Sponsored by

Find leads on Reddit on auto pilot

Topic: codex

8,457 skills in this topic.

golang-data-structures Golang data structures — slices (internals, capacity growth, preallocation, slices package), maps (internals, hash buckets, maps package), arrays, container/list/heap/ring, strings.Builder vs bytes.Buffer, generic collections, pointers (unsafe.Pointer, weak.Pointer), and copy semantics. Use when choosing or optimizing Go data structures, implementing generic containers, using container/ packages, unsafe or weak pointers, or questioning slice/map internals.
samber/cc-skills-golang 1,150
golang-observability Golang everyday observability — the always-on signals in production. Covers structured logging with slog, Prometheus metrics, OpenTelemetry distributed tracing, continuous profiling with pprof/Pyroscope, server-side RUM event tracking, alerting, and Grafana dashboards. Apply when instrumenting Go services for production monitoring, setting up metrics or alerting, adding OpenTelemetry tracing, correlating logs with traces, migrating legacy loggers (zap/logrus/zerolog) to slog, adding observability to new features, or implementing GDPR/CCPA-compliant tracking with Customer Data Platforms (CDP). Not for temporary deep-dive performance investigation (→ See golang-benchmark and golang-performance skills).
samber/cc-skills-golang 1,150
golang-benchmark Golang benchmarking, profiling, and performance measurement. Use when writing, running, or comparing Go benchmarks, profiling hot paths with pprof, interpreting CPU/memory/trace profiles, analyzing results with benchstat, setting up CI benchmark regression detection, or investigating production performance with Prometheus runtime metrics. Also use when the developer needs deep analysis on a specific performance indicator - this skill provides the measurement methodology, while golang-performance provides the optimization patterns.
samber/cc-skills-golang 1,150
golang-performance Golang performance optimization patterns and methodology - if X bottleneck, then apply Y. Covers allocation reduction, CPU efficiency, memory layout, GC tuning, pooling, caching, and hot-path optimization. Use when profiling or benchmarks have identified a bottleneck and you need the right optimization pattern to fix it. Also use when performing performance code review to suggest improvements or benchmarks that could help identify quick performance gains. Not for measurement methodology (see golang-benchmark skill) or debugging workflow (see golang-troubleshooting skill).
samber/cc-skills-golang 1,150
agent-repo-init One-click initialization of a multi-agent repository from the Antigravity template. Use this skill when users want to scaffold a new project quickly (`quick` mode) or with runtime defaults (`full` mode) including LLM provider profile, MCP toggle, swarm preference context, sandbox type, and optional git init.
study8677/antigravity-workspace-template 1,117
research
study8677/antigravity-workspace-template 1,117
knowledge-layer
study8677/antigravity-workspace-template 1,117
graph-retrieval
study8677/antigravity-workspace-template 1,117
agent-repo-init
study8677/antigravity-workspace-template 1,117
capture-knowledge Capture structured knowledge about a code entry point and save it to the knowledge docs. Use when users ask to document, understand, or map code for a module, file, folder, function, or API.
codeaholicguy/ai-devkit 1,097
agent-orchestration Proactively orchestrate running AI agents — scan statuses, assess progress, send next instructions, and coordinate multi-agent workflows. Use when users ask to manage agents, orchestrate work across agents, or check on agent progress.
codeaholicguy/ai-devkit 1,097
verify Enforce evidence-based completion claims — require fresh command output before reporting success. Use when completing any task, fixing a bug, finishing a phase, running tests, building, deploying, or making any "it works" claim.
codeaholicguy/ai-devkit 1,097
technical-writer Review and improve documentation for novice users. Use when users ask to review docs, improve documentation, audit README files, evaluate API docs, review guides, or improve technical writing.
codeaholicguy/ai-devkit 1,097
debug Guide structured debugging before code changes by clarifying expected behavior, reproducing issues, identifying likely root causes, and agreeing on a fix plan with validation steps. Use when users ask to debug bugs, investigate regressions, triage incidents, diagnose failing behavior, handle failing tests, analyze production incidents, investigate error spikes, or run root cause analysis (RCA).
codeaholicguy/ai-devkit 1,097
tdd Test-driven development — write a failing test before writing production code. Use when implementing new functionality, adding behavior, or fixing bugs during active development.
codeaholicguy/ai-devkit 1,097
simplify-implementation Analyze and simplify existing implementations to reduce complexity, improve maintainability, and enhance scalability. Use when users ask to simplify code, reduce complexity, refactor for readability, clean up implementations, improve maintainability, reduce technical debt, or make code easier to understand.
codeaholicguy/ai-devkit 1,097
memory Use AI DevKit memory via CLI commands. Search before non-trivial work, store verified reusable knowledge, update stale entries, and avoid saving transcripts, secrets, or one-off task progress.
codeaholicguy/ai-devkit 1,097
dev-lifecycle Structured SDLC workflow with 8 phases — requirements, design review, planning, implementation, testing, and code review. Use when the user wants to build a feature end-to-end, or run any individual phase (new requirement, review requirements, review design, execute plan, update planning, check implementation, write tests, code review).
codeaholicguy/ai-devkit 1,097
ml-ralph Create ML project PRDs. Triggers: ml-ralph, create prd, ml project, kaggle.
pentoai/ml-ralph 33
ml-ralph REQUIRED first step for ANY ML task. When user describes an ML problem, goal, experiment, or model improvement — ALWAYS invoke this skill BEFORE exploring code or planning. Triggers: ml-ralph, create prd, ml project, kaggle, implement model, improve model, train model, better model, new approach, experiment.
pentoai/ml-ralph 33
ralph-log-events Event schemas for ml-ralph log.jsonl. Use when logging any event.
pentoai/ml-ralph 33
ralph-file-schemas Schema reference for ml-ralph state files (prd.json, kanban.json). Use when reading or writing these files.
pentoai/ml-ralph 33
opentui Comprehensive OpenTUI skill for building terminal user interfaces. Covers the core imperative API, React reconciler, and Solid reconciler. Use for any TUI development task including components, layout, keyboard handling, animations, and testing.
nitodeco/ralph 7
takt TAKT ワークフローエンジン。codex exec でサブエージェントを起動し、ワークフロー YAML（steps / initial_step）に従ってマルチエージェントオーケストレーションを実行する。
nrslib/takt 940