SKILLEMALL.ai

AC todo-accelerator

Use when a user needs to add a new task or record a pending to-do item; when prompting an Agent to execute a pending task; during Agent heartbeat events to pick up and work on tasks; when a user expresses interest in a topic for later research, saves a bookmark for processing, or asks for help preparing deliverables. Not for reminders (eating, sleeping, meetings).

ClawHub Agent Skills author: liusining v0.1.0 MIT-0 9 files · 1 script body ≈ 999 tokens Open the sourceclawhub.ai↗ analyzed 6 d ago

when prompting an Agent to execute a pending task; during Agent heartbeat events to pick up and work on tasks; when a user expresses interest in a topic for…

As a process C 60/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice

ProcedureAI and agentsPersonal productivitytype and topics are labelled automatically from the skill text
JSON
Technical rating
A
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
90
Run on models
none yet
Process rating
C
60/100
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: 9. 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 60/100

    • 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
    • 30Running it twice. 6 mutating operations with no state check
    • 50When it triggers. No condition that starts the skill
    • 55Failures and branches. 1 branches
    • 100Tools and files. No external tools needed
    • 100Steps. 7 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 999 tokens
    • low The response is described with custom markup (3 tags): a typed call is more reliable

    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
    • +3Output format is not stated: the model decides each time
    • -5TODO / placeholder text left in the skill
    • +1No license
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 366: enough signal without eating the budget
    • +4Structure: 10 headings
    • +3Step-by-step instructions: 7 items
    • +4Has examples (9 code blocks)
    • +4Reference files are cited in the instructions (3 of 3)
    • +3All 1 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 90.

    External checks

    ClawHub: suspicious
    This task-board automation skill is coherent, but it needs review because editable task names and notes can drive automatic agent work and can reach Markdown files outside the intended notes folder.
    LLM: suspicious (high) · VirusTotal: · 14 Sept 2026