SKILLEMALL.ai

AC fund_buying_decision

Parameterize and apply a Chinese mutual-fund buy, add, reduce, or hold strategy driven by price drawdown, recurring DCA, cash-pool management, and position limits. Use when Codex needs to maintain the strategy thresholds in SKILL.md, explain or simulate a daily decision, update the bundled Eastmoney-to-SQLite importer, or produce a detailed report for a fund such as 011598.

ClawHub Agent Skills author: Xinhai Zou v1.0.0 MIT-0 12 files body ≈ 2 736 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process C 62/100 · Has gaps — weak spots: result and completion, consistency, progress reporting

GeneratorSoftware developmentInfrastructureAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
80
Run on models
none yet
Process rating
C
62/100
Has gaps
Result and completion w 14
0
Progress reporting w 2
0
Consistency w 8
40
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: 12. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)

    Process rating: all ten parameters 62/100

    • 0Result and completion. Does not say what the result is
    • 0Progress reporting. Says nothing while it works
    • 40Consistency. Frontmatter name (fund_buying_decision) differs from the folder (fund-buying-decision)
    • 60Tools and files. Uses tools (bash, web, python) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 70Failures and branches. 5 branches
    • 100Steps. 86 steps
    • 100Execution cost. Instruction body is 2736 tokens
    • 100Running it twice. Mutating operations check current state
    • low 12 top-level sections: this looks like several domains in one skill

    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)
    • +3Output format is not stated: the model decides each time
    • -31 of 8 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +3Description length 376: enough signal without eating the budget
    • +4Structure: 17 headings
    • +3Step-by-step instructions: 86 items
    • +4Has examples (1 code blocks)
    • +4Reference files are cited in the instructions (2 of 2)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 80.

    External checks

    ClawHub: clean
    This skill is a disclosed local fund-strategy tool that fetches public fund data and maintains a local SQLite planning ledger.
    LLM: benign (high) · VirusTotal: · 29 May 2026