ehuerta6/ai-setup

記録された最新のソース活動: 2026年10月23日 21:24

SkillsMP カタログ更新: 2026年11月5日 19:57

収集済み skills: 28

GitHub スター: 1
GitHub フォーク: 1

AI アシスタントでインストール

このプロンプトをコピーして、使用中の AI アシスタントに送ってください。

Follow https://skillsmp.com/skill-install/prompt.md to install Agent Skills from https://github.com/ehuerta6/ai-setup.

リポジトリの README

README の元ファイル：README.md

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README 全文を表示

# AI Setup Reusable configuration for AI-assisted software engineering, with selected workflows for research, writing, learning, and multi-chat workspaces. Browse this README to find configuration. Use [WORKFLOWS.md](WORKFLOWS.md) to choose a process for a specific task. ## Developer Portal The read-only [Developer Portal](https://ehuerta6.github.io/ai-setup/) is the catalog and workflow guide. Initial deployment requires setting the repository's Pages source to **GitHub Actions** under **Settings → Pages → Build and deployment**. To run it locally: ```sh cd portal npm ci npm run dev ``` The portal reads canonical Markdown from this repository when it builds. Run `npm run build` from `portal/` to create the production site. For Google Gemini products and Antigravity guidance that routes back to canonical `ai-setup` skills, see [gemini/README.md](gemini/README.md). This is an integration guide, not a second configuration catalog. Projects can adopt only what they need. Keep product behavior, architecture, and other project-specific decisions in the project repository. ## Skills-first distribution `ai-setup` is the canonical vault for useful AI configuration, but **installable skills are the preferred delivery surface for repeatable behavior**. When a rule, agent practice, template, or lesson materially improves an existing skill's workflow, update that skill first. Preserve the original canonical artifact and embed only the small, relevant operational subset that lets a skill work after selective `npx skills` installation—even when the rest of this repository is unavailable. Project rules and decisions still override generic skill guidance. Do not copy entire rule files into every skill, turn distinct agents into fake skills, or add a skill merely to distribute static rules. Templates, agents, rules, and Project Instructions remain useful in their own right. Keep related instructions consistent when a canonical rule changes; do not claim that `npx skills update` synchronizes anything except installed skills. ## Quick start - **Choose a workflow:** start with the scenario router in [WORKFLOWS.md](WORKFLOWS.md), then load the relevant skills. - **Use skills in Codex:** [install selected skills globally](#codex-shared-configuration) instead of copying them into each local project. - **Adopt configuration into a project:** inspect its existing instructions, stack, and conventions; classify what to add or keep using [WORKFLOWS.md](WORKFLOWS.md). Keep product-specific behavior in that repository, and install stack-specific skills only where they apply. - **Set up a multi-chat workspace:** choose a Project Instructions preset below and tailor it with `skills/ai-config-builder/SKILL.md`. The catalog below lists each artifact's canonical path and purpose. ## Codex: shared configuration ### Skills (verified locally) The [skills CLI](https://github.com/vercel-labs/skills) discovers skills in this repository. In an earlier local Codex trial, it found 21 skills; three selected global installations were recognized by Codex. This was a point-in-time trial, not a claim about the current catalog size or the new verification skills. To reproduce that **selective** setup: ```sh npx skills add ehuerta6/ai-setup --list npx skills add ehuerta6/ai-setup -g -a codex -s github-flow -s implement-issue -s review-pr npx skills ls -g npx skills update -g ``` These commands are examples, not a default bundle. Install only useful skills, and keep stack- or project-specific configuration where it belongs. The trial installed **copies** in `~/.agents/skills/`; pushing a change to GitHub does **not** update them automatically. Run `npx skills update -g` when needed. The update command confirmed the installed copies were current, but replacing a copy after an upstream change has **not** yet been tested. The optional third-party `find-skills` skill is not required. The project-local verification skills generated by `create-verification-skill` belong in each actual product repo, not globally inside `ai-setup`. The generators themselves may be installed globally, for example: ```sh npx skills add ehuerta6/ai-setup -g -a codex -s create-verification-skill -s maintain-verification-skill ``` ### Agents (not yet