SKILLEMALL.ai

BC iaiops

Vendor-neutral, governed industrial/OT data tap + intelligent troubleshooting. Read (and, gated, write) PLCs, controllers, machine tools and IIoT brokers over OPC-UA, Modbus-TCP, Siemens S7comm, Mitsubishi MC, Omron FINS, MTConnect, MQTT/Sparkplug B, Allen-Bradley EtherNet/IP, EtherCAT (pysoem/SOEM), SECS/GEM (semiconductor / display fab equipment over HSMS), PROFINET (DCP discovery), the building edition (BACnet/IP), and Phoenix Contact PLCnext vPLC — plus cross-protocol diagnostics ("no-data" dataflow diagnosis, OPC-UA connection self-diagnosis, subscription health, ISA-18.2 alarm bad-actors, tag/historian health, and the AI downtime root-cause copilot) and analytics (OEE/downtime, asset inventory, OPC-UA HDA, change-of-value). Use when the task names any industrial protocol, a PLC/SCADA/HMI/historian/CNC/RTU/IED, a semiconductor/display fab or SECS/GEM equipment, an electrical substation, an opc.tcp:// or mqtt:// endpoint, OEE/downtime, downtime root-cause, or OT asset inventory. Routes to the iaiops MCP server. Read-first; writes are MOC-gated (high risk, dry-run + double-confirm). Do NOT use for IT/network gear, Kubernetes, hypervisors, or backups — those are separate AIops tools.

ClawHub Agent Skills author: wei zhou v0.27.0 MIT-0 2 files body ≈ 1 342 tokens Open the sourceclawhub.ai analyzed 2 d ago

Vendor-neutral, governed industrial/OT data tap + intelligent troubleshooting.

As a process C 50/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches

AnalyzerKubernetesAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
B
82/100
safety, quality, tests
Safety 60%
100
Quality 40%
56
Run on models
none yet
Process rating
C
50/100
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

  1. Shorten the description to 1024 characters.
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

  • error description-long description is 1204 chars, limit 1024

Process rating: all ten parameters 50/100

  • 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
  • 50When it triggers. No condition that starts the skill
  • 60Tools and files. Uses tools (python) that frontmatter does not declare
  • 100Steps. 15 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1342 tokens
  • 100Running it twice. No mutating operations

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
  • +3Description length 1204: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • +4No input/output examples
  • +1No license
  • +2Single-language instructions
  • +4Description says when NOT to use the skill
  • +4Structure: 7 headings
  • +3Step-by-step instructions: 15 items

Quality base 70; lint remarks subtract, signals add up to 100. Result: 56.

External checks

ClawHub: clean
This skill is a disclosed router for industrial operations tooling, with high-impact write actions described as gated and user-controlled.
LLM: benign (high) · VirusTotal: · 3 Sept 2026