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AC heteromind

Unified heterogeneous knowledge QA system. Automatically routes natural language queries to SQL databases, Knowledge Graphs, or table files using 4-layer detection (rule-based, LLM semantic, schema matching, entity verification). Supports multi-LLM providers and bilingual queries. Trigger on data queries, "how many", "show", aggregations, filters, joins, or structured information requests.

ClawHub Agent Skills author: Yongrui Chen v0.3.0 MIT-0 45 files body ≈ 2 689 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process C 62/100 · Has gaps — weak spots: result and completion, failures and branches

AnalyzerAI and agentsInfrastructuretype 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
90/100
safety, quality, tests
Safety 60%
96
Quality 40%
80
Run on models
none yet
Process rating
C
62/100
Has gaps
Result and completion w 14
0
Failures and branches w 10
0
Tools and files w 18
60
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 · 4

    ✓ No critical or high findings

    Medium and low: 4
    • low Exfiltration read-dotenv SECURITY.md:241
      Reads a .env file (documentation of a security skill)
      cp .env.example .env
      security skill
    • low Exfiltration read-dotenv SECURITY.md:248
      Reads a .env file (documentation of a security skill)
      export $(cat .env | xargs)
      security skill
    • low Exfiltration read-dotenv SKILL.md:292
      Reads a .env file
      cp .env.example .env
    • low Exfiltration read-dotenv SKILL.md:298
      Reads a .env file
      export $(cat .env | xargs)

    Files scanned: 39. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "required_env_vars"
    • note frontmatter-key unknown frontmatter key "optional_env_vars"

    Process rating: all ten parameters 62/100

    • 0Result and completion. Does not say what the result is
    • 0Failures and branches. Linear process with no failure handling
    • 60Tools and files. Uses tools (python) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 20 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2689 tokens
    • 100Running it twice. No mutating operations
    • 100Progress reporting. Reports progress
    • low 15 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
    • -2localhost URLs: will not work for another user
    • +1No license
    • +2Single-language instructions
    • +3Description length 392: enough signal without eating the budget
    • +4Structure: 36 headings
    • +3Step-by-step instructions: 20 items
    • +4Has examples (21 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 80.

    External checks

    ClawHub: suspicious
    This appears to be a real multi-source QA skill, but it can run AI-generated code and database queries with weak enforced safeguards.
    LLM: suspicious (high) · VirusTotal: suspicious · 28 May 2026