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BC rem

Manages macOS Reminders from the terminal using the rem CLI. Creates, lists, updates, completes, deletes, searches, and exports reminders and lists. Supports natural language due dates, filtering, import/export, and multiple output formats. Use when the user wants to interact with Apple Reminders via command line, automate reminder workflows, or build scripts around macOS Reminders.

Not recommendedcritical or high security findings
modbender/skill-library-mcp Agent Skills author: modbender MIT 3 files body ≈ 1 403 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Manages macOS Reminders from the terminal using the rem CLI.

As a process C 53/100 · Has gaps — weak spots: inputs and preconditions, failures and branches, consistency

IntegrationGitHubSoftware developmentData and analyticsAI 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
B
86/100
safety, quality, tests
Safety 60%
82
Quality 40%
91
Run on models
none yet
Process rating
C
53/100
Has gaps
Inputs and preconditions w 11
0
Failures and branches w 10
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.

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.
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 Dangerous commands cmd-pipe-to-shell SKILL.md:18
    Downloads and executes remote code from an unrecognised host (pipe to shell)
    curl -fsSL https://rem.sidv.dev/install | bash

Files scanned: 3. 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 53/100

  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Failures and branches. Linear process with no failure handling
  • 0Progress reporting. Says nothing while it works
  • 30Running it twice. 10 mutating operations with no state check
  • 40Consistency. Frontmatter name (rem) differs from the folder (rem-cli)
  • 60Tools and files. Uses tools (bash) that frontmatter does not declare
  • 60Result and completion. Output format stated, no completion criterion
  • 70When it triggers. States when to use, but not when not to
  • 100Steps. 7 steps
  • 100Execution cost. Instruction body is 1403 tokens

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)
  • +1No license
  • +2Single-language instructions
  • +3Description length 385: enough signal without eating the budget
  • +4Structure: 21 headings
  • +3Step-by-step instructions: 7 items
  • +3Output format is stated explicitly
  • +4Has examples (8 code blocks)
  • +4Reference files are cited in the instructions (2 of 2)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 91.