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More from Mamdouh Aboammar

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A curated ChatGPT and Codex distribution of MIT-licensed engineering, AI/ML, and productivity workflows, matured for automatic routing. It includes 42 focused Skills for requirements, specs, tickets, multi-agent spec implementation, TDD, debugging, review, session retrospectives, architecture, research, AI model development, data remediation, statistical ML best practices, J-space cognitive reasoning, autonomous goal contracts, skill conductor authoring, repository safety and setup, TypeScript package boundaries, workflow design, handoffs, teaching, course exercise scaffolding, and long-form writing. Host-specific duplicates and repository-development noise are excluded.

Example prompts

  • “Choose the best engineering workflow for this task and execute it”
  • “Turn this idea into a buildable engineering plan”
  • “Diagnose this bug, fix it, and verify the result”

Skills

  • ai-data-remediation

    Self-healing data pipeline layer using semantic anomaly clustering, AST-validated lambda transformations, and zero-loss mathematical reconciliation. Use when data quality checks fail, anomalous records break ETL/ELT pipelines, you need automated data cleansing with sandboxed Python transformations, or you want to group data errors into semantic clusters — even if they don't explicitly say "data remediation". Do NOT use for standard database schema migrations, routine CRUD queries, or basic pipeline scheduling.

  • ai-engineering

    Design, train, optimize, deploy, and evaluate production machine learning and LLM systems. Use when building ML models, training classifiers, fine-tuning LLMs with LoRA/PEFT, deploying low-latency inference endpoints, architecting vector RAG pipelines, or evaluating model bias and data leakage — even if they don't explicitly say "AI engineering". Do NOT use for standard backend CRUD development, basic SQL queries without ML, or simple UI styling.

  • code-review

    Review changed code against repository coding standards and original specification intent. Use when reviewing a branch, diff, PR, pull request, merge-base changes, or verifying code against documented standards — even if the user just says "review this". Do NOT use for authoring new code or diagnosing failing runtime bugs.

  • codebase-design

    Shared vocabulary and patterns for designing deep modules with narrow interfaces and clean seams. Use when designing module interfaces, finding deepening opportunities, deciding where seams go, or making code more testable and AI-navigable — even if the user says "improve this module". Do NOT use for whole-codebase architectural surveys.

  • diagnosing-bugs

    Diagnose hard bugs, intermittent flakes, and performance regressions using a tight feedback loop. Use when the user reports broken behavior, runtime exceptions, failing tests, flaky CI, or says "debug this" / "diagnose this" — even if no error message is provided. Do NOT use for routine test-driven feature development.

  • domain-modeling

    Build and sharpen a project's domain model, ubiquitous language, and architectural decision records. Use when establishing codebase terminology, challenging fuzzy concepts, writing ADRs, or updating CONTEXT.md — even if the user says "define our terms". Do NOT use for general code refactoring without domain shifts.

  • engineering-workflow-guide

    Route engineering, AI/ML, cognitive, or productivity tasks to the narrowest effective specialist skill. Use when a task spans planning, implementation, debugging, review, architecture, research, setup, AI modeling, data remediation, or autonomous goals, when the user asks what to do next, or when unsure which workflow fits — even if they don't explicitly name a skill. Do NOT use when the specific specialist skill is already obvious and unambiguous.

  • git-safety-guardrails

    Safeguard repositories against destructive, irreversible, or history-rewriting Git operations. Use when running force pushes, hard resets, branch deletions, cleans, destructive restores, or history re-writes — even if the user says "clean up git history". Do NOT use for routine safe git status, fetch, diff, or branch queries.

  • goal

    Design and synthesize high-leverage autonomous goal prompts and contracts for unattended execution. Use when crafting a /goal prompt, planning an overnight autonomous coding run, converting rambling specifications into a self-contained agentic mission, or setting up multi-agent autonomous loops — even if they don't explicitly say "goal prompt". Do NOT use when the user asks for immediate single-turn execution in the current conversation without an autonomous goal contract.

  • grill-me

    Interview the user relentlessly to sharpen an idea, requirement, or decision before execution. Use when a plan, design, requirement, or decision needs a focused interview to expose ambiguity, missing constraints, trade-offs, or weak assumptions — even if the user just says "grill me on this". Do NOT use for repo-stateful domain modeling with doc generation.

  • grill-with-docs

    Interview the user to stress-test a design while simultaneously recording domain terms and architectural decisions in project documentation. Use when planning features in a codebase and wanting domain terms captured in CONTEXT.md and ADRs — even if the user says "grill this feature". Do NOT use when no codebase context or documentation trail is needed.

  • grilling

    Relentlessly interview the user round-by-round to stress-test thinking and expose unexamined assumptions. Use when the user requests a grilling interview, wants their idea challenged, or uses trigger phrases like "grill me" — even if they just say "poke holes in my plan". Do NOT use when the user asks for immediate implementation.

