By theafh
Enforce consistent formatting and AI-friendly conventions across your project: structured git commits, day-grouped changelogs, pseudo-XML AI instructions, and linting for Markdown, Python, and Rust.
Organize AI-consumed content (prompts, rules, skills, commands, agents, system instructions) into pseudo-XML by wrapping each semantic concern in a dedicated tag for role, policy, inputs, and output contract.
Write AI-consumed content using positive, action-oriented language as the primary carrier of every instruction. Use when creating or editing any artifact an AI reads at inference time — SKILL.md files, .mdc rule files, CLAUDE.md/AGENTS.md/GEMINI.md configuration, prompt templates, system prompts, commands, agent and sub-agent definitions, instruction sets, and persona definitions.
Apply markdown linting compliance and best practices when creating or editing markdown files (.md, .mdc). Covers blank-line rules around block elements, consistent bullet style, fenced code blocks with language identifiers, table alignment syntax, header level progression, list indentation and spacing, and link/image conventions.
Apply formatting standards, code quality rules, structure conventions, linting prevention, and best practices when generating or editing Python code (.py). Covers indentation, quoting, imports, naming, line length, type hints, error handling, logging, data structures, testing, and security practices aligned with common linters (flake8, ruff, pylint).
Apply clippy-aligned Rust practices when writing or editing Rust code (.rs). Covers procedural flow, clippy-driven clarity improvements, minimal imports, Result/Option idioms, fallible builders with project error types, string prefix/suffix handling, borrowing clarity, iteration style, string building, and function signature grouping.
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A collection of professional AI skills, agents, commands, and hooks. The same source of truth ships through several equal paths. You can install it from the bundled Claude Code marketplace, symlink it into vendor config dirs globally with make deploy (VS Code Copilot, Cursor, Claude Code, OpenAI Codex, Gemini CLI, Google Antigravity), symlink it into a single repo's local config via --project-dir, or use it in-place from a checkout. The deployment script discovers artefacts by plugin layout and installs them where each tool expects them.
ai-modules/
├── .claude-plugin/
│ └── marketplace.json # registers the plugins below as a Claude marketplace
├── plugins/ # one subdirectory per plugin
│ ├── knowledge_management/
│ │ ├── .claude-plugin/plugin.json
│ │ ├── .codex-plugin/plugin.json
│ │ ├── README.md
│ │ ├── agents/ # one .md file per agent
│ │ │ └── wiki_auto_shaper.md
│ │ └── skills/ # one subdirectory per skill, each with SKILL.md
│ │ ├── wiki/
│ │ ├── wiki_wrapup/
│ │ ├── wiki_import/
│ │ ├── wiki_fix/
│ │ ├── executive_summary/
│ │ └── spr/
│ └── ai_dev/
│ ├── .claude-plugin/plugin.json
│ ├── .codex-plugin/plugin.json
│ ├── README.md
│ └── skills/
│ ├── git_commit/
│ ├── update_changelog/
│ ├── ai_instruction_writing/
│ ├── ai_instruction_formatting/
│ ├── format_markdown/
│ ├── format_python/
│ └── format_rust/
└── deployment/ # deployment script for installing artefacts globally or per-project
├── deployment.sh
├── deployment.conf
└── README.md
Each skill is a written procedure the model loads when its trigger fires. Bundling deterministic helpers (bash and python scripts, linters, schema files) alongside the prose lets the agent offload mechanical work to programs that can't hallucinate, and follow a written workflow instead of re-deriving one each session. The practical effect is fewer turns per task, smaller context per turn, and more consistent output across runs. On metered models, that translates directly into time and tokens saved.
Skills and agents for building, maintaining, and distilling a persistent, compounding knowledge base. Everything is plain markdown, readable in any editor or CLI, with no Obsidian or vendor reader required.
The wiki itself plus paired front ends that wrap two of its workflows so the model has a single named entry point per use case:
entity, concept, comparison, summary, query, procedure) is read from SCHEMA.md, so a wiki extends its taxonomy without touching the linter. Provenance is anchored by footnotes plus body-only sha256 drift detection on raw sources. Discovery, init, lint, and the sha256 helper all ship as bundled scripts, so the agent runs deterministic programs for the mechanical parts instead of inventing them inline each session.wiki_import takes one named resource (URL, file, paper, PDF, transcript, meeting note, internal note, or paste); wiki_wrapup takes the current chat session. Both capture the source, diff each candidate against the existing wiki, and emit a triage report (new pages, extensions, contradictions with both excerpts and concrete reconciliation options) before any wiki-page write lands. Approved writes route back through the wiki skill. Use them when "review what you'd change before changing it" matters — for example, after a research chat, or before importing a contested paper.wiki_fix is the one-shot skill wrapper; wiki_auto_shaper is the agent it hands off to. The agent runs a two-phase loop — assess (lint plus semantic audit), then fix, then re-lint until clean. It repairs frontmatter and schema violations, broken links, off-taxonomy tags, oversized or topic-mixing pages, procedure pages leaking instance content, content that drifts from the per-type page anatomy, and surfaces cross-page contradictions via the contested-page protocol for human review rather than auto-resolving them.Two distillation skills that operate on text outside the wiki:
npx claudepluginhub theafh/ai-modules --plugin ai_devSkills and agents for building, maintaining, and distilling a persistent, interlinked knowledge base. Ships the wiki, wiki_wrapup, wiki_import, wiki_fix, executive_summary, and spr skills, and the wiki_auto_shaper agent.
Design fluency for frontend development. 1 skill with 23 commands (/impeccable polish, /impeccable audit, /impeccable critique, etc.) and curated anti-pattern detection.
Behavioral guidelines to reduce common LLM coding mistakes, derived from Andrej Karpathy's observations on LLM coding pitfalls
Comprehensive skill pack with 66 specialized skills for full-stack developers: 12 language experts (Python, TypeScript, Go, Rust, C++, Swift, Kotlin, C#, PHP, Java, SQL, JavaScript), 10 backend frameworks, 6 frontend/mobile, plus infrastructure, DevOps, security, and testing. Features progressive disclosure architecture for 50% faster loading.
Access thousands of AI prompts and skills directly in your AI coding assistant. Search prompts, discover skills, save your own, and improve prompts with AI.
Develop, test, build, and deploy Godot 4.x games with Claude Code. Includes GdUnit4 testing, web/desktop exports, CI/CD pipelines, and deployment to Vercel/GitHub Pages/itch.io.
Complete developer toolkit for Claude Code