SKILLEMALL.ai

AC hk-bus-eta

Query Hong Kong bus ETA/stop data and MTR heavy rail ETA from natural-language transport questions using official KMB/LWB, Citybus, and MTR open-data endpoints. Use when the user asks in Cantonese, Chinese, or English things like「74X 幾多分鐘後喺九龍灣有車」「A41 去機場而家幾時到青衣站」「城巴 20 喺啟德幾耐到」「火炭站去金鐘方向幾點有車」「金鐘去北角下一班港島線幾時」or any message that combines a bus route with a stop/place, or an MTR station with a destination/direction/ETA intent.

ClawHub Agent Skills author: jimpang8 v0.2.0 MIT-0 3 files body ≈ 1 720 tokens Open the sourceclawhub.ai analyzed 4 d ago

As a process C 61/100 · Has gaps — weak spots: result and completion, inputs and preconditions, consistency

ProcedureInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
87
Run on models
none yet
Process rating
C
61/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Consistency w 8
40
the three weakest of ten parameters · all ten
This is a copy of a skill from another catalog; the rating counts the canonical one: hk-bus-eta (ClawHub)

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: 3. 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 61/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 40Consistency. Frontmatter name (hk-bus-eta) differs from the folder (jim-hk-bus-eta)
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 85Steps. 51 steps, 1 vague phrases
    • 100When it triggers. States when to use and when not to
    • 100Failures and branches. 14 branches, has a failure section
    • 100Execution cost. Instruction body is 1720 tokens
    • 100Running it twice. No mutating operations
    • 100Progress reporting. Reports progress
    • low The response is described with custom markup (4 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
    • +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 424: enough signal without eating the budget
    • +4Structure: 12 headings
    • +3Step-by-step instructions: 51 items
    • +4Has examples (6 code blocks)
    • +3All 1 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.

    External checks

    ClawHub: clean
    This is a focused Hong Kong transit ETA helper that runs a local Python script and queries public transport data APIs.
    LLM: benign (high) · VirusTotal: · 29 May 2026