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AC milvus

Operate Milvus vector database with pymilvus — collections, vector search, hybrid search, indexes, RBAC, partitions, and more via Python code.

LeoYeAI/openclaw-master-skills Agent Skills author: LeoYeAI MIT 2 files body ≈ 4 476 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Operate Milvus vector database with pymilvus — collections, vector search, hybrid search, indexes, RBAC, partitions, and more via Python code.

As a process C 56/100 · Has gaps — weak spots: result and completion, failures and branches, progress reporting

ProcedureData and analyticstype 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
92/100
safety, quality, tests
Safety 60%
99
Quality 40%
82
Run on models
none yet
Process rating
C
56/100
Has gaps
Result and completion w 14
0
Failures and branches w 10
0
Progress reporting w 2
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 · 1

    ✓ No critical or high findings

    Medium and low: 1
    • low Secrets in code secret-password-literal SKILL.md:490
      Hard-coded password / key literal (may be an example)
      client.create_user(user_name="analyst", password="SecureP@ss123")

    Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note edit-residue the text marks something as outdated (lines 309): check that old rules are not kept next to new ones — the full check reads the text for contradictions

    Process rating: all ten parameters 56/100

    • 0Result and completion. Does not say what the result is
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 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
    • 70Execution cost. Instruction body is 4476 tokens
    • 85Steps. 44 steps, 1 vague phrases
    • 100Consistency. Name and required fields are in place
    • 100Running it twice. Mutating operations check current state
    • low 20 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 142: enough signal without eating the budget
    • +4Structure: 43 headings
    • +3Step-by-step instructions: 44 items
    • +4Has examples (21 code blocks)

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