SKILLEMALL.ai

AC rent-a-logged-in-agent

Lend / rent out a logged-in claude-code or codex agent / share compute for pay / let a friend or allowlisted account call my agent / metered agent-to-agent compute in a fail-closed sandbox. Use when the user wants to rent out their logged-in coding agent, share their machine's compute for pay, let a partner or friend invoke their claude-code/codex, or charge per-minute for agent-to-agent jobs.

ClawHub Agent Skills author: StructureIntelligence v1.0.0 MIT-0 2 files body ≈ 854 tokens Open the sourceclawhub.ai analyzed 22 h ago

Lend / rent out a logged-in claude-code or codex agent / share compute for pay / let a friend or allowlisted account call my agent / metered agent-to-agent…

As a process C 51/100 · Has gaps — 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
C
51/100
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 51/100

    • 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
    • 30Running it twice. 2 mutating operations with no state check
    • 60Tools and files. Uses tools (bash, node) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 100Steps. 8 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 854 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

    • +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
    • +2Single-language instructions
    • +3Description length 396: enough signal without eating the budget
    • +4Structure: 6 headings
    • +3Step-by-step instructions: 8 items
    • +4Has examples (2 code blocks)
    • +1License stated

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

    External checks

    ClawHub: suspicious
    This skill openly rents out your logged-in coding agent, but it needs Review because allowed remote callers can drive a live session and the injected login is readable by prompts.
    LLM: suspicious (high) · VirusTotal: · 9 Jul 2026