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---
name: Guides/AI For Dummies Presentation
tags:
- guide
- ai
- skills
- agents
- worktrees
- gitea
- pages
---
# AI For Dummies — authoring and operations guide
This guide explains how to maintain the **AI For Dummies** presentation, how
Gitea Pages is updated, and how the skills, subagents, model routing, and Git
worktree practices taught by the presentation fit together.
## Quick links
| Resource | Location |
| :--- | :--- |
| Live presentation | [https://netcracker.pages.marcospaulo.dev.br/ai-for-dummies/](https://netcracker.pages.marcospaulo.dev.br/ai-for-dummies/) |
| Gitea repository | [https://git.marcospaulo.dev.br/netcracker/ai-for-dummies](https://git.marcospaulo.dev.br/netcracker/ai-for-dummies) |
| Local checkout | `/home/marcos/Projects/ai-for-dummies` |
| Source branch | `main` |
| Published branch | `pages` |
| Local verification | `npm run verify` |
| SilverBullet page | `Guides/AI For Dummies Presentation` |
| Skills-review vote API | `vote-service/` — separate pod, see `vote-service/README.md` |
## How the site is built
The presentation is deliberately dependency-free. Gitea Pages serves the
repository files directly; there is no bundler or generated `dist/` folder.
| File | Responsibility |
| :--- | :--- |
| `index.html` | Default route map and focused chapter navigation |
| `full-guide/index.html` | Complete bilingual field guide, controls, labels, and English source copy |
| `styles.css` | Base editorial visual system |
| `responsive.css` | Interactive diagrams and Full HD, 4K, tablet, and mobile adaptations |
| `app.js` | Interactions, state, and Portuguese translations |
| `scripts/verify.mjs` | Content and interaction contract checks |
| `docs/references/` | Primary documentation and additional reading |
The English HTML is the fallback when JavaScript is unavailable. Portuguese
copy is applied by `app.js`; the language preference is stored in
`localStorage`, and the document language changes to `pt-BR`.
## Normal edit and publish workflow
```mermaid
flowchart LR
E[Edit main] --> V[npm run verify]
V --> C[Commit]
C --> M[Push main]
M --> P[Fast-forward pages]
P --> S[Gitea Pages Server]
S --> L[Live URL]
```
### 1. Start from current `main`
```bash
cd /home/marcos/Projects/ai-for-dummies
git switch main
git pull --ff-only
git status --short --branch
```
Do not overwrite unrelated local changes. The untracked
`scripts/inspect.py` and `scripts/__pycache__/` are local visual-test artifacts
and are intentionally not part of the published site.
### 2. Preview locally
```bash
python3 -m http.server 4173
```
Open [http://localhost:4173](http://localhost:4173). Check English and
Portuguese, keyboard focus, the interactive panels, and at least one desktop
and one mobile viewport.
### 3. Verify before committing
```bash
npm run verify
node --check app.js
node scripts/audit-ui.mjs
git diff --check
```
Expected project verifier output:
```text
content verification passed
interaction verification passed
standalone verification passed
```
### 4. Commit and push the source branch
```bash
git add README.md app.js index.html full-guide/ styles.css responsive.css scripts/verify.mjs scripts/audit-ui.mjs docs/
git commit -m "feat: describe the change"
git push origin main
```
Stage only files that belong to the change. Review `git status --short` before
committing.
### 5. Fast-forward the published branch
Use a temporary worktree so the current checkout stays on `main`:
```bash
git worktree add /tmp/ai-for-dummies-pages pages
git -C /tmp/ai-for-dummies-pages merge --ff-only origin/pages # local pages is often stale
git -C /tmp/ai-for-dummies-pages merge --no-edit main
git -C /tmp/ai-for-dummies-pages push origin pages
git worktree remove /tmp/ai-for-dummies-pages
```
The `pages` branch should represent the exact published source. Avoid editing
it directly and avoid force-pushing it.
The two histories have diverged — `pages` carries merge commits and
cherry-picked duplicates of `main` commits — so `merge --ff-only main` fails
with `Not possible to fast-forward`. A normal merge is correct here, and
matches the `Merge branch 'main' into pages` commits already on the branch.
