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phase0-design

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更新时间2026年7月7日 07:52

Run Phase 0 (DESIGN) of the A2MC calibration workflow as the offline agent — the human-in-the-loop analog of the orchestrator's `_run_design()`. Sample the parameter space (Morris/Sobol/LHS), materialize one FATES parameter file per case, generate + build + submit the ensemble, and arm monitoring. Use when the user says "design a new round", "set up the ensemble", "sample the parameters", "submit the Morris ensemble", "start a calibration round", "expand the parameter space (redesign)".

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jingtao-lbl

jingtao-lbl/A2MC-elm

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在 Manus 中运行

Phase 0: Design & Submit (offline agent)

The offline analog of the orchestrator's _run_design(). Online this is deterministic sampling + submission; offline you own the design decisions the online loop takes from config — scheme, trajectory count, which parameters vary, bounds, and whether this is a fresh round or a redesign/replay. The underlying scripts are the same the online agent drives.

Floor, not ceiling. This skill is the minimum to stand up a round the way the online agent would. As the offline agent you can do more: reconsider the parameter list itself, add or widen bounds a redesign implies, sanity-check the sampling against prior-round μ* before spending compute, or design a targeted sub-ensemble the fixed loop would never propose. Match the discipline (verify param existence, don't clobber the committed matrix, validate before submit), then exercise judgment about what to sample.

Deliverable: simulation jobs queued (ADSP→RGSP→TRANS dependency chains), submission_manifest.json written, monitoring armed.

Step 1 — sample the parameter space (parameter-file prep — no simulations)

phases/phase0_design/create_parameter_sample.py --method {morris|sobol|lhs} reads $A2MC_PARAM_LIST_FILE (name + bounds) and writes the ensemble matrix ($A2MC_ENSEMBLE_MATRIX_FILE) and SALib problem ($A2MC_SALIB_PROBLEM_FILE). Morris: --trajectories T (N = T×(P+1)).

Footgun — matrix overwrite. $A2MC_ENSEMBLE_MATRIX_FILE for Kougarok points at the committed, byte-stable 4890-set matrix the manuscript depends on. To regenerate without clobbering it, write to a scratch path (--output-matrix) or repoint the env var to a fresh round file first.

Redesign / replay (reuse a prior round's NC dir instead of fresh sampling): apply_param_override.py (global override, e.g. R5's prescribed_puptake=1.0 onto R3's NCs) or create_subset_replay.py (top-N replay preserving source case numbers).

Step 2 — materialize per-case parameter files (parameter-file prep — no simulations)

phases/phase0_design/generate_parameter_files.py reads the X matrix + $A2MC_BASE_PARAM_FILE and writes one parameter file per row into $A2MC_PARAM_DIR (pattern $A2MC_PARAM_PATTERN, {N} placeholder). It is method- and format-agnostic (JSON at api-43+, NetCDF at api-31 and earlier) and dispatches to tools/modify_fates_parameters.py for the per-parameter edits. Verify file count = N_sets.

Footgun — column↔parameter mapping + file format. The X-matrix column order must match the site parameter list; build_param_lookup() in tools/modify_fates_parameters.py maps each shorthand → FATES name + PFT + organ. Confirm tools/describe_mode.py shows the milestone you expect so the right parameter-file format (JSON vs NetCDF) is written. Never assume a column's meaning.

Step 3 — generate scripts, build, submit the simulations

phases/phase0_design/submit_phase0.py --start 1 --end N --dry-run first (preview), then --submit. It (3a) writes per-case scripts via tools/create_case.sh --write-script, (3b) auto-validates via tools/validate_submission_plan.py, (3c.1) builds ONE case fresh (~30 min), (3c.2) runs the rest in --batch-size parallel batches reusing the build's bld/. Non-sequential rounds: --cases-file.

Footgun — build-case reuse. All cases share the build case's bld/. If the build case fails, every dependent case is orphaned — confirm it completed before the batch. Restart it separately, then --skip-build-case --build-case N.

