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AB data912-market-data

Query Data912 market data endpoints for Argentina and USA instruments. Use when the user asks for MEP/CCL quotes, live Argentine market panels (stocks, options, cedears, notes, corporate debt, bonds), USA panels (ADRs, stocks), OHLC historical series by ticker, USA option chains, or volatility/risk metrics. Also use when the user mentions "Data912", "mep", "ccl", "cedears", "option chain", "historical bars", "OHLC", "implied volatility", "historical volatility", or "volatility percentiles" and expects API-backed market snapshots.

modbender/skill-library-mcp Agent Skills author: modbender MIT 2 files body ≈ 1 149 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Query Data912 market data endpoints for Argentina and USA instruments.

As a process B 69/100 · Nearly there — weak spots: result and completion, consistency, running it twice

IntegrationData 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
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
89
Run on models
none yet
Process rating
B
69/100
Nearly there
Result and completion w 14
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 · 0

    ✓ No critical or high findings

    Files scanned: 2. 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 69/100

    • 0Result and completion. Does not say what the result is
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 1 mutating operations with no state check
    • 40Consistency. Frontmatter name (data912-market-data) differs from the folder (data912)
    • 60Tools and files. Uses tools (web) that frontmatter does not declare
    • 100Steps. 75 steps
    • 100When it triggers. States when to use and when not to
    • 100Inputs and preconditions. Inputs and preconditions are listed
    • 100Failures and branches. 1 branches, has a failure section
    • 100Execution cost. Instruction body is 1149 tokens

    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

    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 7 example trigger phrases
    • +3Description length 535: enough signal without eating the budget
    • +4Structure: 16 headings
    • +3Step-by-step instructions: 75 items
    • +4Has examples (3 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 89.