SKILLEMALL.ai

AC gold-fundamental-analysis

Fetches and analyzes gold fundamental data from FRED, CFTC, SPDR ETF, and Fed RSS. Used when the user requests gold fundamental analysis, phân tích cơ bản vàng, or gold macro data. Also used by cron jobs for gold trading sessions (Asian/European/US) that need fundamental context. Returns structured JSON with macro indicators, COT positioning, ETF holdings, Fed stance, and upcoming events. The agent MUST execute the Python script to fetch data, then analyze the output to produce Bullish/Bearish/Neutral factors, a Fundamental Score (-100 to +100), and short/medium/long-term outlook.

ClawHub Agent Skills author: Minh Swati v1.0.0 MIT-0 6 files body ≈ 881 tokens Open the sourceclawhub.ai analyzed 36 h ago

Fetches and analyzes gold fundamental data from FRED, CFTC, SPDR ETF, and Fed RSS.

As a process C 58/100 · Has gaps — weak spots: inputs and preconditions, failures and branches, progress reporting

AnalyzerOutlookAI and agentsSoftware developmentMarketingtype and topics are labelled automatically from the skill text
JSON
Technical rating
A
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
87
Run on models
none yet
Process rating
C
58/100
Has gaps
Inputs and preconditions w 11
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 · 0

    ✓ No critical or high findings

    Files scanned: 0. 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 58/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 60Tools and files. Uses tools (web, 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
    • 85Steps. 29 steps, 2 vague phrases
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 881 tokens
    • 100Running it twice. No mutating operations

    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)
    • +1No license
    • +2Single-language instructions
    • +3Description length 587: enough signal without eating the budget
    • +4Structure: 9 headings
    • +3Step-by-step instructions: 29 items
    • +3Output format is stated explicitly
    • +4Has examples (2 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.

    External checks

    ClawHub: clean
    This skill is a disclosed gold-market data fetcher/analyzer, with manageable risks around outbound requests, financial-use reliance, and an exposed FRED API key.
    LLM: benign (high) · VirusTotal: · 14 Jun 2026