The invariant worth checking is the *tree*, not the history:
```bash
git rev-parse main^{tree} pages^{tree} # must print the same hash twice
```
If the merge conflicts (duplicated commits touching the same lines will do
it), resolve by taking `main` wholesale, since `main` is the source of truth
for published content:
```bash
git -C /tmp/ai-for-dummies-pages checkout main -- .
git -C /tmp/ai-for-dummies-pages add -A
git -C /tmp/ai-for-dummies-pages diff --cached main --stat # must be empty
git -C /tmp/ai-for-dummies-pages commit --no-edit
```
### 6. Verify the deployment
```bash
curl -I https://netcracker.pages.marcospaulo.dev.br/ai-for-dummies/
```
If the edge still shows an older page, retry with the current commit as a
cache-busting query:
```bash
git rev-parse --short HEAD
curl -I "https://netcracker.pages.marcospaulo.dev.br/ai-for-dummies/?v=COMMIT"
```
The correct URL pattern is **owner subdomain + repository path**:
```text
https://netcracker.pages.marcospaulo.dev.br/ai-for-dummies/
```
`https://ai-for-dummies.netcracker.pages...` is the wrong hostname and can
produce `ERR_SSL_PROTOCOL_ERROR` because it does not match the wildcard TLS
certificate.
## Skills-review vote service
`skills-review/` is served by the same static Pages Server as the rest of
this site, so it cannot itself remember votes. `vote-service/` is a separate
Go API on its own pod for that: one JSON file as the store, one vote per
visitor enforced by IP (a MAC address never reaches a server across the
internet). It is deployed independently of `main`/`pages` — the site can be
republished without touching it, and vice versa.
```bash
cd vote-service
docker build -t localhost:30892/ai-for-dummies-vote-service:latest .
docker push localhost:30892/ai-for-dummies-vote-service:latest
# kubelet cannot pull that ref (no certs.d/hosts.toml for localhost:30892 →
# `no basic auth credentials`), so side-load into containerd instead and let
# `imagePullPolicy: Never` skip the network pull. Use microk8s's bundled ctr.
docker save localhost:30892/ai-for-dummies-vote-service:latest -o /tmp/vote-service.tar
/snap/microk8s/current/bin/ctr --address /var/snap/microk8s/common/run/containerd.sock \
--namespace k8s.io image import /tmp/vote-service.tar
microk8s kubectl apply -f deploy/deployment.yaml # namespace + Deployment + PVC + Service
microk8s kubectl apply -f deploy/ingress.yaml
microk8s kubectl -n ai-for-dummies rollout restart deploy ai-for-dummies-vote
```
Namespace `ai-for-dummies`, `ingressClassName: public`, no per-ingress TLS.
The Deployment is pinned to node `kubernets` with a `nodeSelector`: the
`microk8s-hostpath` PV carries a `nodeAffinity` for whichever node first binds
it, so scheduling and storage have to agree on one node.
The vote widget's browser-side `fetch` calls must reach the API over the public
internet — a cluster-internal-only Service would be unreachable from a
visitor's browser even if the Pages Server happens to run on the same
network. Exposure is therefore public, terminated by **Caddy on the Oracle VPS
over Tailscale** (the same path as every other public host here, not the
cloudflared tunnel), with `ALLOWED_ORIGIN`/CORS as the boundary that restricts
which site's script may call it. After deploying, keep
`window.SKILLS_REVIEW_VOTE_API` in `skills-review/index.html` in sync with
`ALLOWED_ORIGIN` on the service.
One cluster-wide gotcha worth knowing before reading the vote code: the ingress
controller runs with `use-forwarded-headers` off, so nginx *overwrites*
`X-Forwarded-For`/`X-Real-IP` with the VPS's tailnet address. Caddy stamps the
true client address into `X-Client-IP` instead. Full rationale, the Caddy block,
and the anti-abuse design are in
[vote-service/README.md](../vote-service/README.md).
## Adding or changing a presentation section
1. Add semantic HTML and stable `data-*` hooks in the focused chapter or `full-guide/index.html`; keep `index.html` as the short route map.
2. Put interactive content in a data object inside `app.js`.
3. Add one focused render function and bind its controls once.
4. Add Portuguese static copy to `translations.pt` and dynamic copy to the
relevant interaction data.
5. Add responsive CSS, visible keyboard focus, and reduced-motion behavior.
6. Extend `scripts/verify.mjs` with structural tokens that would disappear if
the feature were accidentally removed.
7. Test English, Portuguese, Full HD, 4K, and mobile layouts.
Keep English as the source HTML. Do not duplicate the whole site into separate
language endpoints unless the architecture changes to server-side routing.
