tsugu

Natural language constraints → deterministic test artifacts. Write constraints in structured Markdown, generate unit tests / PBT / mutation tests, enforce with deterministic tools.

3 skills

Assisted review loop — local reviewers answer blind and in parallel, each returns its own convergence verdict (loop-until-dry, facilitator owns K); a behaviour claim about a runtime prompt is settled by five independent simulation runs, not by a string comparison; a settled finding keeps the command that settled it, and the loop re-runs every kept command at the start of each round and before each commit; a finding no command settled keeps nothing; roster enrolled per host via /review-loop:init (routine or on-request reviewers, a private-content rule for each); the author-stop shape is published at A0 and then held (author pass, one batched message per round that has a T2/T3 finding, the hand-off, plus named exceptions that re-publish it), and a T3 that comes back after a fix is read as a drift signal: that round's message re-confirms the direction of the whole change with the author, as well as asking for the item's own fix; never merges autonomously

1 skill · 2 commands

FP-thinking review and refactor rules for pre-RSC React projects

1 skill · 1 command

Describe multi-step agent workflows using an Arrow-style DSL (>>>, ***, &&&, |||, ?, loop, \, let...in, (), ;) with epistemic operator conventions and structural validation

1 skill

Explore an unfamiliar codebase together with the user, one step at a time, through its data flow and its types

1 skill

Socratic dialogue for reflecting on human-AI stewardship

1 skill

Commands for git commit workflows including commit, push, and PR creation

3 commands

Agent teams, swarm coordination, Monitor streams, and worktree isolation

2 skills · 2 commands · 2 agents

Perform AI code review on Git diffs — supports workspace changes, branch ranges, and single commits with concurrent per-file analysis, codebase search, and deep context-aware review.

2 commands

DataRobot skills for AI/ML workflows — model training, deployment, predictions, feature engineering, monitoring, explainability, data preparation, App Framework CI/CD, and external agent monitoring.

19 skills

Eval-gated LLM fine-tuning lifecycle (LoRA/QLoRA, DPO, GRPO/RLVR, vision SFT, quantized export). Use when fine-tuning an open-weights model end-to-end: building the eval harness first, selecting method and model, preparing data, training with Unsloth or TRL, gating checkpoints, and exporting GGUF/FP8.

10 skills · 2 commands · 3 agents

DataRobot skills for AI/ML workflows — model training, deployment, predictions, feature engineering, monitoring, explainability, data preparation, App Framework CI/CD, and external agent monitoring.

19 skills