Agent skill

Klingai Debug Bundle

by jeremylongshore in jeremylongshore/tons-of-skills-marketplace

Set up logging and debugging for Kling AI API integrations. An agent skill from jeremylongshore/tons-of-skills-marketplace.

MITAuto-check passedMedia & Creative

Install Klingai Debug Bundle

skills CLI
$ npx skills add jeremylongshore/tons-of-skills-marketplace --skill klingai-debug-bundle -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install jeremylongshore/tons-of-skills-marketplace klingai-debug-bundle --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/jeremylongshore/tons-of-skills-marketplace.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/.curated/klingai-debug-bundle .claude/skills/klingai-debug-bundle && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
klingai-debug-bundle
GitHub stars
2.8k
Token cost
~2.5k tokens
SKILL.md length
421 words
Files
7 (incl. references)
Skills in repo
3,342
Repo updated
First seen
Licence
MIT

At a glance

Set up logging and debugging for Kling AI API integrations. An agent skill from jeremylongshore/tons-of-skills-marketplace.

  • Works in 5 steps: Reproduce only with a synthetic or… → Apply redaction to request bodies,… → Use bounded polling and retry budgets.… → …
  • Troubleshooting video generation
  • SKILL.md covers Overview, Debug-Enabled Client, Usage and Structured Log Entry Format, plus 8 more sections
  • Calls python3; reaches api.klingai.com; needs KLING_ACCESS_KEY and KLING_SECRET_KEY

What it does

Klingai Debug Bundle is an agent skill from jeremylongshore/tons-of-skills-marketplace. Set up logging and debugging for Kling AI API integrations. Use when troubleshooting video generation or building observability. Trigger with phrases like 'klingai debug', 'kling ai logging', 'klingai troubleshoot', 'debug kling video generation'.

Its SKILL.md is about 2.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 7 other files, including reference files (for example `references/debug-utilities.md`, `references/errors.md` and `references/examples.md`). Compatibility notes: Designed for Claude Code

It sits in Media & Creative, covering AI video generation, Debugging and Third-party API integration. The repository describes itself as: Model-agnostic agent-skills platform with a harness-free canonical layer, verified adapters, and the ccpi package manager. Explore at tonsofskills.com. The licence is MIT.

When your agent uses it

  • Troubleshooting video generation
  • Building observability
  • With phrases like klingai debug
  • Kling ai logging

Example prompts

  • “klingai debug”
  • “kling ai logging”
  • “klingai troubleshoot”
  • “/klingai-debug-bundle”

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

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

  1. Reproduce only with a synthetic or rights-cleared fixture in staging, and assign a correlation ID before making a request.
  2. Apply redaction to request bodies, response bodies, exception text, headers, URLs, prompts, and generated-media references before logging…
  3. Use bounded polling and retry budgets. Do not replay a policy rejection, exceed the approved credit budget, or replay a request against an…
  4. Keep diagnostic output private and watermarked until the incident owner approves it. Quarantine generated media, remove temporary links…
  5. Verify log deletion at the retention deadline and retain only the aggregate, redacted receipt needed for incident follow-up.

What it can do on your machine

Read from SKILL.md and the folder at commit 80f86df. 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

    Shell commands in SKILL.md call:

    • python3

    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 Debug Bundle loads about 2.5k tokens when it runs, and up to ~4.7k if it reads all its reference files. Until then it costs about 67 tokens; SKILL.md has 421 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~67
When it runs · the whole SKILL.md, loaded when a task matches
~2.5k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~4.7k

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

Auto-check passed

The automated check found no risky patterns in SKILL.md.

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 80f86df, republished under its MIT licence (© jeremylongshore). 421 words, ~2,484 tokens.