tested locally) The Markdown files in [agents/](agents/) define canonical responsibilities; they are **not** directly installable as Codex custom agents using `npx skills`. Codex currently uses standalone TOML definitions under `~/.codex/agents/` (personal) or `.codex/agents/` (per project), with `name`, `description`, and `developer_instructions`. A small TOML definition can ask the subagent to load its canonical Markdown file from a local checkout, but that read and the subagent's actual behavior must be tested in the target environment. Start with `agents/pr-reviewer.md` rather than converting every agent. See the [Codex subagents guide](https://developers.openai.com/codex/subagents). ### Rules and project instructions (not yet tested locally) Codex reads global instructions from `~/.codex/AGENTS.md` and repository-specific instructions from a project's `AGENTS.md`. Keep [rules/](rules/) canonical: task-relevant essentials are embedded in installable skills, while project-wide policies can still be installed or pointed to explicitly from a local checkout. Adding such a pointer is **not** automatic loading or Git synchronization. Project decisions always outrank reusable guidance. Codex `~/.codex/rules/*.rules` govern command permissions, **not** these Markdown engineering rules. See [AGENTS.md](https://developers.openai.com/codex/guides/agents-md) and [Codex rules](https://developers.openai.com/codex/rules). Preserve existing global and project instructions when adding this guidance. Update the local `ai-setup` checkout separately with Git: `npx skills update -g` updates installed skills, **not** this checkout, TOML agent definitions, or rules. Local files and global installations may be unavailable in remote/cloud Codex environments; verify access there rather than assuming it. ### Claude Code (discovery documented; not tested locally) Claude Code supports project skills in `.claude/skills/<skill-name>/SKILL.md` and personal skills in `~/.claude/skills/`. The canonical skills in this repository can be selectively installed with the [skills CLI](https://github.com/vercel-labs/skills), for example `npx skills add ehuerta6/ai-setup -g -a claude-code -s project-knowledge`. It reads project `CLAUDE.md` instructions. Claude Code v2.1.277 and later can also read `AGENTS.md`, depending on project-instruction settings; by default it reads `AGENTS.md` only when there is no `CLAUDE.md`, `.claude/CLAUDE.md`, or `CLAUDE.local.md` in the working directory or an ancestor. When both instruction files are needed, use an `@AGENTS.md` import in the loaded `CLAUDE.md`; a prose pointer alone does not guarantee that file is read. Check the installed behavior before relying on `AGENTS.md`, and preserve existing project instructions. See [Claude Code memory and project instructions](https://code.claude.com/docs/en/memory) and [Claude Code skills](https://code.claude.com/docs/en/skills). The CLI usage is documented, not tested locally. ## Choosing configuration Use the smallest artifact that fits the job: The map below summarizes what each artifact owns and how the repository helps you find and choose it. It is a guide to the catalog, not a requirement to adopt every artifact. Project-specific decisions take precedence over reusable configuration. ```mermaid flowchart LR project["Project-specific decisions"] -->|take precedence over| reusable guides["README catalog WORKFLOWS router"] -->|help choose| reusable reusable["Reusable configuration — adopt what fits Skills · repeatable workflows Agents · focused roles Rules · stable guidance Templates · artifact starting points Project Instructions · workspace context"] ``` The repository's catalog and workflow guide describe the available choices; they do not prescribe a bundle or mandatory sequence. | Artifact | Use it for | | --- | --- | | Skill | A repeatable task workflow, such as [research-and-compare](skills/research-and-compare/SKILL.md). | | Agent | A focused role that benefits from its own instructions or review context. | | Rule | A stable constraint that should guide many kinds of work, such as [security](rules/security.md). | | Template | A starting structure for a recurring document or decision. | | Project Instruction | Shared context for a long-running, multi-chat workspace, such as [research-workspace](project-instructions/research-workspace.md). | You do not need to adopt the whole catalog. In an existing repository, inspect its instructions first, prefer already installed global skills when appropriate, and copy or install project-scoped configuration only when justified. Keep product and architecture decisions in the project. For example, a