SKILLEMALL.ai

AC dr-schedule-manager

Design and implement reliable scheduled or event-triggered automations for OpenClaw agents so changes to model, prompt, delivery, and policy take effect immediately on the next run. Use when cron jobs, daily briefings, reminders, digests, or background agents keep using stale models, stale prompts, stale session state, or detached execution contexts. Also use when standardizing automation architecture across multiple agents or converting brittle time-triggered workflows into reusable config-driven jobs.

ClawHub Agent Skills author: Daniel Refahi v1.1.0 MIT-0 8 files body ≈ 4 784 tokens Open the sourceclawhub.ai analyzed 3 d ago

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

ProcedureAI and agentstype 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
52/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
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: 8. 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
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 3 mutating operations with no state check
    • 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
    • 70Execution cost. Instruction body is 4784 tokens
    • 100Steps. 228 steps
    • 100Failures and branches. 7 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • low 15 top-level sections: this looks like several domains in one skill

    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 508: enough signal without eating the budget
    • +4Structure: 41 headings
    • +3Step-by-step instructions: 228 items
    • +4Has examples (3 code blocks)
    • +4Reference files are cited in the instructions (4 of 4)

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

    External checks

    ClawHub: clean
    This is a scheduling architecture guide with disclosed automation and delivery considerations, not an executable installer or hidden runtime component.
    LLM: benign (high) · VirusTotal: · 29 Jun 2026