Download SKILL.mdSave it as .claude/skills/klingai-debug-bundle/SKILL.md (or your agent's skills folder). This skill also uses 6 other files; get the full folder from GitHub.
name
klingai-debug-bundle
description
Set up logging and debugging for Kling AI API integrations. Use when troubleshooting video generation or building observability. Trigger with phrases like 'klingai debug', 'kling ai logging', 'klingai troubleshoot', 'debug kling video generation'.
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, debugging, observability

Kling AI Debug Bundle

Overview

Structured logging, request tracing, and diagnostic tools for Kling AI API integrations. Captures request/response pairs, task lifecycle events, and timing metrics for every call to https://api.klingai.com/v1.

Debug-Enabled Client

python
import jwt, time, os, requests, logging, json
from datetime import datetime

logging.basicConfig(
    level=logging.DEBUG,
    format="%(asctime)s [%(levelname)s] %(name)s: %(message)s"
)
logger = logging.getLogger("kling.debug")

class KlingDebugClient:
    """Kling AI client with full request/response logging."""

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

    def __init__(self):
        self.ak = os.environ["KLING_ACCESS_KEY"]
        self.sk = os.environ["KLING_SECRET_KEY"]
        self._request_log = []

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

    def _traced_request(self, method, path, body=None):
        """Execute request with full tracing."""
        url = f"{self.BASE}{path}"
        start = time.monotonic()
        trace = {
            "timestamp": datetime.utcnow().isoformat(),
            "method": method,
            "path": path,
            "request_body": body,
        }

        try:
            if method == "POST":
                r = requests.post(url, headers=self._get_headers(), json=body, timeout=30)
            else:
                r = requests.get(url, headers=self._get_headers(), timeout=30)

            trace["status_code"] = r.status_code
            trace["response_body"] = r.json() if r.content else None
            trace["duration_ms"] = round((time.monotonic() - start) * 1000)

            logger.debug(f"{method} {path} -> {r.status_code} ({trace['duration_ms']}ms)")

            if r.status_code >= 400:
                logger.error(f"API error: {r.status_code} -- {r.text[:300]}")

            r.raise_for_status()
            return r.json()

        except Exception as e:
            trace["error"] = str(e)
            trace["duration_ms"] = round((time.monotonic() - start) * 1000)
            logger.exception(f"Request failed: {path}")
            raise
        finally:
            self._request_log.append(trace)

    def text_to_video(self, prompt, **kwargs):
        body = {
            "model_name": kwargs.get("model", "kling-v2-master"),
            "prompt": prompt,
            "duration": str(kwargs.get("duration", 5)),
            "mode": kwargs.get("mode", "standard"),
        }
        result = self._traced_request("POST", "/videos/text2video", body)
        task_id = result["data"]["task_id"]
        logger.info(f"Task created: {task_id}")
        return self._poll_with_logging("/videos/text2video", task_id)

    def _poll_with_logging(self, endpoint, task_id, max_attempts=120):
        start = time.monotonic()
        for attempt in range(max_attempts):
            time.sleep(10)
            result = self._traced_request("GET", f"{endpoint}/{task_id}")
            status = result["data"]["task_status"]
            elapsed = round(time.monotonic() - start)
            logger.info(f"Poll #{attempt + 1}: status={status}, elapsed={elapsed}s")

            if status == "succeed":
                logger.info(f"Task {task_id} completed in {elapsed}s")
                return result["data"]["task_result"]
            elif status == "failed":
                msg = result["data"].get("task_status_msg", "Unknown")
                logger.error(f"Task {task_id} failed after {elapsed}s: {msg}")
                raise RuntimeError(msg)

        raise TimeoutError(f"Task {task_id} timed out after {max_attempts * 10}s")

    def dump_log(self, filepath="kling_debug.json"):
        with open(filepath, "w") as f:
            json.dump(self._request_log, f, indent=2, default=str)
        logger.info(f"Debug log written to {filepath} ({len(self._request_log)} entries)")

Usage

python
client = KlingDebugClient()
try:
    result = client.text_to_video("A cat surfing ocean waves at sunset")
    print(f"Video: {result['videos'][0]['url']}")
except Exception:
    pass
finally:
    client.dump_log()  # always save debug log

