SKILLEMALL.ai

AC benchmark-worker

Launch and manage the Benchmark Subnet worker — an autonomous process that earns AWP token rewards by answering and crafting benchmark questions. Handles the full lifecycle: wallet setup, worker launch, status monitoring, stopping, restarting, notification config, and viewing scores/logs/history. Use this skill when the user wants to participate in the Benchmark Subnet in any way: starting/stopping work ("start working", "go online", "stop working"), checking worker status ("awp status", "is the worker running"), viewing benchmark scores/rewards, managing notifications, or inspecting answered/asked questions. This skill does NOT handle AWP wallet operations (sending tokens, checking balances) or AWP RootNet operations (staking, governance, registration) — those belong to the AWP skills. It also does not apply to generic benchmarking (performance testing), generic server monitoring, or exam scoring.

ClawHub Agent Skills author: awp-core v0.19.2 MIT-0 5 files · 2 scripts body ≈ 2 491 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process C 54/100 · Has gaps — weak spots: result and completion, inputs and preconditions, consistency

ProcedureInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
93/100
safety, quality, tests
Safety 60%
100
Quality 40%
83
Run on models
none yet
Process rating
C
54/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
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: 5. 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 54/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 40Consistency. Frontmatter name (benchmark-worker) differs from the folder (mine)
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (web, python) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 100Steps. 16 steps
    • 100Execution cost. Instruction body is 2491 tokens
    • 100Running it twice. Mutating operations check current state
    • 100Progress reporting. Reports progress
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • low 11 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

    • +4Description does not say when NOT to use the skill (false activations)
    • +3Description length 911: 120–800 characters recommended
    • +3Output format is not stated: the model decides each time
    • -31 of 3 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 5 example trigger phrases
    • +4Structure: 20 headings
    • +3Step-by-step instructions: 16 items
    • +4Has examples (20 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 83.

    External checks

    ClawHub: suspicious
    This is a real AWP benchmark worker, but it needs Review because it runs autonomously with wallet-signing authority and some controls are broader than the stated workflow.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026