SKILLEMALL.ai

AB prompt-optimizer

Iterative prompt optimizer for complex tasks. Strictly implements ACON's two-stage iterative optimization + APE automatic prompt engineering. Only triggers when user explicitly requests it, actively collects feedback after optimization, supports multi-round iteration until satisfied.

ClawHub Agent Skills author: ucsdzehualiu v1.0.0 MIT-0 2 files body ≈ 1 224 tokens Open the sourceclawhub.ai analyzed 2 d ago

As a process B 72/100 · Nearly there — weak spots: inputs and preconditions, consistency, running it twice

ReferenceAI and agentstype 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
B
72/100
Nearly there
Inputs and preconditions w 11
0
Progress reporting w 2
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

    • note frontmatter-key unknown frontmatter key "usage"

    Process rating: all ten parameters 72/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 3 mutating operations with no state check
    • 40Consistency. Frontmatter name (prompt-optimizer) differs from the folder (prompt-optimizer-en)
    • 60Failures and branches. 2 branches
    • 70When it triggers. States when to use, but not when not to
    • 100Tools and files. No external tools needed
    • 100Steps. 58 steps
    • 100Result and completion. Output format and completion criterion are stated
    • 100Execution cost. Instruction body is 1224 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)
    • -221 emoji in the instructions: noise for the model
    • +2Single-language instructions
    • +3Description length 284: enough signal without eating the budget
    • +4Structure: 7 headings
    • +3Step-by-step instructions: 58 items
    • +3Output format is stated explicitly
    • +4Has examples (1 code blocks)
    • +1License stated

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

    External checks

    ClawHub: clean
    This is an instruction-only prompt rewriting skill with no code execution, credential access, persistence, or hidden install behavior.
    LLM: benign (high) · VirusTotal: · 29 May 2026