# Dual Target Calibration > Child skill of pfc-workflow for trial-budget-limited calibration with exactly two active micro-parameter levers and two coupled macro targets, using zero-crossing checkpoints, local 2x2 solves, basin recovery, and controlled sensitivity fine-tuning. - Skill: `jiangnan030-del/dual-target-calibration` (Agent Skill, multi-file: 21 files) - Install (CLI): `npx skillmds@latest add jiangnan030-del/dual-target-calibration` - Raw SKILL.md: https://api.skillmd.com/api/skills/jiangnan030-del/dual-target-calibration/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Finance & Business - Author: jiangnan030-del (https://skillmd.com/u/jiangnan030-del) - Updated: 2026-09-21 - Page: https://skillmd.com/skills/jiangnan030-del/dual-target-calibration --- # Dual-Target Calibration Use this child skill through `pfc-workflow` during P3 when a particle or bonded model must match exactly two coupled macro targets with exactly two active levers and a small true-run budget. Typical pairs are peak strength + peak strain, modulus + strength, or another confirmed non-zero target pair. The method is engine-agnostic; PFC execution remains owned by the parent workflow or a project adapter. ## Parent And Sibling Boundaries - `pfc-workflow` owns intake, experiment provenance, case generation, true-run orchestration, campaign recovery, V&V, and delivery. - `dual-target-calibration` owns the two-lever/two-target numerical decision method and its four submission checkpoints. - `pfc-servo-calibration` owns stress/force servo and loading-boundary stability. - `pfc-fast-calibration` owns the PFC3D improved-LPBM 13-factor orthogonal/regression route. - `pfc-workflow` auto-calibration owns LHS, surrogate, Bayesian, RSM, and DE campaigns when more than two levers are active or local two-target assumptions fail. Do not route here for a single target, more than two independent levers, unconfirmed experimental units, or an unrepeatable baseline. ## Required Inputs Before any trial, require: - confirmed target A and B values, units, signs, source rows, and provenance - exactly two active levers X and Y, with finite increasing physical bounds - linked parameters expressed only as `factor * parameter` - all remaining parameters frozen - fixed geometry, random seed, loading path, stop criteria, and output contract - `thresholds` and `budget` copied from `config.example.yaml` into the project - project implementations of `adapters/submit.py`, `check.py`, and `target.py` The installed skill is read-only guidance. Copy config, adapters, and log templates into the project artifact directory; never write trial results into `skills/dual-target-calibration/`. ## When To Use Use this method only when all are true: 1. exactly two macro targets are scored 2. exactly two independent levers are active 3. each run is expensive enough that a small sequential design matters 4. local response surfaces may be discontinuous but still contain identifiable basins 5. the baseline case already runs reproducibly Otherwise return to `pfc-workflow` and select manual family tuning or LHS/surrogate optimization. ## Workflow ### S0 - Configure Project Copies Copy: - `config.example.yaml` -> `/calibration/dual-target/config.yaml` - `adapters/*.py` -> `/calibration/dual-target/adapters/` - `templates/*.md` -> `/calibration/dual-target/logs/` Fill targets, lever bounds, linked/frozen parameters, thresholds, and budget. Adapter templates intentionally block until connected to a real engine and experiment contract. ### S1 - Confirm Targets Implement `target(source, config)` against the confirmed experiment data. Call `write_targets()` only after units, specimen identity, row policy, and compression/tension sign are confirmed. ### S2 - Seed Trial Prefer extrapolation from a confirmed sibling case. Without one, use the physical-bound geometric midpoint and widen the next zero-crossing probes. If either relative error exceeds `thresholds.seed_divergence`, treat the seed as a wrong basin; do not continue local regression from it. ### S3 - Dual Zero-Crossing Collect trials until both target error sets contain values on both sides of zero. The crossing pairs may differ between A and B. **CHECKPOINT 1:** both target errors have mixed signs. No regression before this passes. ### S4 - Local Exact Solve Select three explicitly identified, non-collinear, same-basin trials and run: ```bash python scripts/regress_exact.py trials.csv config.yaml --rows I J K ``` The script rejects same-sign targets, excessive B-error span, rank-deficient designs, rank-deficient target responses, and out-of-bound solutions. **CHECKPOINT 2:** only submit the predicted point when the CLI exits `0`. ### S5 - Basin Recovery When the exact solve detects a basin jump or unsafe solution, collect at least 4-6 deliberately distributed points and run: ```bash python scripts/regress_lstsq.py trials.csv config.yaml ``` **CHECKPOINT 3:** both response surfaces must meet `thresholds.regression_r2`, the design and response matrices must have full rank, and the predicted point must remain inside physical bounds. ### S6 - Controlled Sensitivity Fine-Tune Estimate X sensitivity only from a pair whose Y difference is within `thresholds.sensitivity_max_relative_y_change`: ```bash python scripts/sensitivity.py trials.csv config.yaml ``` **CHECKPOINT 4:** a detected basin jump stops local fine-tuning. Accept the nearest confirmed candidate or return to basin recovery; do not cross the boundary by repeated tiny steps. ### S7 - Archive And Handoff Record every attempted point, including failures, in the project ledger. Hand back to `pfc-workflow`: - confirmed target contract and tolerances - X/Y bounds, linked/frozen parameters, seed, and engine version - complete `trials.csv` with status/failure reason/artifact path - checkpoint outputs and selected regression rows - best confirmed candidate and independent confirmation run - per-target errors and whether both pass - basin/fallback/safety-stop evidence Do not call a result calibrated unless both target tolerances pass on a confirmation run. ## Failure And Budget Rules - `budget.escalate_at` permits a reviewed structural-model change; it does not authorize silently opening a third lever. - `budget.max_trials` is a hard stop. Expanding it requires a new parent-workflow decision. - Failed or timed-out submissions remain in the ledger; check for existing artifacts before retrying. - A regression prediction is a proposal, not evidence. Only a true engine run can pass. - If R2 stays poor, crossings cannot be obtained, or response rank is below 2, return to `pfc-workflow` for LHS/surrogate or model-structure review. ## Local Contents - `config.example.yaml`: project-copy configuration template. - `adapters/`: safe project adapter contracts; no default engine side effects. - `scripts/dual_target_common.py`: validated numerical core. - `scripts/regress_exact.py`: CHECKPOINT 1/2 exact local solve. - `scripts/regress_lstsq.py`: CHECKPOINT 3 basin recovery. - `scripts/sensitivity.py`: CHECKPOINT 4 controlled sensitivity. - `references/parameter-effects.md`: lever-role guidance. - `references/basin-recovery.md`: discontinuous-response diagnosis. - `references/anti-patterns.md`: trial-budget failure modes. - `examples/pfc2d-ctb-fissured.md`: historical method example; qualitative transfer only. - `templates/`: project ledger and experience-log templates. - `LICENSE` and `NOTICE.md`: AGPL-3.0 licensing and provenance for this child skill.