ng-agentic-skills
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- License — License: MIT
- Description — Repository has a description
- Active repo — Last push 0 days ago
- Community trust — 13 GitHub stars
Code Fail
- child_process — Shell command execution capability in .agents/skills/brainstorming/scripts/server.cjs
- exec() — Shell command execution in .agents/skills/brainstorming/scripts/server.cjs
- fs.rmSync — Destructive file system operation in .agents/skills/brainstorming/scripts/server.cjs
- process.env — Environment variable access in .agents/skills/brainstorming/scripts/server.cjs
- fs module — File system access in .agents/skills/brainstorming/scripts/server.cjs
- rm -rf — Recursive force deletion command in .agents/skills/brainstorming/scripts/stop-server.sh
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Agent skills for Angular development – the companion repo for the Agentic Engineering blog post series
NG Agentic Skills
A library of agent skills for Angular work – focused, reusable capabilities an AI agent can invoke
for signal forms, data access, SignalStore state, migrations, refactoring, accessibility, performance
and security reviews, prototyping, unit and e2e tests, and verifying a feature in the browser – plus
skills for the agentic process itself: skill authoring, brainstorming and grilling, plan execution,
test-driven development, bug diagnosis, code review, prose de-slopping, handover and git stack rewrites.
Companion repo for the talk AGENTS.md Is Not Enough: Build Skills, Don't Download Them and the
Agentic Engineering blog post series.
Slides ·
Blog post ·
Full skill directory ·
Skill maintenance
The skills
Skills live under .agents/skills/. Each skill is a folder with a SKILL.md plus optionalreferences/, scripts/, or assets/ support files loaded just in time. They are split into
custom skills authored here (MIT-licensed) and third-party skills adapted from public sources
with their origins and local adaptations recorded in skill-sources.json.
See 02-SKILLS.md for the full directory – every skill with a one-line description,
grouped into custom and third-party – and SKILL-MAINTENANCE.md for how
skills are added, updated and retired.

This sketch outlines the Agentic Engineering workshop arc: choosing AI models and harnesses, setting up an Angular AI workspace with best practices and style guides, building reusable AI skills, applying AI supported workflows, and using those foundations for targeted Angular refactoring. The skills in this repo are the "building reusable AI skills" step.
Workspace setup
This repository is the ng-agentic workspace with the skills
library added. The step-by-step setup – Angular CLI, Husky, ESLint and lint-staged, the opinionatedAGENTS.md, MCP servers and per-agent files, feedback loops and recommended companion apps – is
documented in the ng-agentic README and not repeated here.
Two parts are worth a note because the files live in this repo too:
Angular Coding Style Guide
Find our Angular Coding Style Guide in the style-guide folder.
It contains general guidelines for writing clean and maintainable code in Angular projects, as well as specific style guides for different file types such as Git commits, HTML templates, NPM packages, SCSS styling files, and TypeScript files.
Anyone who copies and pastes this style guide should replace the lxt- class-name prefix with a prefix that is meaningful for her or his own app.
Opinionated Agent Instructions
This commit turns AGENTS.md from a generic Angular guidance file into a repo specific operating contract for AI agents, adding workflow rules for preserving user edits, loading only the narrowest relevant style guides, applying modern Angular v22+ defaults, keeping TypeScript strict, and respecting template, accessibility, service, and testing boundaries.
Two things to keep in mind when adopting these instructions:
- They are tuned for a specific model generation. This
AGENTS.mdwas developed and tested against Opus 4.5 to Opus 4.8 and GPT-5.2 to GPT-5.5. With newer, more intelligent models arriving – Fable 5, GPT-5.6 – we will have to reevaluate whether the detailed instructions on Angular and TypeScript best practices are still necessary to get high-quality code output, or whether the models already internalize them. Treat this as a constantly evolving instruction set, not a finished artifact. - Less can be more. Parts of the community argue that shorter instruction files outperform long ones, because every rule competes for the model's attention. So don't copy this file verbatim – play around with the instructions, measure what actually changes your output quality, and fine-tune them for your own projects and workflows.
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