address-code-review
Address Code Review
Work through code review comments with the user, one comment at a time. Never present multiple comments at once.
Critical rule: Do not make any code changes — not a single edit — until every comment has been triaged with the user. Triage and implementation are strictly separate phases. Implementation only begins after the final comment has been discussed and a decision recorded.
Input sources
Comments may come from:
review.jsonl- A JSONL file at the repo root. Each line is a JSON object:{"base":"<sha>","comment":"...","compare":"<sha>","endLine":45,"file":"main.go","startLine":42}- GitHub PR - Fetch inline and general comments using
gh api - Document in the repo - Parse whatever markdown structure is found
- Conversation - Comments given directly by the user
When no explicit source is specified, check for review.jsonl at the repo root first. If it exists, use it.
Process
More from maragudk/fabrik
diary
Write and maintain an implementation diary capturing what changed, why, what worked, what failed (with exact errors and commands), what was tricky, and how to review and validate. Activates proactively during non-trivial implementation work (new features, bug fixes, refactors, research spikes) and at natural session-end moments -- after a PR merges, a feature ships, or a work chunk wraps up -- to capture the narrative while it's still fresh. Does not activate for trivial tasks like one-line fixes, config tweaks, or quick questions.
3decisions
Guide for recording significant architectural and design decisions in docs/decisions.md. Use this skill when clearly significant architectural decisions are made (database choices, frameworks, core design patterns) or when explicitly asked to document a decision. Also suggest proactively at natural session-end moments -- after a PR merges, a feature ships, or a work chunk wraps up -- if a significant decision was made during the session and not yet recorded. Be conservative - only suggest for major decisions, not minor implementation details.
3unsloth
Guide for fine-tuning LLMs, embedding models, vision-language models, and TTS models efficiently with Unsloth. Covers LoRA/QLoRA SFT, reinforcement learning (GRPO, DPO, ORPO, KTO), embedding fine-tuning with sentence-transformers, continued pretraining, and saving/exporting to GGUF, Ollama, or vLLM. Use this skill whenever the user mentions Unsloth, FastLanguageModel, FastSentenceTransformer, FastVisionModel, FastModel, or wants memory-efficient fine-tuning of open LLMs or embedding models on a single GPU, even if they don't explicitly say "Unsloth".
2garden
Autonomous project gardening. Scans for maintenance issues (starting with documentation), picks one, fixes it in a worktree, self-reviews with competing agents, and opens a PR. Use when the user wants to tidy up the project, fix stale docs, or generally tend the codebase. Invoke with /garden.
2modal
Guide for running Python code on Modal, the serverless compute platform for AI workloads, batch jobs, scheduled tasks, web endpoints, and sandboxed code execution. Use this skill whenever the user is writing or modifying Modal code (anything importing `modal`, decorating with `@app.function`, `@app.cls`, `@modal.fastapi_endpoint`, etc.), running `modal run`/`modal deploy`/`modal serve`, configuring GPUs/images/volumes/secrets for Modal, or asking how to host inference, fine-tuning, or agent sandboxes on Modal.
2sql
Guide for working with SQL queries, in particular for SQLite. Use this skill when writing SQL queries, analyzing database schemas, designing migrations, or working with SQLite-related code.
2