Step 4 — arm monitoring, don't idle-wait

Launch tools/ensemble_auto_monitor.sh (queue polling + auto extraction kick + milestone plots), then hand monitoring setup to arm-hpc-monitoring (CLAUDE.md Rule #6). Point-in-time completion: tools/diagnose_ensemble_status.py --cases 1-N (writes completed/incomplete lists + a validated restart script). Infrastructure failures → restart-failed-jobs; model failures need a fix first.

Step 5 — log it and hand off

Log via calibration-log (phase log → PhaseLogger.log_design): scheme, N cases, param dir, manifest hash, any redesign rationale. Hand off to phase1-exploration once TRANS is >95% complete. (Arrived here from Phase 6 with all candidates pinned at bounds? That's the 6→0 redesign — widen bounds / add parameters and start a new calibration_round.)

Related skills / next phase

Monitoring the in-flight ensemble → arm-hpc-monitoring. Failed jobs → restart-failed-jobs.
Rebuilding a new site/model's knowledge base first → build-rag-from-scratch.
Next: phase1-exploration (extract Y matrix + Morris sensitivity).

Changelog

2026-07-02: Created — offline Phase 0 routine mirroring _run_design(); drives create_parameter_sample → generate_parameter_files → submit_phase0, delegates monitoring/restart, hands off to phase1-exploration.

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a2mc-init

jingtao-lbl/A2MC-elm

First-run interactive setup for the offline agent — the official "getting started" flow the FIRST time someone uses A2MC in a repo/site. Interview the user (is a2mc_config.sh set up, where is the E3SM/ELM-FATES checkout, FATES on/off, carbon-only vs nutrient-enabled PARTEH, ECA vs RD, site name/location, PFTs, calibration targets), verify the checkout + RAG milestone, then create and populate the use case (site config + validation targets + parameter list) and hand off to phase0-design. Use when the user says "set up A2MC", "first time using A2MC", "help me get started / onboard me to A2MC", "initialize a new site / use case", "configure A2MC for my site", "I want to calibrate a new site". DISTINCT from onboard-session (which resumes an ALREADY-configured setup).

2026-07-076

add-skill

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Scaffold + register (or retire + de-register) an A2MC skill so the registry steps are never half-done. Use when the user or agent wants to create a new capability — "add a skill", "create/scaffold a skill", "make this reusable as a skill", "distill X into a skill" — OR to remove one — "remove/retire/delete the X skill". Writes (or deletes) the SKILL.md, and registers (or de-registers) it across all THREE human-facing registries (the README 'Current skills' table, docs/a2mc_reference/skills_catalog.md, AND the AGENTS.md 'At a glance' table), runs the mechanical drift check, and stops for human review before commit. Human-gated.

2026-07-076

arm-hpc-monitoring

jingtao-lbl/A2MC-elm

Set up real-time monitoring of an active A2MC HPC ensemble or experiment on Perlmutter at session start (CLAUDE.md Rule

2026-07-076

calibration-log

jingtao-lbl/A2MC-elm

Log interactive calibration work for a site under use_cases/{site}/memory/logs/. Two log types — a PHASE log (a diagnosis, screening, experiment design/result, refinement — recorded the same way the autonomous agent does, via PhaseLogger, so both modes' logs synthesize together) or a free-form SESSION log (exploratory work that does not map to a specific phase). Use when the user wants to record calibration or exploration work — "log this", "log this phase / diagnosis / experiment", "log this calibration session", "write a session log", "record what I explored".

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compare-calibration-rounds

jingtao-lbl/A2MC-elm

Compare A2MC calibration rounds (R1, R2, …) against each other and the validation targets — top-N best-achievable biomass per round, per-target Morris μ* sensitivity, P-pool / cross-regime overlays. Use when the user asks to "compare rounds", "update the multi-round figure", "refresh the R1-RN comparison", "which round is best", "top sensitive parameters per round", or to regenerate any `multiround_*` / cross-round figure after new cases complete. Codifies the multiround bundle pipeline + the footguns (screening contamination, case-name patterns, μ* ranking, param-set mismatch, partial-ensemble caveats).

