Claude-code-plugins klingai-known-pitfalls

install
source · Clone the upstream repo
git clone https://github.com/jeremylongshore/claude-code-plugins-plus-skills
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/jeremylongshore/claude-code-plugins-plus-skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/plugins/saas-packs/klingai-pack/skills/klingai-known-pitfalls" ~/.claude/skills/jeremylongshore-claude-code-plugins-klingai-known-pitfalls && rm -rf "$T"
manifest: plugins/saas-packs/klingai-pack/skills/klingai-known-pitfalls/SKILL.md
source content

Kling AI Known Pitfalls

Overview

Documented mistakes, gotchas, and anti-patterns from real Kling AI integrations. Each pitfall includes the symptom, root cause, and tested fix.

Pitfall 1: Duration as Integer

Symptom:

400 Bad Request
on valid-looking requests.

# WRONG -- duration as integer
{"duration": 5}

# CORRECT -- duration as string
{"duration": "5"}

The API requires

duration
as a string
"5"
or
"10"
, not an integer.

Pitfall 2: JWT Without Explicit Headers

Symptom:

401 Unauthorized
even with correct AK/SK.

# WRONG -- missing headers parameter
token = jwt.encode(payload, sk, algorithm="HS256")

# CORRECT -- explicit JWT headers
token = jwt.encode(payload, sk, algorithm="HS256",
                   headers={"alg": "HS256", "typ": "JWT"})

Some JWT libraries don't include

typ: "JWT"
by default. Kling requires it.

Pitfall 3: Token Generated Once at Import Time

Symptom: Works for 30 minutes, then all requests fail with

401
.

# WRONG -- token generated once
TOKEN = generate_token()  # at module import
headers = {"Authorization": f"Bearer {TOKEN}"}

# CORRECT -- generate fresh token per request (or auto-refresh)
def get_headers():
    return {"Authorization": f"Bearer {generate_token()}"}

JWT tokens expire after 30 minutes. Always implement auto-refresh.

Pitfall 4: Polling Without Timeout

Symptom: Script hangs forever on a failed task.

# WRONG -- infinite loop
while True:
    result = check_status(task_id)
    if result["status"] == "succeed":
        break
    time.sleep(10)

# CORRECT -- with timeout and failure check
start = time.monotonic()
while time.monotonic() - start < 600:  # 10 min max
    result = check_status(task_id)
    if result["status"] == "succeed":
        break
    elif result["status"] == "failed":
        raise RuntimeError(result["error"])
    time.sleep(10)
else:
    raise TimeoutError("Generation timed out")

Pitfall 5: Not Downloading Videos Promptly

Symptom: Video URLs return

404
or
403
after a day.

Kling CDN URLs are temporary (24-72 hours). Always download and store on your own infrastructure immediately after generation completes.

# WRONG -- storing only the Kling URL
db.save(video_url=kling_cdn_url)  # will expire

# CORRECT -- download and rehost
local_path = download_video(kling_cdn_url)
permanent_url = upload_to_s3(local_path, bucket)
db.save(video_url=permanent_url)

Pitfall 6: Mixing Mutually Exclusive Features (I2V)

Symptom:

400 Bad Request
on image-to-video with multiple features.

These are mutually exclusive for image-to-video:

  • camera_control
  • dynamic_masks
    /
    static_mask
  • image_tail

You can only use ONE group per request.

Pitfall 7: Wrong Model for Text-to-Video

Symptom:

400
or unexpected behavior.

# WRONG -- kling-v2-1 is I2V-only
{"model_name": "kling-v2-1", "prompt": "A sunset..."}  # fails

# CORRECT -- use models that support T2V
{"model_name": "kling-v2-master", "prompt": "A sunset..."}
{"model_name": "kling-v2-5-turbo", "prompt": "A sunset..."}

Check the model catalog:

kling-v1-5
and
kling-v2-1
support image-to-video only.

Pitfall 8: No Error Handling on Task Status

Symptom: Silent failures, missing videos.

# WRONG -- only check for success
if result["task_status"] == "succeed":
    process(result)
# silently ignores failures

# CORRECT -- handle all terminal states
if result["task_status"] == "succeed":
    process(result)
elif result["task_status"] == "failed":
    log_failure(result["task_status_msg"])
    retry_or_alert(task_id)

Pitfall 9: Ignoring Credit Costs with Audio

Symptom: Credits depleted 5x faster than expected.

Native audio (v2.6,

motion_has_audio: true
) multiplies credit cost by 5x:

  • 5s standard without audio: 10 credits
  • 5s standard WITH audio: 50 credits

Always check

motion_has_audio
in cost estimates.

Pitfall 10: Vague Prompts

Symptom: Low-quality, incoherent video output.

# WEAK -- too vague
"A nice video of nature"

# STRONG -- specific and descriptive
"Close-up of a monarch butterfly landing on a lavender flower, "
"soft bokeh background, golden hour lighting, macro lens, 4K"

Good prompts: specific subject, clear action, lighting, camera angle, style.

Quick Reference

PitfallFix
Duration as intUse string:
"5"
JWT headers missingAdd
headers={"alg":"HS256","typ":"JWT"}
Token not refreshedAuto-refresh with 5-min buffer
No poll timeoutMax 600s with failure check
Kling URLs as permanentDownload and rehost immediately
Mixed I2V featuresOne feature group per request
Wrong model for T2VCheck model supports text-to-video
No failure handlingCheck for
"failed"
status
Audio cost surprise5x multiplier with
motion_has_audio
Vague promptsSpecific subject, action, style, lighting

Resources