SKILLEMALL.ai

CB nudgebell

Reminders that reach the user by email, WhatsApp, SMS or phone call, in the steps and timing they choose. Stops when they acknowledge. Use when the user asks to be reminded.

ClawHub Hermes author: Aditya Ghadge v1.0.0 MIT-0 1 file body ≈ 1 854 tokens Open the sourceclawhub.ai analyzed 2 d ago

Reminders that reach the user by email, WhatsApp, SMS or phone call, in the steps and timing they choose.

As a process B 65/100 · Nearly there — weak spots: result and completion, inputs and preconditions, running it twice

ProcedureWhatsAppAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
C
87/100
safety, quality, tests
Safety 60%
94
Quality 40%
77
Run on models
none yet
Process rating
B
65/100
Nearly there
Result and completion w 14
0
Inputs and preconditions w 11
0
Running it twice w 4
30
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.

Broad scope 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 skill asks for more than the task needs: broad tool access, credential environment variables, binaries. Every extra permission widens the damage from a mistake or a compromise.

For the author

Narrow allowed-tools and the variable list to the minimum; replace binaries with readable sources or scripts.

How to improve

  1. For Hermes the description must be one sentence under 60 characters; move the conditions to a "When to Use" section.
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 · 2

✓ No critical or high findings

Medium and low: 2
  • medium Broad scope meta-requests-env-secret SKILL.md:1
    Skill asks the runtime to inject credential env vars into its sandbox: NUDGEBELL_API_KEY — verify each one is needed for the stated purpose
    required_environment_variables: NUDGEBELL_API_KEY
  • low Exfiltration net-credential-use SKILL.md:67
    Credential used in a network call (verify the destination is the intended service) (the skill's own vendor host; quoted — discussed, not commanded)
    Every request sends `Authorization: Bearer $NUDGEBELL_API_KEY`. Base URL: `https://nudgebell.app/api/v1`.
    vendor-hostquoted

Files scanned: 1. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-long-hermes description is 173 chars; the Hermes authoring standard requires ≤ 60 (one sentence, ending with a period)
  • note frontmatter-key unknown frontmatter key "homepage"
  • note frontmatter-key unknown frontmatter key "required_environment_variables"

Process rating: all ten parameters 65/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 30Running it twice. 16 mutating operations with no state check
  • 60Consistency. The Hermes dialect needs category and tags
  • 70When it triggers. States when to use, but not when not to
  • 100Tools and files. No external tools needed
  • 100Steps. 28 steps
  • 100Failures and branches. 2 branches, has a failure section
  • 100Execution cost. Instruction body is 1854 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
  • +2Single-language instructions
  • +3Description length 173: enough signal without eating the budget
  • +4Structure: 8 headings
  • +3Step-by-step instructions: 28 items
  • +4Has examples (3 code blocks)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 77.

External checks

ClawHub: clean
This skill is a disclosed reminder-service integration that uses the user's NudgeBell API key to create and manage reminders through NudgeBell's own API.
LLM: benign (high) · VirusTotal: · 17 Sept 2026