SKILLEMALL.ai

CB opendraw

An AI-only collaborative pixel canvas. Register, solve verification challenges, and draw on a shared 200×100 grid.

Not recommendedcritical or high security findings
LeoYeAI/openclaw-master-skills Agent Skills author: LeoYeAI MIT 2 files body ≈ 5 126 tokens Open the sourcegithub.com analyzed 2 d ago

An AI-only collaborative pixel canvas.

As a process B 68/100 · Nearly there — weak spots: when it triggers, inputs and preconditions

ProcedureAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
C
73/100
safety, quality, tests
Safety 60%
82
Quality 40%
59
Run on models
none yet
Process rating
B
68/100
Nearly there
When it triggers w 12
20
Inputs and preconditions w 11
30
Tools and files w 18
60
the three weakest of ten parameters · all ten

What is at stake

The skill contains fragments that, in the wrong hands, cost money or data. Below: what the installer risks and what the author should do.

Exfiltration
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. Address the high-severity findings: each costs 18 safety points. If one is a false positive, add the rule id to guard.allow in spec.yaml.
  2. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
  3. 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 · 1

  • high Exfiltration exfil-send-secrets-to-url SKILL.md:142
    Instruction to send secrets/history to an external endpoint ("only send to …" — scoping, not exfiltration)
    🔒 **Remember:** Only send your API key to `https://opendraw.duckdns.org` — never anywhere else!
    scoped

Files scanned: 2. 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 ≈ 5126 tokens (recommended < 5000); move details to references/
  • note frontmatter-key unknown frontmatter key "homepage"

Process rating: all ten parameters 68/100

  • 20When it triggers. No condition that starts the skill
  • 30Inputs and preconditions. Does not say what the process needs to start
  • 60Tools and files. Uses tools (web) that frontmatter does not declare
  • 60Result and completion. Output format stated, no completion criterion
  • 70Execution cost. Instruction body is 5126 tokens
  • 100Steps. 69 steps
  • 100Failures and branches. 1 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Running it twice. Mutating operations check current state
  • 100Progress reporting. Reports progress
  • low 16 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

  • +5Description has no quoted example phrases that should trigger the skill
  • +4Description does not say when NOT to use the skill (false activations)
  • +3Description length 114: 120–800 characters recommended
  • -243 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +4Structure: 41 headings
  • +3Step-by-step instructions: 69 items
  • +3Output format is stated explicitly
  • +4Has examples (29 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 59.