Firebase project may need the `firebase` skill and `security` rule, but unrelated projects do not. The catalog also supports nontechnical work. Use [research-and-compare](skills/research-and-compare/SKILL.md) for a focused comparison, [learning-coach](project-instructions/learning-coach.md) for a multi-chat learning workspace, or [project-base](project-instructions/project-base.md) for personal planning that needs continuity across chats. Skills guide a task; Project Instructions hold durable workspace context. The project workspace, not this reusable catalog, should contain personal details and decisions. For a focused, practice-driven learning method across programming, mathematics, theory, and other subjects, use [TRACE](project-instructions/TRACE.md). TRACE is a general learning cycle; [learning-coach](project-instructions/learning-coach.md) remains the roadmap-driven preset for technical learning, implementation, debugging, and progression. # Skills ## Configuration ### `ai-config-builder` Path: `skills/ai-config-builder/SKILL.md` Design or evolve skills, agents, rules, templates, and Project Instructions without unnecessary duplication. ### `project-knowledge` (testing) Path: `skills/project-knowledge/SKILL.md` Retrieve, capture, reconcile, and review durable project knowledge without duplicating GitHub work history or official documentation. ### `project-audit` Path: `skills/project-audit/SKILL.md` Audit existing project AI configuration for drift, duplication, stale instructions, and missing reusable configuration. ### `research-and-compare` Path: `skills/research-and-compare/SKILL.md` Research a question and compare options using current, traceable evidence and explicit tradeoffs. ## Learning and study ### `learning-notes` (testing) Path: `skills/learning-notes/SKILL.md` Create, clean, review, and connect personal learning notes in local Markdown workspaces. It handles note files; `learning-coach` remains responsible for tutoring and learning progression. ## Planning ### `project-viability` Path: `skills/project-viability/SKILL.md` Decide whether a software project idea is worth building, needs a small real-world validation, or is better solved without new software. For personal, school, club, community, open-source, and commercial problems; separate from requirements discovery and technical design. ### `adversarial-discovery` (testing) Path: `skills/adversarial-discovery/SKILL.md` Resolve complex, bounded decisions through independent analysis, selective structured debate, and evidence. This experimental workflow preserves dissent and approval boundaries; agreement is not proof. Hand-authored scenarios and limitations (not evaluations run): [adversarial-discovery scenarios](skills/adversarial-discovery/references/evaluation-scenarios.md). ### `adversarial-design` (testing) Path: `skills/adversarial-design/SKILL.md` Coordinate the complete software design phase for a product or major feature, from project grounding and decision ordering through architecture, behavior, validation, specification review, and a human approval gate. Selectively reuse `adversarial-discovery` for consequential decisions; use `product-grill` for product-only discovery or `grill-me` for combined product and technical clarification. Hand-authored scenarios and limitations (not evaluations run): [adversarial-design scenarios](skills/adversarial-design/references/evaluation-scenarios.md). ### `product-grill` (testing) Path: `skills/product-grill/SKILL.md` Rigorously clarify product requirements without asking users to choose technical internals. Produces a categorized handoff for `to-spec`; technical uncertainties remain for engineering research. ### `grill-me` Path: `skills/grill-me/SKILL.md` Use when a feature, product decision, or implementation plan still contains ambiguity. Stress-tests decisions before implementation so agents do not have to guess. Sharpens domain terminology, surfaces behavior conflicts, and identifies useful edge cases. ### `prototype` Path: `skills/prototype/SKILL.md` Optional disposable UI or logic experiments for answering concrete design questions before implementation. Use only when trying alternatives will resolve uncertainty better than discussion. ### `to-spec` Path: `skills/to-spec/SKILL.md` Turn resolved product and technical decisions into an implementation-ready specification. Does not restart discovery or invent missing decisions. ### `to-issues` Path: `skills/to-issues/SKILL.md` Break an approved spec into focused GitHub Issues. Prefers vertical slices, explicit dependencies, and issues that fit inside a fresh agent context. ## Implementation and review ### `implement-issue` Path: `skills/implement-issue/SKILL.md` Implement one approved GitHub Issue while respecting acceptance criteria and project conventions, using a direct, minimal, readable approach informed by Ponytail-style simplification. ### `verify-change` Path: `skills/verify-change/SKILL.md` Discover and run the repository's validation workflow, including real user-path evidence when applicable. Reports actual results and unverified gaps. ### `create-verification-skill` Path: `skills/create-verification-skill/SKILL.md` Generate a project-local verification skill with launch, doctor, drive, evidence, cleanup, and a feature map. Prove at least one real journey before calling the generated instructions verified. Adapted from PStack. ### `maintain-verification-skill` Path: `skills/maintain-verification-skill/SKILL.md` Check the local verification map against source and live behavior, correct verified instruction drift, and report real product regressions separately. Adapted from PStack. ### `review-pr` Path: `skills/review-pr/SKILL.md` Review completed work independently against requirements, verification evidence, engineering quality, and material structural complexity. Includes selective maintainability principles inspired by Thermonuclear. ### `debug-with-evidence` Path: `skills/debug-with-evidence/SKILL.md` Debug using reproduction, evidence, explicit hypotheses, and focused tests instead of random edits. ### `github-flow` Path: `skills/github-flow/SKILL.md` Execute the repository's Git and GitHub workflow for branches, commits, issues, PRs, and merging. Project-specific Git rules override its defaults. ### `issue-flow` Path: `skills/issue-flow/SKILL.md` Plan existing GitHub Issues into dependency-aware batches, generate a prompt for a selected batch, and update the plan as repository state changes. Unlike `to-issues`, it does not create issues. Unlike the `batch-orchestrator` agent, it prepares the plan and prompt rather than executing the batch. ## Stack-specific ### `firebase` Path: `skills/firebase/SKILL.md` Use for Firebase Auth, Firestore, Security Rules, Emulator Suite, Functions, Storage, seeds, and trusted server boundaries. Only use in projects that actually use Firebase. ## Agent workflow ### `handoff` Path: `skills/handoff/SKILL.md` Create compact context for transferring unfinished work to another agent or session. ### `retro` Path: `skills/retro/SKILL.md` Evaluate completed AI-assisted work and identify improvements to skills, rules, tooling, or workflow. ### `writing-for-agents` Path: `skills/writing-for-agents/SKILL.md` Write and maintain compact instructions intended for AI agents. Use this when creating or cleaning AGENTS files, skills, prompts, or similar agent context. ## Writing and design ### `humanizer` Path: `skills/humanizer/SKILL.md` Rewrite prose to sound natural while preserving facts, intent, and the writer's real voice. ### `unslop` Path: `skills/unslop/SKILL.md` Clean technical prose by removing vague, inflated, formulaic, or low-signal writing. ### `impeccable` Path: `skills/impeccable/SKILL.md` Design, critique, audit, polish, or improve frontend UI and UX. ## Career ### `resume-review` Path: `skills/resume-review/SKILL.md` Review, roast, rewrite, and tailor technical resumes for software engineering and related roles. Personal resume history and Resume Bank content remain outside this repository. # Agents Agents are specialized roles used when isolating responsibility or context improves the result. Straightforward work should be done directly. ## `pr-reviewer` Path: `agents/pr-reviewer.md` Independent reviewer for completed implementation work. Use before merge when a separate review perspective is useful. ## `mentor` Path: `agents/mentor.md` Explains engineering concepts and implementation decisions while helping with a task. Use only when learning or explanation is desired. ## `supabase-database-reviewer` Path: `agents/supabase-database-reviewer.md` Review Supabase and PostgreSQL schema changes, migrations, RLS, authorization, and database security. Only use for projects where Supabase or PostgreSQL database review is relevant. ## `batch-orchestrator` Path: `agents/batch-orchestrator.md` Execute an approved multi-issue execution plan. Coordinates implementation agents, independent reviewers, merge gates, and dependent waves. ## Additional agents ### `spec-reviewer` Path: `agents/spec-reviewer.md` Independently review a specification before implementation. ### `test-engineer` Path: `agents/test-engineer.md` Design high-value tests around meaningful behavior, regressions, invariants, and critical flows. # Rules Rules contain stable engineering