The 38 skills Matt Pocock uses every day for real engineering
Editor's takeSells parts, not a process: 38 SKILL.md behind a strict user/model-invoked contract, a SCOPE.md admission bar and a shared GLOSSARY.md; the spec-to-tickets-to-implement-spec pipeline is the most complete agentic engineering loop in this channel.
Daily-working set of the Total TypeScript author: 38 SKILL.md files (engineering 20, productivity 7, misc 4 frozen, in-progress 7) behind a user-invoked/model-invoked contract, a SCOPE.md admission bar and a shared GLOSSARY.md; installs as a Claude Code, Codex, Copilot or Gemini plugin, or an editable skills.sh copy.
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.
Diagram Design: Editorial Diagram Taste as 45 Visual Types and 32 Machine Verifiers
Editor's takeWhat is most worth stealing is that taste is decomposed into three decidable machine contracts: color as semantic roles plus a contrast contract (verify-contrast.py computes WCAG for every role against the background it actually sits on, compositing translucency first), geometry as a 4px grid plus per-diagram budgets of at most 9 nodes, 12 arrows and 2 accent elements, and numbers as data-value / data-carry / data-status declarations in the HTML that 32 verify-* scripts recompute. Measured locally: change one delta in the shipped waterfall example from −38 to −36 and verify-waterfall.py immediately reports that bar 6 'FY26 plan' declares 236 while the walk arrives at 238; mermaid_extract.py on the 9-node fixture prints a node and edge census, hub degrees, entry and terminal nodes, collapsible groups and budget flags; the adversarial Excalidraw fixture's 'IGNORE ALL PREVIOUS INSTRUCTIONS' survives only as an escaped inert label while its two links and one embed land in the discarded list. The limits are stated just as honestly: the README concedes output quality depends on the model, the same prompt can lay out differently twice, and the result is not a diffable source format — for docs that live in Git and change every sprint, Mermaid is still the better fit; PNG export needs a one-time Playwright plus Chromium install; the 9-node budget means complex systems must be split into overview plus detail; and the 210 pre-baked examples were built under an earlier skin, with regeneration an open v5.1 task.
Cathryn Lavery's diagram-design (MIT, 54,318 stars) turns one sentence of natural language into a self-contained HTML+SVG editorial diagram: 45 visual types each with its own reference, semantic patterns chosen before layout; six mandatory connector rules, a 4px grid and a per-diagram budget of at most 9 nodes make taste decidable; .drawio / Mermaid / Excalidraw sources are extracted, never rendered, then redrawn through four dials (format, size, detail, audience) with a fidelity ledger; every diagram's HTML carries a data-* metadata contract that 32 verify-* scripts audit number by number and line by line.
anatomy: turn a technical concept into a real machine, a zero-dependency isometric figure skill with a 12-check geometry audit
Editor's takeWhat is most worth stealing is that it ships its own falsification tooling: twelve checks in kit/audit.mjs (clearances, solid clearances, terminals, loose ends, supports, folds, self-overlaps, crowding, stand-in shapes, wobble, seats, depth order) plus scripts/lines.mjs demanding 'line ends: 0', which turns 'looks fine' into countable failures. It also states outright that a stand-in shape that quiets the audit is itself a bug, and the coverage check exists to catch it. Aesthetics are numeric too: bevels 0.4-1.6, screw dots 0.4-0.55, the subject filling 55-65% of the card, a device ratio cap of 1.5, and a 3D bend bound of R ≥ 2r / sin²(e − θmax). CI rebuilds every example in both themes and requires an empty git diff, so kit compatibility is a machine guarantee. The limits are stated just as honestly: not a chart library (no axes or series), machines, instruments, tools and furniture only, 3D self-reported beta at roughly 20 fps for 380 parts, and one figure costs a long session.
A standard Agent Skill that makes Claude Code, OpenCode or Codex explain a technical idea by inventing a real machine (a pin-tumbler lock for a cache key, a gantry plotter for one shared GPU context) and drawing it as four-tone isometric SVG with a zero-dependency kit of about 16,200 lines, plus optional WebGL shaders and a beta 3D mode. Eight examples ship with their built pages committed, and CI on macOS with Node 22 rebuilds each one and requires byte-identical output.
Claude CodeOpenCodeCodex
$npx skills add wheresryan22/anatomy -g -a claude-code
Three Chinese-first research skills that run on both Claude Code and Codex
Editor's takeThe part worth copying is that academic integrity is written as instructions: no fabricated data, DOIs or journals, and every piece of content is classified as quotation, confirmed fact, reasonable inference or writing suggestion, so the author can see at a glance which sentences have a source. The real danger in academic writing is not weak prose but a model writing inference as though it were established fact, and that four-way split blocks exactly that. The three skills also compose: figures, then body text, then defence slides.
4,117 stars, MIT. research-writing handles drafting, revising and polishing paper sections plus rebuttals; office-academic generates and edits academic Word and PPT at the OOXML layer (literature reports, group meetings, proposals, defences); scientific-toolkit covers MATLAB/Python simulation, signal processing, statistics, machine learning and paper figures, with literature search across arXiv, PubMed, CrossRef, Semantic Scholar and OpenAlex.
