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Process-industry edition of iaiops — chemical / pharma / food & beverage / oil & gas plants: HART-IP process instrumentation (transmitters, valve positioners, loop current), OPC-UA (DCS / gateway read), Modbus-TCP/RTU (skids, analyzers), optional MQTT/Sparkplug B UNS, plus the cross-protocol brain (downtime root-cause, data quality, OEE). Use when the task mentions HART, HART-IP, transmitter, 变送器, valve positioner, process instrumentation, PV/SV/TV/QV, loop current, burst mode, DCS tap, or a process plant. Read-first, MOC-gated writes.

ClawHub Agent Skills author: wei zhou v0.27.0 MIT-0 2 files body ≈ 1 579 tokens Open the sourceclawhub.ai analyzed 23 h ago

Process-industry edition of iaiops — chemical / pharma / food & beverage / oil & gas plants: HART-IP process instrumentation (transmitters, valve positioners…

As a process D 45/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureSecuritySoftware developmentAI 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
45/100
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: 0. 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 45/100

    • 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
    • 30Running it twice. 1 mutating operations with no state check
    • 60Tools and files. Uses tools (python) that frontmatter does not declare
    • 100Steps. 34 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1579 tokens
    • 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 541: enough signal without eating the budget
    • +4Structure: 9 headings
    • +3Step-by-step instructions: 34 items

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

    External checks

    ClawHub: suspicious
    This industrial diagnostics skill is mostly coherent, but it should be reviewed because it lists export, push, and publish tools without clearly documenting their approval controls.
    LLM: suspicious (high) · 3 Sept 2026