constraints. Procedural workflows belong in skills instead. ## `context` Path: `rules/context.md` Defines source precedence, conflict handling, context loading, and how to treat missing information. ## `engineering` Path: `rules/engineering.md` General implementation principles for scope, simplicity, architecture, errors, comments, and dependencies. ## `github` Path: `rules/github.md` Stable Git and GitHub conventions. Project-specific Git rules may override these defaults. ## `writing` Path: `rules/writing.md` Technical writing rules for accuracy, clarity, signal density, terminology, and claims. ## `security` Path: `rules/security.md` General rules for secrets, trust boundaries, authorization, data, privileges, and destructive actions. ## `ui` Path: `rules/ui.md` General UI rules for hierarchy, real states, accessibility, responsive behavior, and consistency. ## `definition-of-done` Path: `rules/definition-of-done.md` Defines the minimum evidence required before considering engineering work complete. # Templates Templates provide lightweight structure for common engineering artifacts. They are starting points, not mandatory formats. ## `SPEC` Path: `templates/SPEC.md` Implementation-ready product or technical specification. ## `PLAN` Path: `templates/PLAN.md` Implementation strategy derived from an approved spec. ## `ISSUE` Path: `templates/ISSUE.md` Focused GitHub Issue structure. ## `PULL_REQUEST` Path: `templates/PULL_REQUEST.md` Concise PR summary and verification structure. ## `HANDOFF` Path: `templates/HANDOFF.md` Transfer unfinished work between agents or sessions. ## `BATCH` Path: `templates/BATCH.md` Persist an issue orchestration graph, execution waves, gates, and orchestrator prompt. ## `DECISION` Path: `templates/DECISION.md` Lightweight record for important durable decisions and their rationale. # Project instructions Reusable presets for long-running ChatGPT Projects or other AI workspaces with multiple chats. Project instructions define how an entire project behaves. Individual chats may have different purposes and should use only the relevant parts. ## `resume-reviews` Path: `project-instructions/resume-reviews.md` For technical resume review, tailoring, Resume Bank usage, recruiter signal, and truthful positioning. ## `learning-coach` Path: `project-instructions/learning-coach.md` For roadmap-driven technical learning with interactive teaching, implementation, debugging, retrieval practice, and mastery checks. ## `TRACE` (testing) Path: `project-instructions/TRACE.md` For an adaptive five-stage learning cycle with Learn, Review, and Exam modes across subjects. ## `interview-prep` Path: `project-instructions/interview-prep.md` For coding interviews, DSA, system design, behavioral preparation, mocks, and weakness tracking. ## `software-engineering-project` Path: `project-instructions/software-engineering-project.md` For multi-chat software projects covering product thinking, brainstorming, research, decisions, architecture, implementation, UI, database work, and review. ## `course-companion` Path: `project-instructions/course-companion.md` For academic courses where syllabus, slides, assignments, and professor-provided material define the primary learning context. ## `project-base` Path: `project-instructions/project-base.md` Universal starting point when no specialized Project Instruction preset fits. ## `research-workspace` Path: `project-instructions/research-workspace.md` For long-running, multi-chat research projects with shared sources, findings, decisions, and continuity notes. # Repository structure - `skills/` — reusable workflows - `agents/` — specialized roles - `rules/` — stable engineering constraints - `templates/` — reusable artifact structures - `project-instructions/` — reusable multi-chat project behavior - `AGENTS.md` — instructions for agents working inside this repository - `WORKFLOWS.md` — scenario-based guide for choosing workflows Run basic catalog consistency checks with `python3 scripts/validate_catalog.py`. # Principles - Keep global behavior separate from project-specific context. - Keep one canonical source for shared instructions. - Load only the context needed for the current task. - Prefer the smallest correct implementation. - Never invent requirements, facts, metrics, test results, or technical decisions. - Prefer project-specific instructions over reusable defaults. - Testing something does not automatically make it adopted.

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Research a question and compare options using current, traceable evidence and explicit tradeoffs.

原文の言語: 英語

2026-10-08

収集済み skill 28 件中 28 件を表示しています。