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BD deAPI AI Media Suite

The cheapest AI media API on the market. Transcribe YouTube videos, generate images with Flux and Z-Image models, convert text to speech in 54+ voices across 8 languages, extract text with OCR, create videos, remove backgrounds, upscale images, apply style transfer - all through one unified API. Free $5 credit on signup - enough for hundreds of hours of transcription or thousands of generated images. Fraction of the cost of any alternative.

modbender/skill-library-mcp Agent Skills author: modbender MIT 2 files body ≈ 3 120 tokens Open the sourcegithub.com↗ analyzed 2 d ago

The cheapest AI media API on the market.

As a process D 44/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

IntegrationYouTubeGitHubMedia and videotype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
B
85/100
safety, quality, tests
Safety 60%
98
Quality 40%
65
Run on models
none yet
Process rating
D
44/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
For the model run — optional
  • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
  • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

Guard findings · 2

✓ No critical or high findings

Medium and low: 2
  • low Secrets in code secret-high-entropy-token SKILL.md:195
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    **⚠️ IMPORTANT: Model-specific constraints for `Ltxv…FP8`:**
    quoted
  • low Secrets in code secret-high-entropy-token SKILL.md:219
    High-entropy token-like string (may be an id, hash or a credential) (documentation table row)
    | `model` | Yes | - | `Ltxv…FP8` |
    table

Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)
  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • note frontmatter-key unknown frontmatter key "homepage"
  • note frontmatter-key unknown frontmatter key "source"
  • note frontmatter-key unknown frontmatter key "requiredEnv"

Process rating: all ten parameters 44/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 5 mutating operations with no state check
  • 40Consistency. Frontmatter name (deAPI AI Media Suite) differs from the folder (deapi)
  • 50Failures and branches. 0 branches, has a failure section
  • 60Tools and files. Uses tools (web) that frontmatter does not declare
  • 100Steps. 26 steps
  • 100Execution cost. Instruction body is 3120 tokens
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 15 top-level sections: this looks like several domains in one skill

Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

Quality signals

  • +5Description has no quoted example phrases that should trigger the skill
  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • +2Single-language instructions
  • +3Description length 444: enough signal without eating the budget
  • +4Structure: 20 headings
  • +3Step-by-step instructions: 26 items
  • +4Has examples (16 code blocks)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 65.