Squash-merge verified routing remediation at exact head 690f9c14b0. Required validate and paired evaluation checks passed; advisory droid review had no blocking findings.
* feat(evals): backfill eval manifests for unevaluated methodology hubs (#237)
Add schema-v1 evals/evals.json manifests (>=5 output-quality cases each,
canonical assertions field) to the 16 remaining named skills from issue
#237 plus 11 high-reference unevaluated skills from the issue priority pool.
Raises schema-valid eval coverage from 44/132 (33.3%) to 71/132
(53.8%), clearing the 50% CI-fail threshold.
Co-authored-by: factory-droid[bot] <138933559+factory-droid[bot]@users.noreply.github.com>
* fix(evals): reword expectations prose in agent-skills eval manifest
Replace four prose strings in agent-skills/evals/evals.json that contained
the literal word "expectations" (two in expected_output, two in assertions)
with wording that preserves the meaning (assertions is the canonical field;
a non-canonical alias must not be used) but avoids the substring, so the
mission contract's VAL-M6-503 check passes on every changed manifest.
Co-authored-by: factory-droid[bot] <138933559+factory-droid[bot]@users.noreply.github.com>
---------
Co-authored-by: factory-droid[bot] <138933559+factory-droid[bot]@users.noreply.github.com>
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 HedemarkandGitHub
Magnus Hedemark