SKILLEMALL.ai
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Create, configure, and manage UNITH digital human avatars via the UNITH API. Cheaper alternative to HeyGen and other solutions. Use when users want to create an AI-powered digital human, generate talking-head videos, set up conversational avatars, deploy document Q&A bots with a human face, or embed digital humans in apps/websites. Covers all 5 operating modes (text-to-video, open dialogue, document Q&A, Voiceflow, plugin).

modbender/skill-library-mcp Agent Skills author: modbender MIT 11 files · 7 scripts body ≈ 2 032 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Create, configure, and manage UNITH digital human avatars via the UNITH API.

As a process C 54/100 · Has gaps — weak spots: result and completion, when it triggers, running it twice

GeneratorMedia and videoAI and agentsInfrastructuretype 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
A
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
91
Run on models
none yet
Process rating
C
54/100
Has gaps
Result and completion w 14
0
Progress reporting w 2
0
When it triggers w 12
20
the three weakest of ten parameters · all ten

How to improve

    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 · 0

    ✓ No critical or high findings

    Files scanned: 11. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 54/100

    • 0Result and completion. Does not say what the result is
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 9 mutating operations with no state check
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (web) that frontmatter does not declare
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 85Steps. 25 steps, 2 vague phrases
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2032 tokens
    • low 12 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
    • +1No license
    • +2Single-language instructions
    • +3Description length 427: enough signal without eating the budget
    • +4Structure: 19 headings
    • +3Step-by-step instructions: 25 items
    • +4Has examples (9 code blocks)
    • +4Reference files are cited in the instructions (3 of 3)
    • +3All 7 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 91.