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AC ragflow-workbench

RAGFlow Workbench — end-to-end RAG platform on Windows: Docker install check, admin bootstrap and API key generation, default model setup (Embedding/Chat/Rerank), knowledge base CRUD, document upload-parse lifecycle, retrieval/search, chat creation. Triggers when the user mentions: RAGFlow installation or deployment, setting up a local RAG instance on Windows, creating a knowledge base, configuring models for RAG, managing documents in datasets, searching across knowledge bases.

ClawHub Agent Skills author: JieJingKe v1.0.0 MIT-0 23 files body ≈ 1 419 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

RAGFlow Workbench — end-to-end RAG platform on Windows: Docker install check, admin bootstrap and API key generation, default model setup…

As a process C 61/100 · Has gaps — weak spots: failures and branches, consistency, running it twice

IntegrationDockerAI 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
94/100
safety, quality, tests
Safety 60%
98
Quality 40%
88
Run on models
none yet
Process rating
C
61/100
Has gaps
Failures and branches w 10
0
Progress reporting w 2
0
Running it twice w 4
30
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 · 2

    ✓ No critical or high findings

    Medium and low: 2
    • low Exfiltration read-dotenv references/command-reference.md:8
      Reads a .env file
      copy .env.example .env
    • low Exfiltration read-dotenv SKILL.md:87
      Reads a .env file
      copy .env.example .env

    Files scanned: 23. 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 61/100

    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 9 mutating operations with no state check
    • 40Consistency. Frontmatter name (ragflow-workbench) differs from the folder (ragflow-workbench-1-0-0-en)
    • 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 70When it triggers. States when to use, but not when not to
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 21 steps
    • 100Execution cost. Instruction body is 1419 tokens
    • low The skill ranks results itself: that belongs to the system behind the tool, not the model

    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)
    • -33 of 16 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +3Description length 483: enough signal without eating the budget
    • +4Structure: 10 headings
    • +3Step-by-step instructions: 21 items
    • +3Output format is stated explicitly
    • +4Has examples (5 code blocks)
    • +4Reference files are cited in the instructions (3 of 4)

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

    External checks

    ClawHub: suspicious
    The skill appears purpose-built for managing a local RAGFlow instance, but it stores and prints sensitive admin/API credentials with weak disclosure and no local file hardening.
    LLM: suspicious (medium) · VirusTotal: · 4 Jun 2026