Klingai Debug BundleSAFE
Model-agnostic agent-skills platform with a harness-free canonical layer, verified adapters, and the ccpi package manager. Explore at tonsofskills.com.
Overview
Model-agnostic agent-skills platform with a harness-free canonical layer, verified adapters, and the ccpi package manager. Explore at tonsofskills.com.
4f83675ca38aOBSERVED · 2026-10-09Host compatibility
What the documentation claims. We have not run a compatibility test.
| Host | Status | Notes |
|---|---|---|
| claude-code | mentioned |
What it tells the agent
The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.
--- 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 version: 1.18.0 license: MIT author: Jeremy Longshore <[email protected]> tags: - saas - kling-ai - debugging - observability compatibility: Designed for Claude Code --- # 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'B
Trust audit
SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.
| Layer | What it checks | Result |
|---|---|---|
| L0 | Provenance & inventory | PASS |
| L1 | Static analysis of the code | PASS |
| L2 | Instruction surface (what it tells the agent) | PASS |
| L3 | Class-specific surface | PASS |
| L4 | Behavioural (sandbox) | SKIPPED |
What the source does
- Filesystem
- none-observed
- Network
- none-observed
- Shell
- none-observed
- Dependencies
- pinned
- Secrets in source
- none-found
Findings (0)
No findings outside the package's declared scope.
Gates applied: no_behavioural_pass.
4f83675ca38afull audit observations/trust-audit/skill/jeremylongshore__klingai-debug-bundle.json · Report an issue / request a re-scanAudit history
Every audit this skill has had.
| Date | Source | Verdict | Grade | Score | Change |
|---|---|---|---|---|---|
| 2026-10-09 | 4f83675ca38a | SAFE | B | 89 | first audit |
Questions
What does the Klingai Debug Bundle skill do?
Model-agnostic agent-skills platform with a harness-free canonical layer, verified adapters, and the ccpi package manager. Explore at tonsofskills.com.
Is Klingai Debug Bundle safe to install?
The audit found nothing in the source that contradicts what it says it does, and graded it B (89/100). Grade A is held back for packages that have also passed a sandboxed behavioural run, which is why a clean skill reads B.
What can Klingai Debug Bundle access on my machine?
The audit observed no filesystem, network or shell use at all in its source.
Which assistants does Klingai Debug Bundle work with?
Its documentation mentions claude-code. That is what the text claims, not a compatibility test we ran.
How current is this page?
The grade is for one exact copy of the source (4f83675ca38a), read on 2026-10-09. The repository is watched, and a new audit runs when it changes — this is the first audit.