SKILLEMALL.ai

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每日脑力训练 — 逻辑推理、数学速算、记忆挑战、文字谜题,游戏化精美卡片呈现, 含难度自适应分级和连续打卡追踪。每天一道精选题目锻炼大脑,保持思维敏锐。 Daily brain training: logic puzzles, math speed drills, memory challenges, and word games with adaptive difficulty, streak tracking, and beautiful visual cards. Trigger on:每日脑力、今日脑力、脑力训练、逻辑题、数学题、记忆挑战、 brain training、daily brain、daily puzzle、brain teaser、logic puzzle、 math challenge、word game、memory game、今天的题、做道题、脑筋急转弯。

ClawHub Agent Skills author: Cosmos Fang v1.0.0 MIT-0 7 files body ≈ 486 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process D 49/100 · Unfinished process — weak spots: result and completion, inputs and preconditions, failures and branches

ProcedureAI and agentsSoftware developmenttype and topics are labelled automatically from the skill text
JSON
Technical rating
A
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
85
Run on models
none yet
Process rating
D
49/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
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: 7. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "keywords"
    • note frontmatter-key unknown frontmatter key "requirements"

    Process rating: all ten parameters 49/100

    • 0Result and completion. Does not say what the result is
    • 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
    • 30Running it twice. 1 mutating operations with no state check
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 100Steps. 22 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 486 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)
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +3Description length 397: enough signal without eating the budget
    • +4Structure: 10 headings
    • +3Step-by-step instructions: 22 items
    • +4Has examples (4 code blocks)
    • +3All 4 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 85.

    External checks

    ClawHub: clean
    The skill is internally coherent: it uses an embedded puzzle DB and only reads/writes a local progress.json file with no network calls, credentials, or external installs required.
    LLM: benign (high) · VirusTotal: · 29 May 2026