Structured Log Entry Format

json
{
  "timestamp": "2026-03-22T10:30:00.000Z",
  "method": "POST",
  "path": "/videos/text2video",
  "request_body": {"model_name": "kling-v2-master", "prompt": "..."},
  "status_code": 200,
  "response_body": {"code": 0, "data": {"task_id": "abc123"}},
  "duration_ms": 342
}

Quick Diagnostic Script

bash
#!/bin/bash
# kling-diag.sh
echo "=== Kling AI Diagnostics ==="
echo "KLING_ACCESS_KEY: ${KLING_ACCESS_KEY:+set (${#KLING_ACCESS_KEY} chars)}"
echo "KLING_SECRET_KEY: ${KLING_SECRET_KEY:+set (${#KLING_SECRET_KEY} chars)}"

python3 -c "
import jwt, time, os, requests
ak = os.environ.get('KLING_ACCESS_KEY', '')
sk = os.environ.get('KLING_SECRET_KEY', '')
if not ak or not sk: print('ERROR: Missing credentials'); exit(1)
token = jwt.encode({'iss': ak, 'exp': int(time.time())+1800, 'nbf': int(time.time())-5},
                   sk, algorithm='HS256', headers={'alg':'HS256','typ':'JWT'})
r = requests.get('https://api.klingai.com/v1/videos/text2video',
                  headers={'Authorization': f'Bearer {token}'}, timeout=10)
print(f'Auth test: HTTP {r.status_code}')
if r.status_code == 401: print('Fix: Check AK/SK values')
elif r.status_code in (200, 400): print('Auth OK')
"

Task Inspector

python
def inspect_task(client, endpoint, task_id):
    """Print detailed task information."""
    result = client._traced_request("GET", f"{endpoint}/{task_id}")
    data = result["data"]
    print(f"Task ID:     {data['task_id']}")
    print(f"Status:      {data['task_status']}")
    print(f"Created:     {data.get('created_at', 'N/A')}")
    if data["task_status"] == "succeed":
        for i, video in enumerate(data["task_result"]["videos"]):
            print(f"Video [{i}]:   {video['url']}")
    elif data["task_status"] == "failed":
        print(f"Error:       {data.get('task_status_msg', 'No message')}")

Prerequisites

  • A non-production or approved staging account, a bounded diagnostic budget, and synthetic or rights-cleared media for reproduction. Do not debug with customer images or identifiable people unless the incident owner has documented consent.
  • A secret manager for Kling credentials and a redaction policy covering JWTs, access keys, source URLs, prompts, masks, output URLs, and provider response bodies.
  • A private, access-controlled log sink with a short retention period, plus an owner-approved canary and rollback procedure before any replay or regeneration.

Instructions

  1. Reproduce only with a synthetic or rights-cleared fixture in staging, and assign a correlation ID before making a request.
  2. Apply redaction to request bodies, response bodies, exception text, headers, URLs, prompts, and generated-media references before logging or exporting a bundle.
  3. Use bounded polling and retry budgets. Do not replay a policy rejection, exceed the approved credit budget, or replay a request against an unapproved destination.
  4. Keep diagnostic output private and watermarked until the incident owner approves it. Quarantine generated media, remove temporary links, and restore the prior approved artifact if a replay changes state.
  5. Verify log deletion at the retention deadline and retain only the aggregate, redacted receipt needed for incident follow-up.
Show full SKILL.md (180 more words)Show less

Output

Produce a redacted diagnostic bundle containing a correlation ID, endpoint path, HTTP status, latency, retry count, opaque task ID, policy result, budget result, and a hash of relevant fixtures. Include a scrubbed error class and rollback reference; never include credentials, bearer tokens, source or CDN URLs, prompts, faces, contact data, or raw request/response bodies.

Error Handling

Classify failures as authentication, validation, policy, quota/budget, transport, provider-task, or storage failures. Redact before persisting or printing any exception, cap retries with exponential backoff, and stop replay when a request is billable, policy-rejected, or outside the approved fixture scope. Quarantine generated media, revoke temporary access, delete debug artifacts at the retention deadline, and restore the last approved output when a replay changes production state. Escalate an unknown provider status with the correlation ID instead of exposing raw payloads.

