SKILLEMALL.ai

AC market-events

Reports upcoming or recent earnings, dividends, and stock splits from FMP for a watchlist of tickers. Accepts a comma-separated ticker list or a file of tickers. Returns events in a given date range (default 7 days ahead; use negative range like -30d to look back). Use when the user asks about upcoming or past corporate events, earnings dates, dividend schedules, or stock splits for specific tickers.

ClawHub Agent Skills author: Kevin Haney v0.1.4 MIT-0 4 files body ≈ 830 tokens Open the sourceclawhub.ai analyzed 4 d ago

As a process C 57/100 · Has gaps — weak spots: inputs and preconditions, failures and branches, progress reporting

ReferenceInfrastructureData and analyticstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
93/100
safety, quality, tests
Safety 60%
100
Quality 40%
83
Run on models
none yet
Process rating
C
57/100
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: 4. 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 57/100

    • 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 (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
    • 75Steps. 3 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 830 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)
    • -4Absolute local paths (C:\Users, /home/…): not portable
    • +1No license
    • +2Single-language instructions
    • +3Description length 403: enough signal without eating the budget
    • +4Structure: 11 headings
    • +3Step-by-step instructions: 3 items
    • +3Output format is stated explicitly
    • +4Has examples (7 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 83.

    External checks

    ClawHub: clean
    This is a straightforward Financial Modeling Prep market-calendar skill; its main issue is dependency hygiene, not hidden or malicious behavior.
    LLM: benign (high) · VirusTotal: · 29 May 2026