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AC oua-intelligence-test

OUA (OpenClaw Unified Assessment) v1.0 — AI 全方位智能评估框架。融合 OIT（8维度智商天花板测试）与 LLI（2维度工程地板测试），共 10 大维度全方位评估 AI 能力。覆盖语言理解、逻辑推理、领域知识、代码生成、创意能力、上下文记忆、工具使用、安全伦理、工程实现、系统鲁棒性。支持交互式评分和 HTML 可视化报告生成（含雷达图+四象限分析）。Trigger phrases: OUA测试 AI全能评估 智商天花板 工程地板 10维度AI评测 AI能力边界测试 openclaw unified assessment 小龙虾综合测评 openclaw oua intelligence test benchmark evaluation.

ClawHub Agent Skills author: RafeYu8899 v1.0.0 MIT-0 4 files body ≈ 2 675 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerInfrastructuretype 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
91/100
safety, quality, tests
Safety 60%
100
Quality 40%
77
Run on models
none yet
Process rating
C
53/100
Has gaps
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 · 0

    ✓ No critical or high findings

    Files scanned: 4. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • warning frontmatter-yaml SKILL.md: the frontmatter is not valid YAML (YAML parse error: Nested mappings are not allowed in compact mappings at line 3, column 14: description: OUA (OpenClaw Unified Assessment) v1.0 — AI 全方位智能评估框架。融合 OIT（8维度智商… ^ ); fields were read line by line. The usual cause is a colon inside an unquoted value
    • note frontmatter-key unknown frontmatter key "description_zh"
    • note frontmatter-key unknown frontmatter key "description_en"
    • note frontmatter-key unknown frontmatter key "repository"

    Process rating: all ten parameters 53/100

    • 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
    • 20When it triggers. No condition that starts the skill
    • 100Tools and files. No external tools needed
    • 100Steps. 58 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2675 tokens
    • 100Running it twice. No mutating operations

    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
    • -220 emoji in the instructions: noise for the model
    • +2Single-language instructions
    • +3Description length 344: enough signal without eating the budget
    • +4Structure: 35 headings
    • +3Step-by-step instructions: 58 items
    • +4Has examples (8 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)
    • +3All 1 scripts are documented
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 77.

    External checks

    ClawHub: clean
    This is a purpose-aligned AI evaluation skill with an optional local scoring/report script; the main issue is broad trigger wording that could activate it unintentionally.
    LLM: benign (high) · VirusTotal: · 29 May 2026