## The agent workflow taught by the presentation
```mermaid
flowchart TD
H[Human intent and boundaries] --> O[Strong orchestrator]
O --> B1[Bounded UI worker]
O --> B2[Bounded test worker]
O --> B3[Bounded docs worker]
B1 --> R[Independent review]
B2 --> R
B3 --> R
R --> E[Evidence and integration]
E --> H
```
Use a strong model where ambiguity dominates: repository inspection,
architecture, decomposition, risk analysis, and review. Use faster or cheaper
models for bounded implementation only after the brief defines the goal,
files, constraints, and checks.
Every worker should return:
- changed files and a concise diff summary;
- checks executed and their results;
- remaining risk, uncertainty, or blocked work;
- no unrelated edits.
Parallelism helps only when tasks are genuinely independent. More agents add
coordination cost, context cost, and integration risk.
## Worktree-per-worker model
A Git branch isolates history; a Git worktree also isolates the active files
and index. Give each editing agent one task, one branch, and one worktree.
```bash
git worktree add ../task-ui -b agent/ui
git worktree add ../task-tests -b agent/tests
git worktree add ../task-docs -b agent/docs
git worktree list
```
Recommended lifecycle:
1. Create the task branch and worktree.
2. Give the worker a bounded brief and acceptance checks.
3. Let the worker edit and verify only inside its worktree.
4. Review `git diff main...agent/name` from fresh context.
5. Merge, request changes, or discard.
6. Remove the finished worktree with `git worktree remove PATH`.
Worktrees prevent agents from changing the same checkout underneath each
other. They do not eliminate semantic merge conflicts; task ownership and
review still matter.
## What a skill is
A skill is a reusable procedure that changes how an agent makes decisions. It
is not magical memory and does not replace a task brief or acceptance criteria.
```text
skill-name/
├── SKILL.md required: name, description, workflow, constraints
├── agents/openai.yaml optional: UI metadata and invocation policy
├── scripts/ optional: deterministic repeated operations
├── references/ optional: conditional facts and detailed guidance
└── assets/ optional: templates or files copied into output
```
Progressive disclosure keeps context light:
1. **Name and description** are visible during selection.
2. **SKILL.md** loads when the skill applies.
3. **References, scripts, and assets** load only when the workflow needs them.
Do not create empty resource directories. Every file should have a real
consumer and should improve a decision or repeatable operation.
## Skill-creation workflow
### 1. Observe repeated friction
Collect realistic requests. Identify a non-obvious decision that agents keep
rediscovering or getting wrong. A one-off project fact usually belongs in
project documentation, not a global skill.
### 2. Define discovery
Choose a lowercase, action-oriented name. Write a concise description stating
what the skill does and when it applies. Include a boundary only when it stops
likely false activation.
```yaml
---
name: review-ui
description: Review frontend changes for focus, responsive layout, and reduced-motion behavior.
---
```
### 3. Choose the smallest anatomy
- Put shared workflow and constraints in `SKILL.md`.
- Add `scripts/` when deterministic execution prevents repeated reimplementation.
- Add `references/` for details needed only in certain modes.
- Add `assets/` for templates or generated-output inputs.
- Add `agents/openai.yaml` only when UI metadata or invocation policy is useful.
### 4. Write decision-changing guidance
Assume the agent is already capable. Include desired outcome, non-obvious
constraints, routing decisions, stopping conditions, and evidence expectations.
Remove generic advice, duplicated manuals, and speculative rules.
### 5. Validate and iterate
```bash
python3 /home/marcos/.codex/skills/.system/skill-creator/scripts/quick_validate.py /path/to/skill
```
Structural validation checks package shape and frontmatter. It does not prove
the skill makes good decisions. Also run every new script and test realistic
prompts. After real failures, sharpen the narrowest relevant rule instead of
accumulating universal instructions.
