Marcos Paulo ba56f7a0c9 fix(blocks): restore legacy values with token-gap markers
Tasks 10 and 11 documented that the first attempt pointed raw values at
the nearest --step-* / palette token. That is a silent redesign: --step-1
is 15px where source uses 14px, var(--muted) is #697b89 where source uses
#9eabb4, and so on. The brief says the site must look exactly as it did.

check-tokens.mjs (synced from main) now waives findings whose own line or
the line above carries 'token-gap: <reason>; owner design-system-keeper'.
Marked values are printed every run as a visible debt queue for
design-system-keeper; the marker needs a real reason or it does not count.

Restored to the exact legacy values:

  FleetDiagram   captain-eyebrow 10px, captain h2 clamp(24,3vw,38),
                 arrow 30px, workers parent #41596b seam,
                 worker-card span color #9eabb4 + font 10px,
                 worker-card strong 16px
  HandoffTable   thead 10px, tbody th 14px, td 13px
  PhasePanel     phase-tab 10px, phase-meta 10px, panel h3 clamp(24,3vw,38)
  RouteTable     head 10px, strong 14px, small 12px
  SkillPackage   label 10px + #ffffff40 borders, row 12px code (no weight)
  WorktreeMap    border 1px solid #41596b, span 9px, strong 14px,
                 small color #9eabb4 (root and branch)

Values that already matched a token (captain/worker code at --step-0=11px,
the panel code, all the layout/spacing values, colours that did match)
are untouched.

gate passes; 19 marked token-gaps await design-system-keeper; no raw
hex or px font-size is unmarked.

Co-Authored-By: Claude Code <noreply@anthropic.com>
2026-09-05 07:25:38 +00:00
2026-09-05 04:29:42 +00:00
2026-09-05 04:29:42 +00:00
2026-09-05 04:29:42 +00:00
2026-09-05 04:29:42 +00:00
2026-09-05 04:29:42 +00:00

AI For Dummies

A lightweight, presentation-style field guide to AI-assisted engineering.

It explains how to combine a strong planning/review model with faster workers, reusable skills, subagent handoffs, Git worktrees, and explicit verification. An interactive field kit compares common behavior skills such as ponytail-lite, caveman, unlazy, research, debugging, and review. The skill-forge workflow covers discovery, triggers, package anatomy, progressive instructions, structural validation, and behavioral iteration. The hands-on lab provides a tiny starter project and copy-ready baseline and skill-enabled prompts for a short side-by-side exercise. An interactive model gearbox separates capability tier from reasoning effort across OpenAI, Claude, and Gemini, and every featured skill links to a pinned source with an approval-first installation prompt.

Run locally

This is a dependency-free static site:

python3 -m http.server 4173

Then open http://localhost:4173.

Verify the content and interaction contracts with:

pnpm run verify

Project structure

  • index.html — default route map and focused chapter navigation
  • full-guide/ — the complete bilingual presentation, with responsive audit overrides
  • styles.css / app.js — editorial visual system and bilingual field-guide interactions
  • responsive.css — interactive diagrams and Full HD-to-4K adaptations
  • docs/references/ — bundled research sources and notes
  • docs/operations-guide.md — canonical SilverBullet operations and skills guide
  • hands-on/starter/ — dependency-free Tiny Tasks exercise
  • hands-on/rules/ — dependency-free Guardrails lab; toggles rule sources into the prompt
  • rules/ — bilingual case study of skills, CLI ratchets, Husky, and PR review
  • skills/ — reusable design and rules-case-study skills, plus an interactive package anatomy explorer
  • skills-review/ — static review desk for submitted skills; its reader vote widget calls the separate vote-service
  • vote-service/ — small Go API + Kubernetes manifests backing the skills-review vote widget (see vote-service/README.md)
  • GATES.md — acceptance ledger for the project

Publishing

The Gitea instance has a Pages Server configured to publish a repositorys pages branch under pages.marcospaulo.dev.br. The intended site address is:

https://netcracker.pages.marcospaulo.dev.br/ai-for-dummies/

If the URL is not available yet, verify that the pages branch exists and that the repositorys pages branch exists. Gitea itself does not provide a built-in Pages server; this setup uses the instances separate Pages Server and Actions deployment path.

For the complete authoring, verification, publication, rollback, worktree, and skill workflow, see docs/operations-guide.md.

Reader voting on the skills-review desk

skills-review/ is static, so its "which draft would you ship?" vote widget calls a separate stateful service — vote-service/, a small Go API on its own pod, one vote per visitor enforced server-side by IP (a MAC address is never visible to a server across the internet, so it cannot be used). See vote-service/README.md for the API, the anti-abuse design, and the build/push/deploy steps; skills-review/index.html sets window.SKILLS_REVIEW_VOTE_API to point at it once deployed.

Research

See docs/references/README.md for official Claude, Codex, and Git documentation. The additional reading path bundles 12 verified articles and guides, including Medium and practitioner sources. See model routing for current provider controls and verified skill sources for commit-pinned provenance.

Rules and enforcement case study

Open /rules/ for a concise walkthrough grounded in the netcracker/interview repository. It shows how AGENTS.md, project-local skills, machine-readable repo ledgers, a UI contract ratchet, lint-staged, Husky, commitlint, specialist verifier agents, and PR review reinforce one another. Every example links to its source file in Gitea, and the page includes a copy-ready prompt for mapping the same layers in another repository.

The implementation patterns are also packaged as project-local skills in skills/. Use editorial-playbook when adding chapters or sections, and rules-case-study when turning repository controls into a source-linked teaching page.

S
Description
A lightweight field guide to AI-assisted engineering workflows
Readme 13 MiB
Languages
Astro 49.5%
Python 17.5%
JavaScript 14.7%
Shell 13%
TypeScript 1.7%
Other 3.5%