calibration-discipline

Enforces per-cycle and per-round completion checklists for long offline model calibration campaigns.

Updated Aug 28, 2026
One-click install
npx skills add https://github.com/jingtao-lbl/A2MC --skill calibration-discipline-jingtao-lbl
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: calibration-discipline
Source: https://github.com/jingtao-lbl/A2MC/tree/main/.claude/skills/calibration-discipline
Command: npx skills add https://github.com/jingtao-lbl/A2MC --skill calibration-discipline-jingtao-lbl

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Long offline calibration campaigns for process-based environmental models (EcoSIM, PFLOTRAN, ATS, ELM-FATES) drift over many experiment cycles: monitors go unarmed after simulation launches, state files go unvalidated, commits pile up unpushed, and round summaries ship without a next-round plan. This Skill is the definition-of-done checklist that keeps every cycle and round consistent. ## Core Features & Use Cases - Per-cycle checklist: Verifies each phase is logged with its phase skill into log/{stem}.md plus a self-documenting phase_results/{stem}/ folder, that analysis is first-hand, that tape variables are reduced per their own temporal semantics, and that workflow_state is updated, validated, committed, and pushed after every phase. - Per-round checklist: Enforces the ordered round-close sequence (summarize-calibration-round, compare-calibration-rounds, write-report) and requires the round summary to propose a concrete next-round work plan covering parameter add/remove, bounds recentering, base updates, and residual routing. - Config-driven loop limits: Reads A2MC_MAX_SKIP_TESTING, A2MC_MAX_EXPERIMENTS, and A2MC_CONFIDENCE_THRESHOLD from the sourced machine config rather than hardcoded literals. - Use Case: While running a multi-round EcoSIM calibration on an HPC cluster, re-check this Skill at every phase transition to confirm monitors are armed after each Phase-5 launch, the rethink protocol is answered on 6-to-3 routings, and the cycle-end synthesis report exists. ## Quick Start Ask the agent to run the calibration-discipline checklist against the current experiment cycle to verify every phase was logged, validated, committed, and pushed before advancing the loop.

Frequently Asked Questions about calibration-discipline

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I keep a long offline model calibration campaign from drifting?▼

Run a per-cycle and per-round discipline checklist at every phase transition. Verify each phase is logged with its phase skill, the workflow state is validated and pushed, monitors are armed after every simulation launch, and each cycle ends with a synthesis report.

What is the difference between calibration-goal and calibration-discipline?▼

calibration-goal is the driver that decides what runs next and advances the loop state. calibration-discipline is the definition-of-done checklist the driver must honor so each cycle is logged, validated, and reported consistently. The two are complementary.

Which models does this calibration checklist support?▼

It is model-agnostic and applies to EcoSIM, PFLOTRAN, ATS, and CIME-configured models like ELM-FATES. Loop limits come from the sourced machine config (a2mc_config.sh or a2mc_noncime_config.sh), not from the checklist itself.

When is a calibration round actually complete?▼

A round is done only when experiment_count equals max_experiments or the state is converged, never on a self-declared exhaustion judgment. The round then closes with summarize-calibration-round, compare-calibration-rounds, and a round report proposing the next-round work plan.

Why must output tape variables be verified before reduction?▼

A tape field's name and units do not reveal whether it is active or its temporal semantics (rate, increment, resetting-cumulative, run-cumulative, or stock). Applying one reduction to all five types produces wrong values, so each variable's semantics must be confirmed in the knowledge base and model source first.