Loading...
Loading...
Found 5,567 Skills
Use when the user asks to investigate, audit, trace, or explain how a feature, issue, module, workflow, API, config, or behavior works across one or more codebase projects.
KERNEL-based prompt engineering — transforms vague requests into structured, high-performance prompts optimized for first-try success.
Use when the user wants to create or update a DDD-style ubiquitous language glossary, define domain terms, resolve ambiguous terminology, harden naming, or write UBIQUITOUS_LANGUAGE.md from the current conversation and codebase context.
Generous whitespace, consistent padding, and grid-based layouts for clean, readable, and breathing interfaces.
App dashboard with purple-themed aesthetic, top-bar navigation, card-based layouts, and developer-first workflows.
Plans security penetration tests for web applications. Analyzes codebase, API routes, auth implementation, and infrastructure config to generate comprehensive pentest plans. For authorized testing only.
Context-aware translation that preserves tone, style, and natural word order. Use when translating UI strings, documentation, marketing copy, or any multilingual content. Infers register, domain, and style from the source text and surrounding codebase context.
Guide a focused CS or AI literature review sprint that turns a topic, idea, claim, or project direction into a ranked paper map, closest-work risk assessment, method taxonomy, novelty implications, baseline implications, and next actions. Use this skill whenever the user needs to survey a topic, check novelty, map related work, prepare a project, find canonical or recent papers, decide read/skim/ignore priority, or turn papers into a research direction.
Create a new Git branch or code worktree for experiments, features, baselines, rebuttal fixes, or method revisions. Use when starting an isolated code direction, creating a branch, creating a project-aware code worktree under a project control root, or setting up a worktree with UV sync, IDE config copying, linked assets, and worktree memory.
Perform common Git operations safely with sandbox-aware failure handling. Use whenever the user wants to inspect or modify git state, especially for cherry-pick, merge, rebase, commit, branch, stash, or worktree workflows. Always use this skill when the user mentions a Git failure, conflict, cherry-pick, merge issue, worktree, branch checkout problem, lock file, permission denied, operation not permitted, or any case where a sandboxed agent might confuse an environment restriction with a real code conflict. Be proactive: if the task smells like Git state or Git write behavior, use this skill even if the user did not explicitly ask for a 'Git' workflow.
Design hypothesis-driven ML/AI experiments before running them. Use this skill whenever the user wants to plan experiments, ablations, baselines, metrics, controls, seeds, logging, stop conditions, reviewer-proof evidence, or an experiment matrix for a paper claim before using run-experiment or writing results.
Diagnose surprising, negative, unstable, or ambiguous ML/AI experiment results and decide whether to debug implementation, rerun experiments, change metrics or baselines, revise the algorithm, narrow the paper claim, park, or kill a direction. Use this skill whenever results do not match expectations, a method fails, metrics conflict, seeds vary, baselines beat the method, plots look suspicious, or the user asks what to do next after experimental results.