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Building edition of iaiops — facility / HVAC / BMS / 厂务 over BACnet/IP (ASHRAE 135): Who-Is discovery, object/point lists, presentValue snapshots, COV capture, TrendLog reads, one MOC-gated property write; plus Modbus-TCP/RTU for meters/chillers and optional plain MQTT for IoT sensors, with the cross-protocol brain. Use when the task mentions BACnet, BACnet-IP, HVAC, AHU, chiller, VAV, BMS, building automation, facility management, 厂务, 楼宇自控, Who-Is, presentValue, or TrendLog; also IO-Link masters for smart building sensors (JSON interface, read-only), and the vendor supervisory-controller REST layer above BACnet (Metasys/OpenBlue, Niagara/oBIX) for point/alarm/trend reads and one MOC-gated command. Read-first, MOC-gated writes.

ClawHub Agent Skills author: wei zhou v0.27.0 MIT-0 2 files body ≈ 2 041 tokens Open the sourceclawhub.ai analyzed 29 h ago

Building edition of iaiops — facility / HVAC / BMS / 厂务 over BACnet/IP (ASHRAE 135): Who-Is discovery, object/point lists, presentValue snapshots, COV…

As a process D 48/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

GeneratorSecurityAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
80
Run on models
none yet
Process rating
D
48/100
Unfinished process
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: 2. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note edit-residue the text marks something as outdated (lines 70): check that old rules are not kept next to new ones — the full check reads the text for contradictions

    Process rating: all ten parameters 48/100

    • 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
    • 20When it triggers. No condition that starts the skill
    • 60Tools and files. Uses tools (python) that frontmatter does not declare
    • 100Steps. 48 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2041 tokens
    • 100Running it twice. No mutating operations
    • 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
    • +4No input/output examples
    • +1No license
    • +2Single-language instructions
    • +3Description length 737: enough signal without eating the budget
    • +4Structure: 11 headings
    • +3Step-by-step instructions: 48 items

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 80.

    External checks

    ClawHub: clean
    This skill is a disclosed building-automation operations skill with high-impact control features that are explicitly gated by dry-run and approval steps.
    LLM: benign (high) · VirusTotal: · 3 Sept 2026