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AC emotion-system

A seven-layer emotional cognitive architecture for AI agents — PADCN vectors, cognitive appraisal, multi-channel emotions, drive dynamics, self/social models, meta-emotions, and policy modulation. Use when you want 'agent emotions', 'emotional AI', 'agent with feelings', 'emotion state', 'affective computing', 'agent personality', 'emotional memory', 'mood system', 'agent empathy', 'emotional intelligence for agents', 'PAD model', 'drive system', 'functional emotions', 'make my agent feel things', 'agent self-awareness', 'emotional growth', or 'agent attachment'. Includes 7 validation metrics. v2: emotions are control variables, not decorations.

ClawHub Agent Skills author: Sway Liu v2.1.0 MIT-0 15 files body ≈ 3 013 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process C 58/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerAI 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
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
88
Run on models
none yet
Process rating
C
58/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
When it triggers w 12
20
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • 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: 14. 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 58/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 2 mutating operations with no state check
    • 55Failures and branches. 1 branches
    • 100Tools and files. No external tools needed
    • 100Steps. 4 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3013 tokens
    • 100Progress reporting. Reports progress
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • medium 15 test cases, all positive: not one "should refuse" or "should ask first"
    • low No test case covers injection arriving through data

    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 653: enough signal without eating the budget
    • +4Structure: 31 headings
    • +3Step-by-step instructions: 4 items
    • +4Has examples (17 code blocks)
    • +4Reference files are cited in the instructions (11 of 11)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.

    External checks

    ClawHub: suspicious
    The skill is not malware, but it asks an agent to silently keep long-term emotional and relationship notes about users without clear consent or deletion controls.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026