GitHub repo `techygarg/lattice` that formalizes a framework of **composable skills** for installing an *engineering discipline* into AI coding assistants (Claude Code, Cursor). Distinctive three-tier architecture: **Atoms** (single-principle guardrails: clean code, DDD, security, test quality, design-first), **Molecules** (multi-step workflows composing the atoms: design, implement, refactor, fix, review), **Refiners** (guided interviews producing project-specific standards that customize the atoms' behavior). Operational pipeline `lattice-init` → `design-blueprint` → `code-forge` → `review`, with `refactor-safely` and `bug-fix` as offshoots. Three pivotal principles: *"Skills over prompts"*, *"Composability over monoliths"*, ***"Living context over static config"*** — the `.lattice/` folder grows smarter with every feature cycle. MIT, pure shell, 18 stars / 52 commits, a series of articles on martinfowler.com explaining five *collaboration patterns*. Strong convergence with Vincent *Superpowers* (2026-04-02), Habert *PROJ-AI* (2026-05-05), Wescale *Usine Logicielle Augmentée* (2026-05-03), and — the highest doctrinal convergence with no declared lineage — **Compound Engineering** by Every (Shipper/Klaassen 2025-12-11): isomorphic pipelines (lattice-init→design-blueprint→code-forge→review ↔ ce:brainstorm→ce:plan→ce:work→ce:review), living context layer (`.lattice/` ↔ `docs/plans/+solutions/+brainstorms/`), a shared design-first stance, mandatory review at the end. 2026 *coding agent harness* doctrine converges on a stable vocabulary, without direct influence.
#lattice#techygarg#composable AI skills
techygarg (auteur GitHub, identité réelle non précisée dans le README ; auteur d'une série d'articles publiée sur martinfowler.com).
Post from the **Ahrefs blog** published on **April 28, 2026** by **Ryan Law** (Director of Content Marketing, Ahrefs) describing an in-house **content engineering** system built around **Claude Code**: an editorial pipeline that produces **publish-ready drafts in 6 to 12 minutes**. **Pivot thesis**: ***« AI content is not, by default, good. This process works well because it mirrors our existing human editorial process »*** — quality doesn't come from the model but from the **faithful reproduction of a human editorial process** proven over decades. Architecture: **~23 skill files**, each corresponding to an editorial step (keyword research, topic gap analysis, structural outlining, research compilation, draft generation, formatting), **orchestrated by a master skill `blog-pipeline`** that chains them to produce a complete article. **Seven design principles**: (1) **mimic human workflows** by chaining skills adapted from existing Ahrefs editorial documentation; (2) **output each step separately** for troubleshooting (*« if you get an article at the end of a ten minute run, and it's bad, it's hard to diagnose precisely where and why the process went wrong »* → save intermediate outputs); (3) **create test cases** via Anthropic's `skill-creator` skill to evaluate and improve guidance; (4) **plug in quality data sources** — the **Ahrefs MCP** (keyword metrics, parent topic, long-tail themes, SERP overviews, competitive analysis), competitive analysis and product docs; (5) **front-load human direction** via context parameters enabling editorial guidance; (6) **build interactive previews** in HTML format for review before publication; (7) **allow customization** (each team member can fork and modify the system). **Volume**: ~**15 articles published** and ~**30 articles updated** via this workflow; development started in **February 2026** (the prior process from **August 2025** took several days and manual intervention). **Explicit caveats** (anti-oversell): *« experience matters »* — the process reflects decades of editorial expertise; topic selection focuses on **informational SEO content** the author knows well; Ahrefs **has no plan to "scale" content massively** but maintains an **evergreen library**. Philosophy: automate *« the formulaic parts of work »* to eliminate drudgery and free up time for research, thought leadership, webinars, and system optimization — **not** replace human effort. Canonical reference cited by Pasquale Pillitteri (*Opus 4.8 SEO workflow*) as field proof of the « 6-12 min/draft » gain. Direct convergence with the **skills-over-prompts** doctrine (Lattice, PROJ-AI), **systems around the model** (Dropbox/Okumura), and the use of **HTML as a review artifact** (Shihipar).
**Ryan Law** — Director of Content Marketing chez **Ahrefs**. Praticien senior du content marketing SEO ; le billet est un retour d'expérience personnel (*« How I do… »*) publié sur le **blog Ahrefs** (ahrefs.com/blog) le **28 avril 2026**.