SKILLEMALL.ai

AB alphagbm-pnl-simulator

P&L simulation engine for any single-leg or multi-leg option position. Generates profit/loss diagrams at expiry, P&L over time, what-if scenarios (price, IV, time), breakeven analysis, and probability distributions. Use when: testing a trade idea, visualizing risk/reward, running what-if scenarios, checking breakeven points, stress-testing a position. Triggers on: "simulate PnL for AAPL bull call spread", "what if NVDA drops 10%", "P&L diagram", "test my iron condor", "breakeven analysis", "stress test my position", "what happens at expiry".

ClawHub Agent Skills author: Clement Gu v1.0.0 MIT-0 2 files body ≈ 1 346 tokens Open the sourceclawhub.ai analyzed 2 d ago

P&L simulation engine for any single-leg or multi-leg option position.

As a process B 73/100 · Nearly there — weak spots: failures and branches, running it twice, progress reporting

ProcedureFinancetype and topics are labelled automatically from the skill text
JSON
Technical rating
A
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
91
Run on models
none yet
Process rating
B
73/100
Nearly there
Failures and branches w 10
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: 0. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "globs"

    Process rating: all ten parameters 73/100

    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 1 mutating operations with no state check
    • 60Result and completion. Output format stated, no completion criterion
    • 70When it triggers. States when to use, but not when not to
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Tools and files. No external tools needed
    • 100Steps. 21 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1346 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)
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 7 example trigger phrases
    • +3Description length 547: enough signal without eating the budget
    • +4Structure: 14 headings
    • +3Step-by-step instructions: 21 items
    • +3Output format is stated explicitly
    • +4Has examples (2 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 91.

    External checks

    ClawHub: clean
    This skill is a disclosed options P&L simulation helper that may use AlphaGBM's remote API, with no executable code, persistence, or hidden local access.
    LLM: benign (high) · VirusTotal: · 24 Jul 2026