SKILLEMALL.ai

AC vibe-code

Discipline skill for vibe-driven development — turns mood, feel, and outcome-shaped requests into shippable code without the usual drift, hallucinated APIs, and silent scope creep. Use when a collaborator describes software by feel rather than spec ("make it feel snappy", "vibe-code a dashboard", "like Linear but cozy", "build me something that does X, you figure out the rest"), when a prototype is being built from a vibe instead of a ticket, or when the request mixes aesthetic intent with vague behavior. Enforces a four-line Vibe Lock before any code is generated, names the specific failure modes of vibe coding, and gives concrete patterns for translating a vibe into a runnable slice. Keywords: vibe coding, vibe code, build me, make it feel, aesthetic, mood, taste, prototype, scaffold from idea, natural-language to code, intent-driven, ambient spec, Karpathy.

ClawHub Agent Skills author: John Haugabook v1.0.0 MIT-0 2 files body ≈ 2 295 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process C 59/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice

GeneratorSoftware developmenttype and topics are labelled automatically from the skill text
JSON
Technical rating
A
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
81
Run on models
none yet
Process rating
C
59/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
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: 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 59/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 30Running it twice. 2 mutating operations with no state check
    • 50When it triggers. No condition that starts the skill
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 100Steps. 31 steps
    • 100Failures and branches. 4 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2295 tokens
    • 100Progress reporting. Reports progress
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

    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)
    • +3Description length 872: 120–800 characters recommended
    • +3Output format is not stated: the model decides each time
    • -5TODO / placeholder text left in the skill
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 4 example trigger phrases
    • +4Structure: 13 headings
    • +3Step-by-step instructions: 31 items
    • +4Has examples (4 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 81.

    External checks

    ClawHub: clean
    The visible skill artifacts are coherent workflow guides with disclosed, user-directed commands and no evidence of hidden persistence, exfiltration, or deceptive behavior.
    LLM: benign (medium) · 28 May 2026