Examples

Use a synthetic fixture and a private canary when collecting a receipt:

json
{
  "correlation_id": "trace-opaque-42",
  "fixture_sha256": "sha256:opaque",
  "task_id": "task-redacted",
  "status": "failed",
  "failure_class": "policy",
  "canary": "watermarked-private",
  "budget": "within-limit",
  "retention": "24h",
  "rollback": "release-r31"
}

Before enabling verbose tracing, verify that redaction is applied to both successful and failed paths; a diagnostic run is never permission to publish or retain generated media.

Resources

© 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 6 other files (references) in skills/.curated/klingai-debug-bundle of jeremylongshore/tons-of-skills-marketplace.

  • SKILL.md
  • references/debug-utilities.md
  • references/errors.md
  • references/examples.md
  • references/logging-setup.md
  • references/performance-metrics.md
  • references/request-tracing.md

Open the folder on GitHubat commit 80f86df

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Questions about Klingai Debug Bundle

What does Klingai Debug Bundle do?

Set up logging and debugging for Kling AI API integrations. An agent skill from jeremylongshore/tons-of-skills-marketplace. Klingai Debug Bundle is an agent skill from jeremylongshore/tons-of-skills-marketplace. Set up logging and debugging for Kling AI API integrations.

When should I use Klingai Debug Bundle?

Klingai Debug Bundle fits situations like: troubleshooting video generation; building observability; with phrases like klingai debug; kling ai logging.

How do I install Klingai Debug Bundle in Claude Code?

Run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill klingai-debug-bundle -a claude-code`. Or copy the skill folder (skills/.curated/klingai-debug-bundle in jeremylongshore/tons-of-skills-marketplace) into .claude/skills/klingai-debug-bundle in your project. Claude Code loads it when a task matches its description.

How do I install Klingai Debug Bundle in Codex?

Run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill klingai-debug-bundle -a codex`. Or copy the skill folder (skills/.curated/klingai-debug-bundle in jeremylongshore/tons-of-skills-marketplace) into .agents/skills/klingai-debug-bundle in your project. Codex loads it when a task matches its description.

Can I use Klingai Debug Bundle in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill klingai-debug-bundle -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/klingai-debug-bundle, .gemini/skills/klingai-debug-bundle, .github/skills/klingai-debug-bundle and .opencode/skills/klingai-debug-bundle in your project.

What does Klingai Debug Bundle need to run?

Going by SKILL.md and its folder, Klingai Debug Bundle needs the command-line tools its instructions call (python3) and credentials named KLING_ACCESS_KEY and KLING_SECRET_KEY. Our summary lists: Python 3; A credential in KLING_ACCESS_KEY; A credential in KLING_SECRET_KEY. Its frontmatter pre-approves these tools: Read, Write, Edit, Bash(npm:*), Grep. Compatibility (from SKILL.md): Designed for Claude Code.

Does Klingai Debug Bundle access the network?

SKILL.md names 1 domain. In commands or code: api.klingai.com; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.

Is Klingai Debug Bundle safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Klingai Debug Bundle use?

Klingai Debug Bundle is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Klingai Debug Bundle use?

About 2.5k tokens (SKILL.md is roughly 9.9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 2.2k tokens, read only when the agent opens those files.

What are the alternatives to Klingai Debug Bundle?

Skills that share tags, products or a category with Klingai Debug Bundle: Clipmivo Video (BarneyD66/clipmivo-tools, 142 stars), Pua Debugging En (LeoYeAI/openclaw-master-skills, 2.2k stars), Video Generation (bytedance/deer-flow, 84k stars) and Video Cover Image (itwanger/toBeBetterJavaer, 18k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Klingai Debug Bundle?

jeremylongshore (a GitHub user) maintains it in jeremylongshore/tons-of-skills-marketplace, which has 2,825 GitHub stars. The repository holds 3,342 skills in this directory. The repository was last updated on October 9, 2026.

Source: jeremylongshore/tons-of-skills-marketplace on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.