"] == "failed":
        raise RuntimeError(result["data"]["task_status_msg"])

response = requests.post(f"{BASE}/videos/image2video", headers=get_headers(), json={
    "model_name": "kling-v2-master",
    "image": "https://example.com/sunrise.jpg",        # first frame
    "image_tail": "https://example.com/sunset.jpg",    # last frame
    "prompt": "Time lapse of sun moving across the sky",
    "duration": "5",
    "mode": "professional",
})

response = requests.post(f"{BASE}/videos/image2video", headers=get_headers(), json={
    "model_name": "kling-v2-6",
    "image": "https://example.com/person-standing.jpg",
    "prompt": "Person walking forward naturally",
    "duration": "5",
    "dynamic_masks": [
        {
            "mask": "https://example.com/person-mask.png",  # white = selected region
            "trajectories": [
                {"x": 0.5, "y": 0.7, "t": 0.0},   # start position (normalized 0-1)
                {"x": 0.5, "y": 0.5, "t": 0.5},   # midpoint
                {"x": 0.5, "y": 0.3, "t": 1.0},   # end position
            ]
        }
    ],
})

response = requests.post(f"{BASE}/videos/image2video", headers=get_headers(), json={
    "model_name": "kling-v2-master",
    "image": "https://example.com/scene.jpg",
    "prompt": "Water flowing in the river, birds flying",
    "duration": "5",
    "static_mask": "https://example.com/buildings-mask.png",  # white = frozen
})

source=fixture:synthetic-landscape-v3; rights=cleared; mode=standard;
duration=5; canary=watermarked-sandbox; policy=pass; approval=pending;
publish=false; contacts_exported=0; receipt=asset-sha256:opaque

© jeremylongshore, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 5 other files (references) in skills/.curated/klingai-image-to-video of jeremylongshore/tons-of-skills-marketplace.

• SKILL.md
• references/batch-image-processing.md
• references/errors.md
• references/examples.md
• references/image-to-video-generation.md
• references/motion-control-options.md

Open the folder on GitHubat commit cfae287