Closes#22
Features:
- references/subagent-experiment-supervision.md: self-healing experiment
pattern with 10-failure catalog, auto-fix implementations, escalation
to Telegram, and harness-specific notes
- references/docker-experiment-isolation.md: resource limits, log
collection, multi-container sweeps, cleanup patterns, Docker Compose
- scripts/Dockerfile: test image for the skill's Docker-based tests
- SKILL.md: CAMPAIGN type in question classifier, Principle #9,
Infrastructure Awareness section, all new references in Available
Resources, updated compatibility field
Test results: 22/22 passing (supervision + Docker build)
Structured protocol for running data science research campaigns:
- Phase 1-8 workflow from problem formulation through synthesis
- Entry/exit criteria and failure modes for every phase
- Executable code examples: sklearn pipelines, PyTorch training loops,
Optuna HP search, distillation, pruning
- See Also references to all companion documents
Part of #22
Standalone CLI that probes GPU (nvidia-smi), CUDA version, PyTorch,
scikit-learn, JAX, Optuna, RAM, and disk space — then generates
structured recommendations for model size, batch size, quantization,
and distillation feasibility.
Ships with 12-test suite (7 local + 5 Docker) covering graceful
degradation, all output flags, and a containerized no-GPU scenario.
Part of #22
PhD-level data science expertise with decision framework, five reference
documents (statistical methodology, experimental design, causal inference,
regression modeling, Bayesian workflow), five automation scripts (power
analysis, assumption diagnostics, model comparison, effect size calculator,
experimental design generator), and two report templates.
Python default with --engine r flag for R output. Dual language support.
Magnus Hedemark
Magnus Hedemark