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Floating avatar widget for AI agents showing emotions, actions, and visual effects. Give your OpenClaw a face! Use when the user wants visual feedback, a floating status window, or to see agent emotions while it works. Triggers on "show avatar", "uruchom avatara", "pokaż avatara", "agent face", "visual feedback".

modbender/skill-library-mcp Agent Skills author: modbender MIT 6 files body ≈ 1 300 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Floating avatar widget for AI agents showing emotions, actions, and visual effects.

As a process D 45/100 · Unfinished process — weak spots: result and completion, inputs and preconditions, failures and branches

ProcedureSoftware developmentAI and agentstype 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
93/100
safety, quality, tests
Safety 60%
99
Quality 40%
84
Run on models
none yet
Process rating
D
45/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
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 · 1

    ✓ No critical or high findings

    Medium and low: 1
    • low Dangerous commands cmd-background-process SKILL.md:84
      Starts a background / autostarted process
      nohup python3 SKILL_PATH/scripts/avatar.py --mode robot > /dev/null 2>&1 &

    Files scanned: 6. 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 45/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 1 mutating operations with no state check
    • 40Consistency. Frontmatter name (clawface) differs from the folder (claw-face)
    • 60Tools and files. Uses tools (bash, web, python) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 100Steps. 23 steps
    • 100Execution cost. Instruction body is 1300 tokens

    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

    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • -217 emoji in the instructions: noise for the model
    • -31 of 2 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 5 example trigger phrases
    • +3Description length 314: enough signal without eating the budget
    • +4Structure: 19 headings
    • +3Step-by-step instructions: 23 items
    • +4Has examples (10 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.