SKILLEMALL.ai

AC happy

Pick 2-3 random happy moment stories from the HappyDB dataset and retell them as short stand-up comedy bits. Use this skill whenever the user wants to hear funny stories, needs a laugh, wants random happy moments from the dataset, or asks for comedy content from the happy moments CSV. Trigger on phrases like "tell me happy stories", "make me laugh", "pick some stories", "random happy moments", "cheer me up", or anything requesting funny/happy content from the data.

ClawHub Agent Skills author: Jay v1.0.1 MIT-0 2 files body ≈ 469 tokens Open the sourceclawhub.ai analyzed 2 d ago

As a process C 50/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches

GeneratorData and analyticstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
89
Run on models
none yet
Process rating
C
50/100
Has gaps
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: 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 50/100

    • 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
    • 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 85Steps. 13 steps, 1 vague phrases
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 469 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

    • +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
    • +5Description quotes 5 example trigger phrases
    • +3Description length 469: enough signal without eating the budget
    • +4Structure: 5 headings
    • +3Step-by-step instructions: 13 items
    • +4Has examples (1 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 89.

    External checks

    ClawHub: clean
    This is a local comedy-writing skill that samples a bundled HappyDB CSV; no wallet, purchase, credential, or network behavior was found.
    LLM: benign (high) · VirusTotal: · 29 May 2026