Fifteen skills that give a coding agent a full development process
Editor's takeIts premise is that models lack process discipline, not coding ability. The single best idea to steal is 'Rulings, not stalls': decide by default, and stop for a human only when an action is irreversible, touches a security-sensitive surface, leaks outside the worktree, or the plan is already too broken to continue.
v6.4.1 (2026-09-18), MIT, 289k stars — the largest single skill repository of its kind. It chains brainstorming, spec writing, git-worktree isolation, planning, subagent execution, red-green TDD, two-way code review and verification-before-completion into one trunk workflow, and the README lists 16+ harnesses: Claude Code, Codex, Cursor, Gemini CLI, OpenCode, Kimi Code, Devin CLI and more.
The 38 skills Matt Pocock uses every day for real engineering
Editor's takeIt sells parts rather than a whole process: code-review, diagnosing-bugs and resolving-merge-conflicts each stand alone. grill-me, grilling and wait-what turn the agent around to interrogate your requirements, and nothing else in this batch does that. CONTEXT.md at the repo root decouples project vocabulary from generic skills — the cleanest version of that idea in this batch.
The personal working set of the author of Total TypeScript; the repo describes itself as real engineering, not vibe coding. 38 SKILL.md files, primary language Shell, grouped as engineering (18), productivity (7), misc (4) and in-progress (9). It explicitly rejects takeover-style frameworks such as GSD, BMAD and Spec-Kit in favour of small, easy to adapt, composable.
The official repo from the format's author: 19 skills plus spec and template
Editor's takeIf you want to know how a SKILL.md is actually supposed to be written, read this instead of any second-hand tutorial. Two things to know first: docx, pdf, pptx and xlsx are source-available rather than open source (the other 15 are Apache-2.0), and the repo carries its own disclaimer — these skills are demos, so test them in your environment before trusting them with anything critical.
Anthropic's own implementation, 177k stars: 19 skills under ./skills plus ./spec (the Agent Skills specification, now at agentskills.io) and ./template as a scaffold. Its definition of a skill is folders of instructions, scripts and resources loaded dynamically, and progressive disclosure comes straight from here.
Design knowledge as searchable local data: 79 styles, 192 palettes, 119 UX rules
Editor's takeIts sharpest self-description is that models can write CSS but lack a taste database. Ten rule categories are ordered by severity, with Accessibility and Touch & Interaction first as CRITICAL and Charts last — that ordering is the stance. The Design System Generator runs five parallel searches, and the repo states plainly: do not persist unverified output, so a generated design system has to be checked before it lands in your repo.
v2.13.0, MIT, 129k stars, site uupm.cc. Instead of lecturing the model it ships structured catalogues the agent queries before writing code: 79 UI styles, 192 palettes with reasoning, 74 type pairings, 119 UX rules, 105 icon suggestions, 17 GSAP presets, 25 chart types, 22 stacks and 34 landing-page patterns.
Map the whole project into a queryable knowledge graph instead of grepping
Editor's takeThe design that matters most: every edge is labelled EXTRACTED or INFERRED, so you can tell what the source explicitly contains from what the tool inferred — which makes an agent's conclusions auditable, a hard requirement in production. It is not a vector index: no embeddings, no vector store, a real graph you can explain, path and query. In its own benchmark, building the graph costs zero LLM spend and ingest is an order of magnitude cheaper, a structural win from local parsing.
YC S26, Apache-2.0, 120k stars, Python 3.10+. /graphify . maps code, docs, PDFs, images and videos into a graph and emits a clickable graph.html, a human-readable GRAPH_REPORT.md and a machine-readable graph.json. Code goes through tree-sitter AST parsing (about 40 languages): deterministic, no LLM calls, nothing leaves the machine.
Taste Skill: 13 anti-slop frontend skills that turn AI tells into countable rules
Editor's takeThe lesson worth stealing is rules expressed as countable mechanical tests: eyebrow count ≤ ceil(sections/3), bento cells equal to content items, zero em-dashes on the page, palette families rotated across projects. Each one aims straight at the model repeating itself instead of stopping at an adjective. The repo is equally honest about its edges: v2 self-declares experimental and is still moving, §13 explicitly excludes dashboards, data tables, multi-step wizards, code editors and native mobile, and the only test in the repo guards the source-invocation semantics of the skill.sh shell helper rather than the design rules.
One MIT repo holding 13 skills: ten that output code and three that output reference images only, all installed into Claude Code / Codex / Cursor with a single npx skills add. The default design-taste-frontend v2 (experimental) is a 1,228-line SKILL.md: first the model must emit a one-line "design read", then it sets three dials (DESIGN_VARIANCE / MOTION_INTENSITY / VISUAL_DENSITY), then it implements against 14 chapters of hard rules - install the official package for corporate briefs, label aesthetic borrowings in comments, zero em-dashes anywhere on the page, eyebrow quota computed from section count, bento cells equal to content items, and finally a 50-plus box pre-flight check. The repo also ships research/laziness, a literature directory using controlled studies to explain why models cut output short.
An AGI progress digest: paper deep reads, open-source projects, demo videos and leaderboards across models, agents and runtimes, skills, multimodal and embodied AI.
Where does the content come from?
Public sources: arXiv papers, official releases and blogs, GitHub repositories, demos on X and YouTube, and evaluators' own leaderboards. Every entry keeps its source link and author credit.
Are the paper and project write-ups summaries?
No. Papers are written up from the full text as bilingual long reads with formulas, experiment tables and method diagrams; open-source projects are read through their README and source before a bilingual write-up with a hands-on verdict.
Where do the leaderboards come from?
From each evaluator's own official board, synced on a schedule, with scrape and official-update timestamps shown.
Can I cite this site?
Yes — cite the canonical URL each page declares. Third-party papers, videos and images remain the copyright of their original authors.
