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AC vestafolio-pea-vs-cto

Compare PEA, CTO (compte-titres) and assurance-vie net-of-tax outcomes for a French investor using Vestafolio's simulator API, after asking the simulator's questions (initial capital, monthly contribution, TMI, holding period, expected return, assurance-vie fees). Use when a user asks which investment envelope to choose, about PEA vs CTO taxation, flat tax (PFU) on investments, assurance-vie abattement, the 150 000 € PEA ceiling, or where to invest monthly savings in France.

ClawHub Agent Skills author: Vestafolio v1.0.2 MIT-0 2 files body ≈ 2 179 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

Compare PEA, CTO (compte-titres) and assurance-vie net-of-tax outcomes for a French investor using Vestafolio's simulator API, after asking the simulator's…

As a process C 58/100 · Has gaps — weak spots: when it triggers, inputs and preconditions, running it twice

IntegrationFinanceInfrastructuretype 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
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
Progress reporting w 2
0
When it triggers w 12
20
the three weakest of ten parameters · all ten

The same skill appears in 1 more place: ClawHub

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 58/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 1 mutating operations with no state check
    • 60Tools and files. Uses tools (bash, web, python) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 60Failures and branches. 2 branches
    • 100Steps. 35 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2179 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

    • +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 479: enough signal without eating the budget
    • +4Structure: 10 headings
    • +3Step-by-step instructions: 35 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 clearly scoped financial simulator helper that discloses its external API use and requires user approval before sending inputs.
    LLM: benign (high) · VirusTotal: · 9 Sept 2026