SKILLEMALL.ai

AC sleep-routine-coach

Provide privacy-first, non-diagnostic sleep habit coaching with a short setup, consent-gated wind-down reminders, and gradual sleep-time adjustment that does not require a fixed sleep duration. Build deterministic 15- or 30-minute reminder stages and require user confirmation before advancing. Use goodnight and morning messages as low-friction data collection for descriptive trend analysis, with local records, corrections, reminder controls, and weekly summaries. Use when a user wants gentler bedtime cues, wants to move a late or early sleep time gradually, says goodnight or good morning in an established coaching context, manages sleep data, or requests explicitly authorized OpenClaw Cron reminders.

ClawHub Agent Skills author: Rain Dong v0.2.1 MIT-0 14 files body ≈ 1 704 tokens Open the sourceclawhub.ai analyzed 29 h ago

Provide privacy-first, non-diagnostic sleep habit coaching with a short setup, consent-gated wind-down reminders, and gradual sleep-time adjustment that does…

As a process C 54/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches

AnalyzerPersonal productivitytype and topics are labelled automatically from the skill text
JSON
Technical rating
A
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
88
Run on models
none yet
Process rating
C
54/100
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

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 · 0

    ✓ No critical or high findings

    Files scanned: 0. 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 54/100

    • 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
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 5 mutating operations with no state check
    • 50When it triggers. No condition that starts the skill
    • 100Tools and files. Tools declared in frontmatter
    • 100Steps. 27 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1704 tokens
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

    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
    • +4No input/output examples
    • +2Single-language instructions
    • +3Description length 709: enough signal without eating the budget
    • +4Structure: 7 headings
    • +3Step-by-step instructions: 27 items
    • +4Reference files are cited in the instructions (4 of 4)
    • +3All 1 scripts are documented
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.

    External checks

    ClawHub: suspicious
    This sleep coaching skill is mostly coherent and consent-based, but its delete-all command can recursively delete whatever directory is passed as the data directory.
    LLM: suspicious (high) · VirusTotal: · 29 Jul 2026