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AC knowledge-graph-builder-pro

知识图谱构建器专业版是在免费版基础上的全功能升级，为AI Agent包含从类型化图谱到可视化的完整知识管控引擎。专业版解锁SQLite迁移、图可视化、SPARQL-like高级查询、版本追踪与差异对比、跨技能通信增强、多平台集成六大高级功能，达成大规模图谱的高性能管控与可视化。Use when 需要数据库操作、SQL查询、数据存储管理时使用。不适用于数据库架构设计决策。 功能涵盖: builder。

ClawHub Agent Skills author: 天轰穿 v1.0.0 MIT-0 2 files body ≈ 3 570 tokens Open the sourceclawhub.ai↗ analyzed 35 h ago

知识图谱构建器专业版是在免费版基础上的全功能升级，为AI Agent包含从类型化图谱到可视化的完整知识管控引擎。专业版解锁SQLite迁移、图可视化、SPARQL-like高级查询、版本追踪与差异对比、跨技能通信增强、多平台集成六大高级功能，达成大规模图谱的高性能管控与可视化。Use when…

As a process C 55/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureAI 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%
100
Quality 40%
82
Run on models
none yet
Process rating
C
55/100
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: 2. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "slug"
    • note frontmatter-key unknown frontmatter key "displayName"
    • note frontmatter-key unknown frontmatter key "summary"
    • note frontmatter-key unknown frontmatter key "tools"

    Process rating: all ten parameters 55/100

    • 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
    • 20When it triggers. No condition that starts the skill
    • 100Tools and files. Tools declared in frontmatter
    • 100Steps. 47 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3570 tokens
    • 100Running it twice. No mutating operations
    • 100Progress reporting. Reports progress
    • 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
    • +2Single-language instructions
    • +3Description length 201: enough signal without eating the budget
    • +4Structure: 43 headings
    • +3Step-by-step instructions: 47 items
    • +4Has examples (20 code blocks)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 82.

    External checks

    ClawHub: suspicious
    This skill is not clearly malicious, but it asks for broad command and file access while describing callbacks, automatic cross-skill events, and external sync without enough scoping or enforcement.
    LLM: suspicious (high) · VirusTotal: · 14 Aug 2026