Show all 42
  • handoff

    Compact current conversation context, decisions, evidence, and next actions into a portable markdown handoff for a fresh agent session. Use when ending a session, transferring work to another agent, switching workspaces, or preparing a resume point — even if the user just says "save progress". Do NOT use for committing code to git or creating branch PRs.

  • implement

    Build scoped code changes and run verification from an approved specification or plan. Use when a concrete spec, approved plan, or set of tickets is ready to implement, when the user asks to build or code a planned feature, or when executing tasks — even if they don't explicitly say "implement". Do NOT use for initial architecture exploration or vague requirements.

  • implement-spec

    Execute a full specification across task tickets using isolated subagent branches into a unified PR. Use when a specification with associated task-graph tickets is ready to implement across multiple subagents or isolated worktrees, and the goal is a complete pull request on a single branch — even if the user says "build the whole spec". Do NOT use for single isolated bug fixes or exploratory coding without tickets.

  • improve-codebase-architecture

    Survey codebases for shallow modules, weak seams, and deepening opportunities, producing a visual report. Use when conducting architectural reviews, identifying design debt, finding deepening opportunities, or preparing codebase refactors — even if the user says "analyze our architecture". Do NOT use for basic syntax linting or formatting.

  • j-space

    Operate the agent's inner cognitive workspace for multi-step reasoning, long-horizon planning, deep debugging, and calibrated self-monitoring. Use when tackling complex multi-step problems, architecture design, long mechanical tasks requiring goal preservation, suspicious input audits, or when asked to think deeper or harder — even if they don't explicitly say "J-space". Do NOT use for single-step trivial lookups, basic formatting, or conversational small talk.

  • migrate-to-shoehorn

    Migrate unsafe TypeScript test assertions to @total-typescript/shoehorn with explicit fixture intent. Use when test files contain unsafe `as` or `as unknown as` typecasts, when mock fixtures break under typechecking, or when modernizing test typing — even if the user says "fix test type assertions". Do NOT use for production code types.

  • ml-best-practices

    Statistical machine learning best practices, exploratory data analysis, feature engineering, and rigorous model evaluation. Use when analyzing tabular datasets, engineering features, training classifiers or regressors, forecasting time series, or computing 95% bootstrap confidence intervals — even if they don't explicitly say "ml best practices". Do NOT use for standard database queries without ML, basic spreadsheet formatting, or general application backend logic.

  • prototype

    Build a throwaway prototype to answer a specific design, state model, or UI exploration question. Use when evaluating whether an interface feels right, exploring UI concepts, or testing logic before committing to a full spec — even if the user says "mock this up". Do NOT use for production implementation.

  • research

    Investigate a technical question against high-trust primary sources and capture findings as a cited Markdown note. Use when gathering API facts, reading documentation, verifying library capabilities, or delegating reading legwork — even if the user says "look into this library". Do NOT use for writing production feature code.

  • resolving-merge-conflicts

    Resolve in-progress git merge or rebase conflicts by intent traced to primary sources. Use when hit with merge conflicts, CONFLICT markers in files, rebase pauses, or when git prompts to resolve conflicted hunks — even if the user just says "fix this merge". Do NOT use for creating new branches or routine rebasing without conflicts.

  • retro

    Conduct a retrospective on a coding session to systematically improve agent environment, navigation pointers, automated checks, coding standards, tool economy, or AGENTS.md instructions. Use when reflecting on completed work, auditing agent mistakes, or optimizing repository rules — even if the user says "run a retro". Do NOT use during active mid-task implementation.

  • scaffold-exercises

    Scaffold course, workshop, or tutorial exercises following repository conventions. Use when creating exercise folders, problem/solution/explainer variants, numbered lesson files, or workshop boilerplate — even if the user says "add a new exercise". Do NOT use for routine application feature scaffolding.

  • setup-engineering-workflows

    Configure repository issue-tracking, triage-label, domain-doc, and agent-instruction conventions. Use when initializing engineering workflows in a repo, configuring GitHub/GitLab issue labels, or establishing CONTEXT.md conventions — even if the user says "setup our workflows". Do NOT use for general project package installs.

  • setup-pre-commit

    Configure pre-commit quality checks with Husky and lint-staged while preserving package manager conventions. Use when setting up git pre-commit hooks, automated formatters, staged linters, or typecheck gates — even if the user says "add pre-commit hooks". Do NOT use for continuous integration (CI) pipeline setup.

  • setup-ts-deep-modules

    Enforce TypeScript package boundaries, entry points, and cyclic dependency rules with dependency-cruiser. Use when structuring monorepo packages, establishing public API entry points, or preventing circular imports — even if the user says "fix TS package boundaries". Do NOT use for non-TypeScript repositories.

