Use when a repo does not yet have canonical memory, AGENTS.md, or CLAUDE.md. Bootstraps the standard Codex + Claude Code project memory layout in one step. Triggers: "project-mem-init", "bootstrap memory", "initialize project memory".
Use when evaluating academic papers or surveying a research topic. Gathers venue, citations, GitHub stats, social buzz, reproducibility, and author signals to produce a scored markdown report. Triggers: "evaluate paper", "paper review", "research survey", "literature review", "is this paper good", "find papers on"…
Use when reading large arxiv papers without context overflow. Progressive two-layer reading: index all sections (2k tokens), then deep-read on demand. Never truncates. Triggers: "read paper", "paper sections", "section index", "progressive reading", "papercontent.json", "section summary".
Use when reading a long technical blog post (ML research, engineering deep-dives, Distill/Lil'Log-style posts) and producing a faithful, figure-aware summary. Handles context-over-limit via section-based chunking, captures important figures via multimodal Read, and runs a coverage test to catch missing information.…
Use when reading PDF papers via Claude Code CLI instead of Python converter tools. Delegates PDF-to-text conversion and summarization to Claude Code's native multimodal Read + WebFetch tools, handling multi-page papers via chunked reads and structured prompts. Triggers: "cc-pdf-reader", "claude code pdf", "read pdf…
Use when analyzing the follow-up works of a specific paper — the papers that cite it. Resolves the seed paper via Semantic Scholar, paginates the full citing-paper list, triages by impact + recency, dispatches parallel subagents to tag each follow-up (extension / improvement / application / theoretical / criticism)…
Use when reading a GitHub repository (especially a research code release with an accompanying paper) and producing a faithful digest that covers the implementation logic, the main insight, and the key reported results. Research-first with graceful fallback for non-paper repos. Handles arXiv link detection and…
Use when promoting an idea from a fleeting thought into a tracked artifact with a lifecycle. Defines a per-idea on-disk directory (./ideabox/ /) and a hard-gated state machine (explored → feasible/blocked → building → built/killed) that the existing reader/evaluator skills (idea-feasibility, ml-ablation-design…
Use when proposing new research ideas grounded in a seed paper or method — surfaces the gaps that the citation cone hasn't filled, the drawbacks the community already complains about in the official repo's GitHub issues, and the adjacent angles the literature suggests. Orchestrates followup-analysis (already-done…
Use when evaluating whether a research or product idea is actually feasible — buildable, evaluable, and de-risked by available checkpoints, code, datasets, and GPU budget. Normalizes the idea, gathers primary-source evidence (arXiv, GitHub, project pages, model hosts), scores it against four mandatory hard gates, and…
Use when capturing a single URL (X tweet, LinkedIn post, HN thread, short blog, news article, GitHub gist or issue) as a small persistent markdown entry the user can re-read later for motivation or grep across future sessions. Verbatim body + author + tags + a one-line "Why I saved this" hook. Writes to…
Use when reading PDF papers, reports, or long documents where text, figures, and tables must all be captured and chunk-summarized without truncation. Converts PDF to a markdown + papercontent.json workspace, extracts figures and tables as standalone files, then delegates to arxiv-latex-reader's progressive two-layer…
Use when reading a YouTube video (especially an AI/ML interview, podcast, or technical talk) and producing a faithful, timestamped wiki entry the user can return to weeks later. Fetches the transcript via yt-dlp, sections by chapters or LLM-detected topics, summarizes per-section with parallel subagents, preserves…
Use when optimizing PyTorch training speed or memory on CUDA GPUs — global flags, torch.compile, fused optimizers, mixed precision, gradient checkpointing, kernel fusion, memory layout, or latent-space training. Applies to any PyTorch training workload. Triggers: "torch.compile", "TF32", "fused optimizer", "mixed…
Use when designing ablation studies to compare model components, loss functions, or architectural choices. Covers synthetic data experiments, variant loops, production metrics, and W&B grouping. Triggers: "ablation", "ablation study", "variant comparison", "controlled experiment", "synthetic data experiment".
★not rated 10 2mo agoA65 tokens
At most 3 mods per repository are shown here, and a mod shipped inside a plugin is left to that plugin's page — the rest are on their repository pages: