evo-reflow-thermal-analysis
SkillFiles & storageProcesses thermocouple CSV data, MES logs, and test defect data to compute preheat ramp rates, TAL, peak temperatures, conveyor speed compliance, and best-run selection per board family.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the evo-reflow-thermal-analysis skill
What this skill tells your AI
The instructions your AI receives, as published by openlair/openskill in tasks-evolved/manufacturing-equipment-maintenance/environment/skills/evo-reflow-thermal-analysis/SKILL.md and read by ahel’s review.
Purpose
All CSV data loading, merging, time-series computation, and compliance checks.
Key Functions
load_thermocouple_data()- Load TC CSVload_mes_log()- Load MES CSVload_test_defects()- Load defects CSVcalculate_ramp_rates(tc_df)- Compute dT/dt per intervalget_max_preheat_ramp_by_run(tc_df, preheat_max_c=150.0)- Max ramp in preheat zone using largest_mass TCcalculate_tal_by_run(tc_df, mes_df)- TAL using largest_mass TC and per-run liquiduscheck_peak_temp_compliance(tc_df, mes_df, peak_margin_c=20.0)- Peak temp checkcheck_conveyor_speed_compliance(mes_df)- Min speed feasibilityfind_best_run_per_board_family(mes_df, defects_df, tc_df)- Best run selection
Usage
import sys
sys.path.insert(0, '/app/environment/skills/evo-reflow-thermal-analysis/scripts')
from utils import load_thermocouple_data, get_max_preheat_ramp_by_run
tc_df = load_thermocouple_data()
ramps = get_max_preheat_ramp_by_run(tc_df)
Signals
- GitHub stars
- 89
- Forks
- 4
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
evo-reflow-thermal-analysis- Source
- github.com/openlair/openskill