Klingai Known Pitfalls

What it does

When your agent uses it

• Troubleshooting
• Learning best practices
• With phrases like klingai pitfalls
• Kling ai mistakes

Example prompts

• “klingai pitfalls”
• “kling ai mistakes”
• “klingai gotchas”
• “/klingai-known-pitfalls”

Requirements

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

Workflow steps

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

1. 1Reproduce pitfalls with a single watermarked sandbox draft; do not use private, unlicensed, or policy-restricted materials.
2. 2Check authentication, model/mode, duration, policy/rights status, credit use, destination, and retention before applying a workaround.
3. 3Stop and remove the draft if a workaround introduces policy, attribution, quality, budget, or retention drift.
4. 4Record the remediation as a redacted receipt and restore the known-good configuration before further testing.

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

  No URLs in SKILL.md.

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

• Credentials

  Names no API keys, tokens, secrets or passwords.

  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 Known Pitfalls loads about 1.8k tokens when it runs, and up to ~3.8k if it reads all its reference files. Until then it costs about 59 tokens; SKILL.md has 524 words of instructions outside code blocks.

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

~59

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

~1.8k

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

~3.8k

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). 524 words, ~1,781 tokens.

name

klingai-known-pitfalls

description

Avoid common mistakes when using Kling AI API. Use when troubleshooting or learning best practices. Trigger with phrases like 'klingai pitfalls', 'kling ai mistakes', 'klingai gotchas', 'klingai best practices'.

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, troubleshooting, best-practices

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

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

# 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"})

# 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()}"}

# 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")

# 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)

# 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..."}

# 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)

# 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"

© 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 3 other files (references) in skills/.curated/klingai-known-pitfalls of jeremylongshore/tons-of-skills-marketplace.

• SKILL.md
• references/common-pitfalls.md
• references/errors.md
• references/examples.md

Open the folder on GitHubat commit cfae287