## Common skills and when to use them
| Skill | Use it for | Core rule | Avoid when |
| :--- | :--- | :--- | :--- |
| `ponytail-lite` | Requests inviting unnecessary frameworks or abstractions | Stop at the first sufficient solution: reuse, standard library, native platform, existing dependency, then minimum new code | Simplification would remove validation, security, accessibility, or real edge cases |
| `caveman` | Routine status, handoffs, and technical summaries | Put signal first and remove filler | Security warnings, irreversible actions, or sequences where terse wording can be misread |
| `unlazy` | Substantial builds, audits, and parallel work | Define observable gates and finish against evidence | Trivial edits or factual answers |
| `research` | APIs, standards, current behavior, and architecture facts | Trace claims to primary sources and save cited findings | The answer is already stable and locally proven |
| `diagnosing-bugs` | Hard bugs, flakes, and regressions | Build a fast red-capable feedback loop before theorizing | Simple known fixes with an existing regression test |
| `code-review` | Branch or PR review | Check repository standards and original specification as separate axes | No comparison point or review request exists |
| `token-saver` | Verbose tests, builds, logs, and Git output | Preserve signal and retain full failure output for recovery | Exact raw wording or full diff context is required |
| `webapp-testing` | Frontend interaction and responsive verification | Drive the real UI and assert on DOM, console, and screenshots | Static structure checks are already decisive |
Useful compositions:
- **Large feature:** `unlazy` → `ponytail-lite` → implementation → `code-review`.
- **Hard regression:** `diagnosing-bugs` → fix → `code-review` → `caveman` handoff.
- **Documentation with unstable facts:** `research` → writing → cited verification.
- **Interactive presentation:** `frontend-design` → `webapp-testing` → responsive evidence.
## Model and effort routing
Treat model tier and reasoning effort as separate controls:
| Work shape | Capability tier | Effort baseline |
| :--- | :--- | :--- |
| Formatting, lookup, narrow edit | Luna / Haiku / Flash-Lite | Low or minimal where supported |
| Normal implementation and tests | Terra / Sonnet / Flash | Medium |
| Architecture, orchestration, hard debugging | Sol / Opus / Pro | High |
For Claude Code, `/model opus`, `/model sonnet`, and `/model haiku` switch the
model alias; `opusplan` can use Opus while planning and Sonnet while executing.
Claude effort support depends on the active model. For OpenAI GPT-5.6,
`reasoning.effort` supports `none`, `low`, `medium`, `high`, `xhigh`, and `max`.
Gemini 3 uses model-specific `thinkingLevel` values, while Gemini 2.5 uses
`thinkingBudget`. Never assume one provider's control maps exactly to another.
Start with the lightest configuration that passes representative checks. Move
one knob at a time and compare quality, latency, and cost. See
[model-routing.md](references/model-routing.md) for official source links and
copy-ready provider examples.
## Installing the featured skills
The field-kit cards link to commit-pinned public sources. The presentation also
includes a copy-ready installation request that tells the coding agent to:
1. Detect the host's documented skill location.
2. Inspect downloaded instructions, scripts, hooks, and permissions first.
3. Show a source-to-destination plan and existing-file diffs.
4. Ask for approval before copying files.
5. Verify final paths, hashes, validation, and actual skill discovery.
Important exceptions: `ponytail-lite` is published as `AGENTS.md`, not a
conventional skill package; `token-saver` expects a separate RTK binary; and
`unlazy` includes optional hooks. The prompt does not install binaries or enable
hooks without separate approval. See [skill-sources.md](references/skill-sources.md)
for exact commits, package paths, and confidence notes.
## Hands-on lab
The presentation includes a dependency-free starter at
`hands-on/starter/`. It renders a small task board but intentionally omits the
All / Open / Done filter.
Run it from the repository root:
```bash
python3 -m http.server 4173
```
Open [http://localhost:4173/hands-on/starter/](http://localhost:4173/hands-on/starter/).
In a fresh coding-agent session, copy **Run A — Good prompt** from the
presentation. Record changed files, dependencies, checks, and evidence. Restore
the starter, then repeat with **Run B — Good prompt + skills**.
The skill-enabled prompt invokes only two working methods:
- `$ponytail-lite` keeps the implementation native and small;
- `$webapp-testing` verifies filters, URL state, history navigation,
accessibility state, empty state, and mobile layout.
The goal is not to prove that a longer prompt is better. Both prompts define
the same task contract. Run B adds reusable operating discipline without
repeating those skill instructions inside the prompt.
Compare:
| Signal | Useful question |
| :--- | :--- |
| Files changed | Did the agent stay inside `hands-on/starter/`? |
| Dependencies | Did it add a library where native APIs were enough? |
| Verification | Did it actually exercise URL reload and browser history? |
| Evidence | Did the final response name checks and results? |
| Complexity | Is the solution proportionate to three tasks and three filters? |
### Hands-on rules lab
A second lab at `hands-on/rules/` mirrors the starter's visual system and runs
the same exercise against rule sources. It lists five toggleable rule sources
— `AGENTS.md`, the `gate-discipline` skill body, the Husky `pre-commit` hook,
the `check-ui-contract.mjs` enforcer, and `commitlint` — and rebuilds the
**ruled** prompt live as each toggle flips.
Run it:
```bash
python3 -m http.server 4173
```
Open [http://localhost:4173/hands-on/rules/](http://localhost:4173/hands-on/rules/).