  • skill-conductor

    Author, refine, evaluate, and package agent skills across their full lifecycle. Use when building a new skill from scratch, improving an existing skill, fixing a skill that triggers unreliably, running skill evals, benchmarking skill performance, or packaging skills for distribution — even if they don't explicitly say "skill conductor". Do NOT use for general software coding tasks or using pre-existing skills.

  • tdd

    Implement features and bug fixes test-first using red-green-refactor cycles. Use when writing new functionality, adding regression tests, fixing bugs with test coverage, or designing behavior through test assertions — even if the user says "write tests for this". Do NOT use for throwaway exploratory prototypes.

  • teach

    Teach a technical concept interactively using the workspace as an active learning lab with durable learning records. Use when the user asks to understand a codebase concept, learn a language feature, explore design patterns, or practice coding skills — even if they say "explain how this works to me". Do NOT use for silent autonomous code generation.

  • to-questionnaire

    Transform unknown requirements and stakeholder dependencies into a structured questionnaire. Use when a plan or spec is blocked by external stakeholder decisions, business rules, or missing domain facts — even if the user says "what should I ask them?". Do NOT use when the user can answer the questions themselves.

  • to-spec

    Synthesize conversation and codebase context into an unambiguous, buildable technical specification. Use when requirements and design decisions are settled and need to be formalized into a technical spec — even if the user says "write a spec for this". Do NOT use when the core idea is still fuzzy and ungrilled.

  • to-tickets

    Decompose an approved specification or plan into ordered, dependency-linked tracer-bullet implementation tickets. Use when turning a spec or architectural plan into actionable tracker issues with clear acceptance criteria — even if the user says "break this into tickets". Do NOT use for initial requirements gathering.

  • triage

    Classify, verify, and prepare external bug reports and feature requests into agent-ready briefs. Use when processing incoming issues, validating bug reproductions, rejecting out-of-scope requests, or structuring contributor tickets — even if the user says "triage these issues". Do NOT use for internal tickets already produced by to-tickets.

  • wait-what

    Re-pitch an explanation or proposal from a simpler angle with reset assumptions and plain English. Use when the previous explanation was confusing, jargon-heavy, or did not land — even if the user just says "what?" or "I don't get it". Do NOT use for general brainstorming or initial planning.

  • wayfinder

    Map and navigate large, uncertain multi-session efforts through a shared graph of decision tickets. Use when facing complex greenfield projects or massive architectural migrations that exceed a single session — even if the user says "map out this massive project". Do NOT use for small, well-scoped features.

  • wizard

    Generate an interactive bash wizard to guide humans through manual setup, dashboard, or credential steps. Use when setting up API keys, third-party dashboards, CI secrets, or infrastructure where human authentication is required — even if the user says "create a setup wizard". Do NOT use for steps the AI agent can execute autonomously.

  • workflow-designer

    Specify recurring operational, review, or content workflows with explicit triggers, inputs, actions, and ownership. Use when designing repeatable team routines, approval loops, release checklists, or automated processes — even if the user says "design a workflow for this". Do NOT use for one-off coding tasks.

  • writing-beats

    Develop long-form writing through an interactive progression of narrative and argumentative beats. Use when drafting essays, articles, documentation, or blog posts where structure evolves beat by beat — even if the user says "write this post with me". Do NOT use for quick single-sentence copy edits.

  • writing-for-agents

    Author and refine agent-facing instructions, skills, AGENTS.md files, and context pointers. Use when creating new agent skills, optimizing prompt guidelines, writing steerable docs, or organizing progressive disclosure — even if the user says "write a skill for this". Do NOT use for human-facing marketing copy.

  • writing-fragments

    Capture and refine raw ideas, quotes, and observations before committing to an article outline. Use when collecting source material, brainstorming essay fragments, or exploring angles without structure pressure — even if the user says "help me brainstorm notes". Do NOT use for final draft formatting.

  • writing-shape

    Shape raw notes, transcript fragments, and research into a structured, coherent article draft. Use when assembling collected fragments into narrative sections, establishing flow, and building a draft block by block — even if the user says "turn these notes into a draft". Do NOT use for initial fragment generation.

Categories
Made by
Mamdouh Aboammar · Website
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On Install
Capabilities
Engineering task routing, Requirements discovery & specs, Issue decomposition & tickets, Multi-agent spec implementation, Test-driven development (TDD), Root-cause bug diagnosis, Pre-commit code review, Software architecture & deep modules, Domain-driven modeling & ADRs, Primary-source technical research, AI & ML model engineering, Self-healing data remediation, ML statistical evaluation & metrics, Cognitive workspace reasoning (J-space), Autonomous goal contracts, Skill conductor authoring & evals, Git safety guardrails & pre-commit, Workflow design & session handoffs, Course scaffolding & pedagogy, Long-form writing & beats structure
Availability
Available
Version
1.1.0
Created
August 9, 2026
More info