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AB zotero-vectorize

Build and maintain a cross-platform local Zotero semantic index using metadata embeddings and PDF full-text chunk embeddings. Use when the user asks to vectorize a Zotero library, create or refresh metadata_vectors.json or fulltext_vectors.json, check for new Zotero items missing from the vector store, incrementally update a Zotero semantic/RAG index, verify vector store counts and sizes, or reproduce this workflow on Windows, macOS, or Linux.

ClawHub Agent Skills author: yckbz v0.1.0 MIT-0 17 files body ≈ 1 214 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process B 78/100 · Nearly there — weak spots: inputs and preconditions, progress reporting

GeneratorAI 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%
97
Quality 40%
88
Run on models
none yet
Process rating
B
78/100
Nearly there
Inputs and preconditions w 11
0
Progress reporting w 2
0
Result and completion w 14
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 · 3

    ✓ No critical or high findings

    Medium and low: 3
    • low Secrets in code secret-high-entropy-token references/config.md:45
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      - Model: `para…-v2`
      quoted
    • low Secrets in code secret-high-entropy-token references/data-format.md:27
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      "model": "para…-v2"
      quoted
    • low Secrets in code secret-high-entropy-token scripts/zotero_vectorize_lib.py:21
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      DEFAULT_MODEL = "para…-v2"
      quoted

    Files scanned: 17. 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 78/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 60Result and completion. Output format stated, no completion criterion
    • 70When it triggers. States when to use, but not when not to
    • 100Tools and files. No external tools needed
    • 100Steps. 50 steps
    • 100Failures and branches. 4 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1214 tokens
    • 100Running it twice. Mutating operations check current state

    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)
    • -31 of 9 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +3Description length 447: enough signal without eating the budget
    • +4Structure: 13 headings
    • +3Step-by-step instructions: 50 items
    • +3Output format is stated explicitly
    • +4Has examples (7 code blocks)
    • +4Reference files are cited in the instructions (6 of 6)

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

    External checks

    ClawHub: clean
    This skill locally builds Zotero search indexes as advertised, but the generated files can contain private library metadata, PDF text, database snapshots, and local paths.
    LLM: benign (high) · VirusTotal: · 29 May 2026