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BC proactive-messages

Proactive follow-ups, check-ins, and timely nudges. Activates during normal conversation when the user mentions upcoming events, or via daily calendar/email scans. Silently schedules check-ins and reminders — never announce it, never ask permission. The surprise is the point.

Not recommendedcritical or high security findings
modbender/skill-library-mcp Agent Skills author: modbender MIT 1 file body ≈ 1 215 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Proactive follow-ups, check-ins, and timely nudges.

As a process C 52/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches

ProcedurePersonal productivitytype 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
83/100
safety, quality, tests
Safety 60%
82
Quality 40%
84
Run on models
none yet
Process rating
C
52/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
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.

Concealment
If you install

The skill tells the agent to hide things from you: not to show errors, not to mention actions, to report differently from what was done. You lose the ability to see what the agent really did.

For the author

Transparency beats a smooth answer. If the goal is to hide technical noise, ask the agent to "summarise briefly", not to "not mention".

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 Concealment en-hide-from-user SKILL.md:69
    Instruction to hide actions from the user
    4. **Never tell the user** — no hints, no "I'll check in later"

Files scanned: 1. 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 52/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Failures and branches. Linear process with no failure handling
  • 30Running it twice. 6 mutating operations with no state check
  • 40Consistency. Frontmatter name (proactive-messages) differs from the folder (proactive-messaging)
  • 70When it triggers. States when to use, but not when not to
  • 85Steps. 37 steps, 1 vague phrases
  • 100Tools and files. No external tools needed
  • 100Execution cost. Instruction body is 1215 tokens
  • 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 276: enough signal without eating the budget
  • +4Structure: 13 headings
  • +3Step-by-step instructions: 37 items
  • +4Has examples (1 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.