SKILLEMALL.ai
GitHub ↗ RU Sign in

CC compute-resell

Autonomous compute-reselling agent on Surplus Intelligence - resells free or low-cost provider compute across Bankr, AWS Bedrock, and Google Vertex (GCP) as one skill. Reads the live market + your cost + your usage, auto-lists your models per enabled provider, and reactively prices each offer to win routing within a cost floor, cap, and health guardrails.

aeonfun/aeon Agent Skills author: aeonfun 7 files · 5 scripts body ≈ 26 128 tokens Open the sourcegithub.com↗ analyzed 53 min ago

Autonomous compute-reselling agent on Surplus Intelligence - resells free or low-cost provider compute across Bankr, AWS Bedrock, and Google Vertex (GCP) as…

As a process C 51/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

IntegrationAWSGoogle CloudInfrastructureAI and agentstype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
C
80/100
safety, quality, tests
Safety 60%
94
Quality 40%
60
Run on models
none yet
Process rating
C
51/100
Has gaps
Result and completion w 14
0
Execution cost w 6
10
When it triggers w 12
20
the three weakest of ten parameters · all ten

What is at stake

Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.

Exfiltration medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

The instructions or scripts send data out: environment variables, keys, file contents, chat history. You may never notice, because the agent performs the upload as "part of the task".

For the author

If the upload is not needed for the task, remove it: catalogs flag such skills and delist them. If it is needed, name the destination explicitly, say what leaves the machine, and give the user a switch.

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
  2. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
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 · 2

✓ No critical or high findings

Medium and low: 2
  • medium Exfiltration exfil-send-secrets-to-url SKILL.md:298
    Instruction to send secrets/history to an external endpoint (describes API authentication (bearer / header / HTTPS); documentation of a security skill; the skill's own vendor host)
    Pick the **cheapest serviceable model** from `/v1/prices` (the public read you already do) and send a 1-token completion with `X-API-Key: {BANKR_LLM_KEY}` (the gateway is OpenAI-compatible; base `http
    API authsecurity skillvendor-host

A further 1 matches are quotations in this security skill's documentation and are not counted as findings.

Files scanned: 7. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • warning body-long SKILL.md body ≈ 26128 tokens (recommended < 5000); move details to references/
  • note edit-residue the text marks something as outdated (lines 135, 158, 169, 373, 406): check that old rules are not kept next to new ones — the full check reads the text for contradictions

Process rating: all ten parameters 51/100

  • 0Result and completion. Does not say what the result is
  • 10Execution cost. Instruction body is 26128 tokens: crowds the task out of the window
  • 20When it triggers. No condition that starts the skill
  • 30Inputs and preconditions. Does not say what the process needs to start
  • 30Running it twice. 168 mutating operations with no state check
  • 60Tools and files. Uses tools (bash, web, python) that frontmatter does not declare
  • 85Steps. 79 steps, 2 vague phrases
  • 100Failures and branches. 9 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 19 top-level sections: this looks like several domains in one skill
  • low The response is described with custom markup (35 tags): a typed call is more reliable

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
  • -214 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +3Description length 357: enough signal without eating the budget
  • +4Structure: 24 headings
  • +3Step-by-step instructions: 79 items
  • +4Has examples (12 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 60.