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AB vnstock-free-expert

Runs an end-to-end vnstock workflow for free-tier-safe Vietnam stock valuation, ranking, and API operations with strict rate-limit control; used when users request Vietnamese stock analysis under free-tier constraints.

modbender/skill-library-mcp Agent Skills author: modbender MIT 27 files body ≈ 1 703 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Runs an end-to-end vnstock workflow for free-tier-safe Vietnam stock valuation, ranking, and API operations with strict rate-limit control; used when users…

As a process B 75/100 · Nearly there — weak spots: result and completion, inputs and preconditions

ProcedureFinanceInfrastructuretype 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%
99
Quality 40%
85
Run on models
none yet
Process rating
B
75/100
Nearly there
Result and completion w 14
0
Inputs and preconditions w 11
0
Tools and files w 18
100
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 references/vnstock/11-best-practices.md:318
      Hard-coded password / key literal (may be an example)
      api_key = "abc1…456"

    Files scanned: 27. 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 75/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 100Tools and files. No external tools needed
    • 100Steps. 64 steps
    • 100When it triggers. States when to use and when not to
    • 100Failures and branches. 3 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1703 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
    • -31 of 9 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +3Description length 218: enough signal without eating the budget
    • +4Structure: 18 headings
    • +3Step-by-step instructions: 64 items
    • +4Has examples (1 code blocks)
    • +4Reference files are cited in the instructions (3 of 4)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 85.