2026-07-076

inject-knowledge

jingtao-lbl/A2MC-elm

Inject a human-originated domain fact (a discovery, a parameter insight, a mechanism/relationship) into A2MC's curated knowledge so the calibration agent surfaces it — placing it correctly across the up-to-three channels (site discoveries.json, generic parameters.json, curated_relationships.yaml), validating, and rebuilding the graph. Use when the user says "add this finding/parameter knowledge to A2MC's AI", "inject the X discovery into the KB", "make the agent aware of X", "author a curated relationship for X", or wants a manuscript/literature insight to reach the reasoning pipeline. This is the HUMAN-originated counterpart to curate-knowledge (which promotes run-originated proposals); both are Tier-3 curated writes and you (the human-in-the-loop) own the truth call. Distilled from graphrag_curated_yaml_roadmap.md + dev log 20260519d (the clumping_index injection).

2026-07-076

name	phase0-design
description	Run Phase 0 (DESIGN) of the A2MC calibration workflow as the offline agent — the human-in-the-loop analog of the orchestrator's `_run_design()`. Sample the parameter space (Morris/Sobol/LHS), materialize one FATES parameter file per case, generate + build + submit the ensemble, and arm monitoring. Use when the user says "design a new round", "set up the ensemble", "sample the parameters", "submit the Morris ensemble", "start a calibration round", "expand the parameter space (redesign)".
modes	{"requires_fates":false,"nutrient_pathway":"any","scope":["calibration"],"summary":"Offline analog of online Phase 0 (design/sample/submit the ensemble). Applies in every calibration mode."}

Phase 0: Design & Submit (offline agent)

Floor, not ceiling. This skill is the minimum to stand up a round the way the online agent would. As the offline agent you can do more: reconsider the parameter list itself, add or widen bounds a redesign implies, sanity-check the sampling against prior-round μ* before spending compute, or design a targeted sub-ensemble the fixed loop would never propose. Match the discipline (verify param existence, don't clobber the committed matrix, validate before submit), then exercise judgment about what to sample.

Deliverable: simulation jobs queued (ADSP→RGSP→TRANS dependency chains), submission_manifest.json written, monitoring armed.

Step 1 — sample the parameter space (parameter-file prep — no simulations)

Footgun — matrix overwrite. $A2MC_ENSEMBLE_MATRIX_FILE for Kougarok points at the committed, byte-stable 4890-set matrix the manuscript depends on. To regenerate without clobbering it, write to a scratch path (--output-matrix) or repoint the env var to a fresh round file first.

Step 2 — materialize per-case parameter files (parameter-file prep — no simulations)

Footgun — column↔parameter mapping + file format. The X-matrix column order must match the site parameter list; build_param_lookup() in tools/modify_fates_parameters.py maps each shorthand → FATES name + PFT + organ. Confirm tools/describe_mode.py shows the milestone you expect so the right parameter-file format (JSON vs NetCDF) is written. Never assume a column's meaning.

Step 3 — generate scripts, build, submit the simulations

Footgun — build-case reuse. All cases share the build case's bld/. If the build case fails, every dependent case is orphaned — confirm it completed before the batch. Restart it separately, then --skip-build-case --build-case N.

Step 4 — arm monitoring, don't idle-wait

Step 5 — log it and hand off

Related skills / next phase

Monitoring the in-flight ensemble → arm-hpc-monitoring. Failed jobs → restart-failed-jobs.
Rebuilding a new site/model's knowledge base first → build-rag-from-scratch.
Next: phase1-exploration (extract Y matrix + Morris sensitivity).

Changelog

2026-07-02: Created — offline Phase 0 routine mirroring _run_design(); drives create_parameter_sample → generate_parameter_files → submit_phase0, delegates monitoring/restart, hands off to phase1-exploration.