Route prompts to a locally running Ollama instance instead of cloud AI APIs. Use this for privacy-sensitive tasks, offline work, or cost reduction. Pairs with the Docker stack in docker/local-ai-stack/.
Advise on learning rate schedules for transformer/LLM training. One of the most under-documented aspects of building LLMs from scratch — the schedule matters as much as the peak LR value.
Review codebase or design for modularity problems and produce actionable improvement recommendations. Also guides design of new modular boundaries from functional requirements. Inspired by Vlad Khononov's balanced-coupling model and golden-age-of-modularity principles. NOTE: Full upstream plugin at…
Decompose a large task into N independent subtasks and dispatch them as parallel subagents, then aggregate results. Inspired by the Modal + OpenAI Agents SDK pattern of spawning multiple coding agents simultaneously — each working in its own sandbox — to discover solutions faster through parallelism.
Git worktree setup for zero dead time. Work on multiple branches simultaneously without switching branches or stashing. Ideal for running tests on main while implementing on a feature branch.
Core AI coding workflow. Orchestrates 8 patterns: Scout → Plan → Implement → Review, with smart commits, session rituals, and continuous learning capture. This is the master skill — invoke it at the start of any non-trivial task.
Maintain a structured, versioned library of the prompts and behavioral templates used by agents in this repository. Inspired by CL4R1T4S's approach of collecting and publishing AI system prompts for community benefit.
Inspired by the CL4R1T4S project (github.com/elder-plinius/CL4R1T4S), this skill audits and surfaces all implicit behavioral instructions, system prompts, and agent directives embedded in this repository. It generates a human-readable transparency report so anyone can understand exactly how the AI agents in this repo…
Upgrades existing websites and apps to premium quality. Audits current design, identifies generic AI patterns, and applies high-end design standards without breaking functionality. Works with any CSS framework or vanilla CSS.
Surface relevant past patterns before starting work. Read the learnings file and extract rules that apply to the current task. Prevents repeating past mistakes.
Keep AGENTS.md files and .Codex/state/ in sync with the current repo reality. Use after significant structural changes, after adding risky modules, or when AGENTS.md feels stale.
Implements codebase intelligence layers similar to repowise-dev/repowise. Provides dependency graph, git history, auto-generated documentation, and architectural decisions intelligence for AI agents.
Conduct structured, systematic research on any topic — synthesizing sources, extracting key findings, identifying gaps, and producing a clear research brief. Designed for both technical and non-technical research tasks.
Track, display, and summarise all resources consumed or produced during an agent session: files read/written, URLs fetched, tools called, tokens used, and external dependencies touched.
Mandatory deep review for changes to security-sensitive modules: adminauth.py, keystore.py, agent/tools.py, and any auth/session/key path. Do not skip this for any change to these files.
Run shell commands or code snippets in an isolated subprocess environment, preventing side effects from leaking into the host workspace. Mirrors the "Modal Sandbox" pattern described in the OpenAI Agents SDK blog post — give an agent a safe "home computer" to work on.
Enables the agent to analyze its own skill library, identify gaps or weaknesses, and propose or implement improvements to existing skills or new skill definitions.
Write structured resume documentation for the next session. Captures state, context, and next actions so any AI or developer can continue without archaeology.
Enables the agent to combine multiple existing skills into a coordinated workflow for complex tasks that no single skill covers. Acts as an orchestration layer over the skill library.
Quality gates + conventional commits. Run tests, lint, and typecheck before committing. Format the commit message as a conventional commit. Blocks on failure.