Compare the **naive** and **ruled** prompt panels. Toggle rules off to shrink
the prompt; toggle them on to add more guards. Copy the final prompt and run
it against a real coding agent.
## Rules and enforcement case study
The separate `/rules/` page uses `netcracker/interview` as a concrete example
of repository-level control. Its interactive pipeline shows five layers:
1. `AGENTS.md` gives every agent the same product and toolchain context.
2. `.agents/skills/` loads narrow procedures for frontend, Go API, gates,
parallel work, repository ledgers, issues, skill writing, and technical debt.
3. `pnpm check:ui` compares violations with a baseline that may only decrease.
4. Husky runs lint-staged and the UI ratchet before commit; commitlint enforces
Conventional Commit messages.
5. `.pr-review.json` supplies repository-specific policy to the AI reviewer,
while the verifier agent reruns gates independently before merge.
The page links directly to each implementation in Gitea and includes a
copy-ready, read-only prompt for mapping the same enforcement layers in another
repository. Update `rules/index.html`, `rules/app.js`, and `rules/styles.css`
together when the underlying interview workflow changes.
## Troubleshooting
| Symptom | Check | Fix |
| :--- | :--- | :--- |
| Live page is old | Compare `main`, `pages`, and remote SHAs | Fast-forward and push `pages`; retry with `?v=COMMIT` |
| `ERR_SSL_PROTOCOL_ERROR` | Confirm the hostname | Use `netcracker.pages.marcospaulo.dev.br/ai-for-dummies/` |
| Portuguese copy is missing | Inspect `translations.pt` and dynamic interaction data | Add both static and dynamic translations; reload after clearing saved language if needed |
| New control does nothing | Check `data-*` hook, event binding, and render function | Add the hook to `scripts/verify.mjs`; run `node --check app.js` |
| Desktop works, mobile breaks | Inspect the section below 800 px and 600 px | Add an explicit stacking rule and preserve focus visibility |
| Worktree creation says branch is checked out | Run `git worktree list` | Reuse or remove the existing worktree; do not force it |
| Pages push is rejected | Fetch and inspect remote branch state | Reconcile normally; never force-push without an explicit recovery decision |
## Safe rollback
Prefer a normal revert so history and the `pages` branch remain fast-forwardable:
```bash
git switch main
git revert BAD_COMMIT
git push origin main
git worktree add /tmp/ai-for-dummies-pages pages
git -C /tmp/ai-for-dummies-pages merge --ff-only origin/pages # local pages is often stale
git -C /tmp/ai-for-dummies-pages merge --no-edit main
git -C /tmp/ai-for-dummies-pages push origin pages
git worktree remove /tmp/ai-for-dummies-pages
```
Verify the live URL after rollback. Do not use `reset --hard` or force-push for
ordinary content recovery.
## Completion checklist
- [ ] English content is complete without JavaScript.
- [ ] Portuguese static and dynamic copy is complete.
- [ ] Mouse and keyboard interactions work.
- [ ] Full HD, 4K, and mobile layouts remain readable.
- [ ] `npm run verify`, `node --check app.js`, and `git diff --check` pass.
- [ ] `main` is pushed.
- [ ] `pages` fast-forwards to the same commit.
- [ ] Live endpoint returns HTTP 200 and contains the new section.
- [ ] Research links and this SilverBullet guide are updated when the workflow changes.
## Resumo rápido em português
Edite sempre em `main`, rode as verificações, faça commit e push, depois avance
`pages` por fast-forward usando um worktree temporário. O servidor externo do
Gitea Pages publica diretamente essa branch. Use o endereço com
`netcracker.pages.../ai-for-dummies/`; o formato inverso quebra o TLS.
Para agentes: modelo forte planeja e revisa; workers delimitados implementam em
worktrees separados; evidências voltam ao orquestrador. Para skills: capture
uma decisão repetida, defina um gatilho preciso, crie apenas os recursos úteis,
escreva orientação que muda decisões e valide estrutura **e** comportamento.