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AC emotional-persona

Give any AI agent a living emotional personality. The agent develops moods, emotional memory, and personality traits that evolve through interaction. Use when building companion agents, wellness bots, or any agent that should feel alive rather than robotic.

ClawHub Agent Skills author: xiaoyuweiliang v1.0.0 6 files · 2 scripts body ≈ 3 179 tokens Open the sourceclawhub.ai↗ analyzed 14 d ago

Give any AI agent a living emotional personality.

As a process C 64/100 · Has gaps — weak spots: when it triggers, inputs and preconditions, failures and branches

PersonaAI 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
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
89
Run on models
none yet
Process rating
C
64/100
Has gaps
Inputs and preconditions w 11
0
Failures and branches w 10
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: 6. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "triggers"

    Process rating: all ten parameters 64/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Failures and branches. Linear process with no failure handling
    • 20When it triggers. No condition that starts the skill
    • 60Result and completion. Output format stated, no completion criterion
    • 100Tools and files. No external tools needed
    • 100Steps. 59 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3179 tokens
    • 100Running it twice. No mutating operations
    • 100Progress reporting. Reports progress
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • low 14 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)
    • +1No license
    • +2Single-language instructions
    • +3Description length 257: enough signal without eating the budget
    • +4Structure: 31 headings
    • +3Step-by-step instructions: 59 items
    • +3Output format is stated explicitly
    • +4Has examples (11 code blocks)
    • +3All 2 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 89.

    External checks

    ClawHub: suspicious
    The skill is broadly aligned with an emotional-memory persona, but it should be reviewed because it persists sensitive wellness/crisis context and its helper scripts expose caller input to Python code execution.
    LLM: suspicious (high) · VirusTotal: · 11 Sept 2026