SKILLEMALL.ai
GitHub ↗ RU Sign in

AC bring-list

Manage Bring! shopping lists (Einkaufsliste / grocery list) — add, remove, check off items, batch ops, default list support. Use when: user wants to set up Bring!, add items to shopping list, check what's on the list, or complete/remove items. Full guided setup in Telegram: agent handles login, list selection and config entirely in chat. Privacy-first: credentials via chat or private terminal input — your choice, never repeated. Terminal optional.

modbender/skill-library-mcp Agent Skills author: modbender MIT 4 files · 1 script body ≈ 1 958 tokens Open the sourcegithub.com↗ analyzed 3 d ago

Manage Bring! shopping lists (Einkaufsliste / grocery list) — add, remove, check off items, batch ops, default list support. Use when: user wants to set up…

As a process C 59/100 · Has gaps — weak spots: result and completion, inputs and preconditions

ProcedureTelegramAI and agentstype 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
A
91/100
safety, quality, tests
Safety 60%
93
Quality 40%
87
Run on models
none yet
Process rating
C
59/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
When it triggers w 12
50
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.

Secrets in code 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 files contain someone else's key or token. If it is live, your agent will call third-party services under a stranger's identity; if it was revoked, the skill's scripts simply fail. Such a key often arrives with the author's whole workspace, personal data included.

For the author

The key is visible to everyone who downloaded the skill and has likely been copied by catalog-scanning bots already. Revoke it now, check bills and access logs, then reissue.

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 · 3

    ✓ No critical or high findings

    Medium and low: 3
    • medium Secrets in code secret-labelled-token scripts/bring.sh:12
      Labelled token / key literal (vendor format unknown — verify it is not a live credential)
      API_KEY="cof4…5Sp"
    • low Secrets in code secret-high-entropy-token scripts/bring.sh:12
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      API_KEY="cof4…5Sp"
      quoted
    • low Secrets in code secret-password-literal scripts/bring.sh:12
      Hard-coded password / key literal (may be an example)
      API_KEY="cof4…5Sp"

    Files scanned: 4. 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 59/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 50When it triggers. No condition that starts the skill
    • 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
    • 70Failures and branches. 4 branches
    • 100Steps. 19 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1958 tokens
    • 100Running it twice. Mutating operations check current state
    • 100Progress reporting. Reports progress

    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
    • +1No license
    • +2Single-language instructions
    • +3Description length 451: enough signal without eating the budget
    • +4Structure: 15 headings
    • +3Step-by-step instructions: 19 items
    • +4Has examples (9 code blocks)
    • +3All 1 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.