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Found 6,203 Skills
Audit whether an ML or AI paper's experimental baselines are necessary, fair, current, and reviewer-proof. Use this skill whenever the user is planning experiments, comparing methods, choosing baselines, worried about missing SOTA or unfair comparisons, preparing a reviewer-proof experiment section, or converting a literature review into must-have, should-have, optional, and not-comparable baselines.
Crawlbase integration. Manage data, records, and automate workflows. Use when the user wants to interact with Crawlbase data.
Complete, AI-ready playbook to migrate Motoko projects from mo:base to mo:core — phases, renames, data structure changes, agent strategy, verification scripts, upgrade tests, and production rollout.
Supabase Edge Function observability style: tiny provider-neutral OTel-shaped shim, OTLP export config, traces/logs/metrics, and LLM cost metrics.
Identify codebase deepening opportunities based on the domain language in CONTEXT.md and decisions in docs/adr/. Use when the user wants to improve architecture, find refactoring opportunities, consolidate tightly-coupled modules, or make a codebase more testable and AI-navigable.
Use this skill when > Identify architectural friction and propose deepening opportunities — refactors that turn shallow modules into deep ones for better testability and AI-navigability. Use when improving architecture, finding refactoring opportunities, consolidating tightly-coupled modules, or making a codebase more testable.
Install and use Supabase Agent Skills (`supabase/agent-skills`) with AI coding agents. Covers install modes, skill selection, plugin path, verification, and safe fallback for direct Supabase CLI/database workflows.
Query the JASPAR database for Transcription Factor (TF) binding profiles. Use when retrieving Position Frequency Matrices (PFMs) or Position Weight Matrices (PWMs) for specific TFs, resolving gene symbols to JASPAR Matrix IDs, or getting TF metadata. Supports multiple output formats (MEME, TRANSFAC, PFM, JASPAR, YAML).
Queries the UniBind database for experimentally validated transcription factor (TF) binding sites. Use when retrieving direct TF-DNA interaction datasets, downloading binding site coordinates (BED/FASTA) for local analysis, or listing available datasets by species, cell line, or TF name. Don't use to query specific intervals, locations, genes, motif models or expression data.
Query the STRING database for protein-protein interactions (PPIs), functional enrichment, and homology. Use when the user asks about interactions between specific proteins, interaction evidence, confidence scores, protein interaction partners, or pathway enrichments.
Migrates a project from Metabase static embedding to guest embeds (web components via embed.js). Use when the user wants to migrate/convert/switch/upgrade from static embedding to guest embeds, from signed embed iframes to web components, or replace /embed/ iframes with metabase-dashboard/metabase-question components.
Bump the NVIDIA PyTorch base image (`nvcr.io/nvidia/pytorch:<YY.MM>-py3`) used by Megatron-LM CI. Covers the two pin sites (GitHub CI in `docker/.ngc_version.dev` and GitLab CI in `.gitlab/stages/01.build.yml`), the post-bump CI loop (re-run functional tests, refresh golden values, mark broken tests), and the gotchas that bit PRs