SKILLEMALL.ai
GitHub ↗ RU Sign in

CC obverse-payments

End-to-end stablecoin payments — links, invoices, receipts, dashboards — across Telegram, WhatsApp, Discord

Not recommendedcritical or high security findings
modbender/skill-library-mcp Agent Skills author: modbender MIT 8 files · 1 script body ≈ 3 453 tokens Open the sourcegithub.com↗ analyzed 2 d ago

End-to-end stablecoin payments — links, invoices, receipts, dashboards — across Telegram, WhatsApp, Discord

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

ProcedureTelegramDiscordWhatsAppData and analyticstype and topics are labelled automatically from the skill text
Runs in: OpenClaw 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) Goose OpenCode
JSON
Technical rating
C
70/100
safety, quality, tests
Safety 60%
75
Quality 40%
63
Run on models
none yet
Process rating
C
51/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
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.

Dangerous commands
If you install

The skill contains commands that delete files, rewrite disks or execute code fetched from the network. The agent may run them without asking if it believes the instructions require it.

For the author

Replace destructive commands with safe equivalents that ask for confirmation, scope them to one folder, and stop piping curl into a shell: pin a version and a checksum.

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. 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.
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 · 4

  • high Dangerous commands cmd-pipe-to-shell DEPLOYMENT.md:86
    Downloads and executes remote code from an unrecognised host (pipe to shell)
    curl -fsSL https://openclaw.ai/install.sh | bash
Medium and low: 3
  • medium Exfiltration net-redirectable-api-key obverse-cli.js:11
    Helper sends the API key to a host configured by an environment variable — the key can be redirected to another server
    API key + configurable base URL from environment
  • low Secrets in code secret-password-literal DEPLOYMENT.md:108
    Hard-coded password / key literal (may be an example) (placeholder value)
    const apiKey = `obv_sk_${randomBytes(32).toString('hex')}`;
    placeholder
  • low Secrets in code secret-password-literal SKILL.md:462
    Hard-coded password / key literal (may be an example)
    Password: AbC1…456

Files scanned: 8. 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")
  • note frontmatter-key unknown frontmatter key "homepage"

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
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 50Failures and branches. 0 branches, has a failure section
  • 60Tools and files. Uses tools (bash) that frontmatter does not declare
  • 100Steps. 30 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 3453 tokens
  • 100Running it twice. Mutating operations check current state
  • low 13 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 107: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • -219 emoji in the instructions: noise for the model
  • -31 of 1 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +4Structure: 35 headings
  • +3Step-by-step instructions: 30 items
  • +4Has examples (26 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 63.