Other

co2-tank-monitor

IoT monitoring simulation to predict CO2 tank depletion and prevent weekend gas outages in cell culture facilities. Monitors cylinder pressure, calculates consumption rates, provides early warnings, and supports automated scheduling via cron.

87100Total Score
Core Capability
86 / 100
Functional Suitability
11 / 12
Reliability
11 / 12
Performance & Context
7 / 8
Agent Usability
14 / 16
Human Usability
7 / 8
Security
10 / 12
Maintainability
12 / 12
Agent-Specific
14 / 20
Medical Task
12 / 12 Passed
88Check CO2 tank status: 8.5 MPa pressure, 1.2 MPa/day consumption
4/4
88Pre-weekend check: 3.5 MPa pressure, 1.5 MPa/day, Friday afternoon
4/4
87Request with pressure in PSI units instead of MPa
4/4

Veto GatesRequired pass for any deployment consideration

Skill Veto✓ All 4 gates passed
✓
Operational Stability
System remains stable across varied inputs and edge cases
PASS
✓
Structural Consistency
Output structure conforms to expected skill contract format
PASS
✓
Result Determinism
Equivalent inputs produce semantically equivalent outputs
PASS
✓
System Security
No prompt injection, data leakage, or unsafe tool use detected
PASS

Core Capability86 / 100 — 8 Categories

Functional Suitability
Auto unit detection (PSI/Bar) now in workflow step 2; constant consumption rate constraint mandated in Output Requirements
11 / 12
92%
Reliability
PSI/Bar auto-conversion with explicit assumption statement; input range validation documented; simulation mode fallback
11 / 12
92%
Performance & Context
SKILL.md 178 lines — lean; pressure conversion reference table is efficient
7 / 8
88%
Agent Usability
Clear workflow with unit detection step; status level table excellent; constant consumption constraint now mandated in Output Requirements
14 / 16
88%
Human Usability
Description is discoverable; lab-specific terminology is appropriate for the target audience
7 / 8
88%
Security
No hardcoded secrets; no injection vectors; cron example uses shell variable safely
10 / 12
83%
Maintainability
Clean function separation; formula documented; unit detection logic documented in workflow
12 / 12
100%
Agent-Specific
Trigger precision good; escape hatches for temperature control and inventory management present; simulation mode is a strong composability feature
14 / 20
70%
Core Capability Total86 / 100

Medical TaskExecution Average: 87.7 / 100 — Assertions: 12/12 Passed

88
Canonical
Check CO2 tank status: 8.5 MPa pressure, 1.2 MPa/day consumption
4/4 ✓
88
Variant A
Pre-weekend check: 3.5 MPa pressure, 1.5 MPa/day, Friday afternoon
4/4 ✓
87
Edge
Request with pressure in PSI units instead of MPa
4/4 ✓
88
Canonical✅ Pass
Check CO2 tank status: 8.5 MPa pressure, 1.2 MPa/day consumption

Constant consumption rate constraint now included in output per updated Output Requirements

Basic 36/40|Specialized 52/60|Total 88/100
✅A1Output calculates remaining days correctly (8.5/1.2 ≈ 7.1 days)
✅A2Output provides depletion datetime
✅A3Output includes weekend risk assessment
✅A4Output includes constant consumption rate constraint
Pass rate: 4 / 4
88
Variant A✅ Pass
Pre-weekend check: 3.5 MPa pressure, 1.5 MPa/day, Friday afternoon

Output completed successfully; pre-weekend check: 3.5 mpa pressure, 1.5 mpa/day, friday afternoon case handled within expected scope.

Basic 36/40|Specialized 52/60|Total 88/100
✅A1Output correctly identifies high weekend risk (depletion Saturday/Sunday)
✅A2Output recommends immediate replacement or weekend duty
✅A3Output assigns danger status (code 2)
✅A4Output includes actionable recommendation
Pass rate: 4 / 4
87
Edge✅ Pass
Request with pressure in PSI units instead of MPa

PSI auto-converted to MPa per updated workflow step 2; assumption stated explicitly

Basic 36/40|Specialized 51/60|Total 87/100
✅A1Output detects PSI input and flags unit mismatch
✅A2Output provides the conversion factor (1 PSI = 0.0069 MPa)
✅A3Output automatically converts PSI to MPa and proceeds with calculation
✅A4Output does not fabricate converted values
Pass rate: 4 / 4
Medical Task Total87.7 / 100

Key Strengths

  • Weekend risk detection with specific day-of-week scenarios (Saturday/Sunday vs Monday morning) is highly practical
  • PSI/Bar auto-conversion now implemented in workflow step 2 with explicit assumption statement
  • Constant consumption rate constraint now mandated in every output per updated Output Requirements
  • Simulation mode (--simulate) enables staff training without real sensor data
  • Cron integration example with pre-weekend check (Friday 5 PM) is production-ready