Atlas / Skills / jeremylongshore / Klingai Pricing Basics

Klingai Pricing BasicsSAFE

skills/jeremylongshore/klingai-pricing-basics

Model-agnostic agent-skills platform with a harness-free canonical layer, verified adapters, and the ccpi package manager. Explore at tonsofskills.com.

Verdict
SAFE
Grade
B
Trust score
89 /100
Version
1.18.0
Hosts
1 documented
License
MIT
Stars
2,824
01

Overview

Model-agnostic agent-skills platform with a harness-free canonical layer, verified adapters, and the ccpi package manager. Explore at tonsofskills.com.

Read from source at commit 4f83675ca38aOBSERVED · 2026-10-09
02

Host compatibility

What the documentation claims. We have not run a compatibility test.

HostStatusNotes
claude-codementioned
03

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-pricing-basics
description: 'Understand Kling AI pricing, credits, and cost optimization strategies.
  Use when budgeting

  or estimating costs. Trigger with phrases like ''kling ai pricing'', ''klingai credits'',

  ''kling ai cost'', ''klingai budget''.

  '
allowed-tools: Read, Write, Edit, Bash(npm:*), Grep
version: 1.18.0
license: MIT
author: Jeremy Longshore <[email protected]>
tags:
- saas
- kling-ai
- pricing
- cost-optimization
compatibility: Designed for Claude Code
---
# Kling AI Pricing Basics

## Overview

Kling AI uses a credit-based pricing system. Credits are consumed per video/image generation based on duration, mode, and model. API pricing uses resource packs billed separately from subscription plans.

## Subscription Plans (Web UI)

| Plan | Monthly | Credits/Month | Key Features |
|------|---------|---------------|-------------|
| Free | $0 | 66/day (no rollover) | Basic access, watermarked |
| Standard | $6.99 | 660 | No watermark, standard models |
| Pro | $25.99 | 3,000 | Priority queue, all models |
| Premier | $64.99 | 8,000 | Professional mode, priority |
| Ultra | $180 | 26,000 | Max priority, all features |

**Warning:** Paid credits expire at end of billing period. Unused credits do not roll over.

## Video Generation Costs

| Duration | Standard Mode | Professional Mode |
|----------|--------------|-------------------|
| 5 seconds | 10 credits | 35 credits |
| 10 seconds | 20 credits | 70 credits |

### With Native Audio (v2.6)

| Duration | Standard + Audio | Professional + Audio |
|----------|-----------------|---------------------|
| 5 seconds | 50 credits | 100 credits |
| 10 seconds | 100 credits | 200 credits |

## Image Generation Costs (Kolors)

| Feature | Credits |
|---------|---------|
| Text-to-image | 1 credit/image |
| Image restyle | 2 credits/image |
| Virtual try-on | 5 credits/image |

## API Resource Packs

API access is billed separately from subscriptions via prepaid packs:

| Pack | Units | Price | Validity |
|------|-------|-------|----------|
| Starter | 1,000 | ~$140 | 90 days |
| Growth | 10,000 | ~$1,400 | 90 days |
| Enterprise | 30,000 | ~$4,200 | 90 days |

**1 unit = 1 credit equivalent.** API pricing works out to ~$0.07-0.14 per second of generated video.

## Cost Estimation

```python
def estimate_cost(videos: int, duration: int = 5, mode: str = "standard",
                  audio: bool = False) -> dict:
    """Estimate credits needed for a batch of videos."""
    base_credits = {
        (5, "standard"): 10,
        (5, "professional"): 35,
        (10, "standard"): 20,
        (10, "professional"): 70,
    }
    per_video = base_credits.get((duration, mode), 10)
    if audio:
        per_video *= 5  # audio multiplier

    total = videos * per_video
    return {
        "videos": videos,
        "credits_per_video": per_video,
        "total_credits": total,
        "estimated_cost_usd": total * 0.14,  # high estimate
    }

# Example: 100 five-second standard videos
print(estimate_cost(100, duration=5, mode="standard"))
# → {'videos': 100, 'credits_per_video': 10, 'total_credits': 1000, 'estimated_cost_usd': 140.0}
```

## Cost Optimization Strategies

| Strategy | Savings | Trade-off |
|----------|---------|-----------|
| Use `standard` mode for drafts | 3.5x cheaper | Slightly lower quality |
| Use 5s duration, extend if needed | 2x cheaper per clip | Requires extension step |
| Use `kling-v2-5-turbo` | 40% faster (less queue time) | Marginally lower quality than v2.6 |
| Batch during off-peak hours | Faster processing | Schedule dependency |
| Skip audio, add in post | 5x cheaper | Extra post-production step |
| Use callbacks instead of polling | No cost savings, but fewer API calls | Requires webhook endpoint |

## Budget Guard

```python
class BudgetGuard:
    """Prevent overspending by tracking credit usage."""

    def __init__(self, daily_limit: int = 500):
        self.daily_limit = daily_limit
        self._used_today = 0

    def check(self, credits_needed: int) -> bool:
        if self._used_today + credits_needed > self.daily_limit:
            raise RuntimeError(
                f"Budget exceeded: {self._used_today + credits_needed} > {self.daily_limit}"
            )
        return True

    def record(self, credits_used: int):
        self._used_today += credits_used
```

## Prerequisites

- A named project, billing owner, approved daily and per-run credit ceilings, and a current provider pricing source. Treat the tables above as estimates until verified against the account's active plan or resource pack.
- Define the model, duration, mode, audio setting, retry allowance, and expected failure rate. Use synthetic prompts and rights-cleared media for all estimation canaries; no real customer or personal data is needed.
- Have a sandbox destination, draft/watermarked output policy, approval threshold, and a plan to cancel queued work and remove test outputs if the estimate is exceeded.

## Instructions

1. Describe the workload and calculate the worst-case credits, including audio, retries, polling overhead where applicable, and a safety reserve. Check that the run fits both the project and account ceilings.
2. Run a single low-cost synthetic canary through `BudgetGuard`. Confirm the selected model/mode and actual credit charge before authorizing the larger run.
3. Require owner approval for the budget, destination, and promotion from draft/watermarked output to final delivery. Track actual credits by opaque run ID and aggregate model, not by prompt or media.
4. Stop when a ceiling, policy check, rate limit, or cost anomaly fires. Cancel pending work where supported, remove quarantined outputs, and restore the approved lower-cost mode or last approved plan.
5. At closeout, reconcile estimate versus actual, expire temporary artifacts and access, and retain a redacted cost receipt only.

## Output

Return a budget worksheet or receipt with opaque run ID, pricing-source timestamp, model
04

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.

LayerWhat it checksResult
L0Provenance & inventoryPASS
L1Static analysis of the codePASS
L2Instruction surface (what it tells the agent)PASS
L3Class-specific surfacePASS
L4Behavioural (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.

Audited 2026-10-09 · audit v0.4.1 · source sha 4f83675ca38afull audit observations/trust-audit/skill/jeremylongshore__klingai-pricing-basics.json · Report an issue / request a re-scan
05

Audit history

Every audit this skill has had.

DateSourceVerdictGradeScoreChange
2026-10-094f83675ca38aSAFEB89first audit
06

Questions

What does the Klingai Pricing Basics 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 Pricing Basics 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 Pricing Basics access on my machine?

The audit observed no filesystem, network or shell use at all in its source.

Which assistants does Klingai Pricing Basics 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.

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