Gogh

Source cited Obsidian operating brain that turns six frontend design skills into one advisable stack.

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Gogh is an evidence-gated Obsidian brain for AI coding agents that need better frontend design judgment than a single prompt can provide. It combines six curated skills into stack advice: which skill to use when, how to combine them, how to resolve conflicts, and how to keep a unified anti-slop rulebook tied to dated sources.

Current maturity: market-ready pending the release gate. The structural audit passes at market-ready; the final release is cut by the operator procedure.

Promise

Turn six frontend design skills into one source-cited operating brain for stacking taste prompting, micro-interaction execution, toolchain enforcement, retrieval, and audit work.

Buyer

Developers, indie hackers, design engineers, and agencies who build frontends with AI coding agents (Claude Code, Cursor, Codex, Windsurf) and want the output to look intentional and brand-fit rather than templated AI slop.

Six Skills

Mechanism map: taste prompting from taste-skill and Anthropic frontend-design, micro-interaction execution from make-interfaces-feel-better, toolchain enforcement from Impeccable, retrieval from ui-ux-pro-max, and audit from Vercel web-design-guidelines.

Showcase

Six message-match variants of one landing page, each a distinct editorial format instead of a generic template. Full source in examples/aimh-pro-landing-variants/, live at agricidaniel.github.io/gogh.

Outputs

• assets/template-brain/, the distributable Obsidian template vault
• SKILL.md plus scripts, the agent-facing operating layer
• Stack advisor reports that translate a project brief into recommended skills, order of use, dial settings, conflicts, and audit checks
• Cheat sheet, stack decision one-pager, unified pre-flight mega-checklist, and quickstart
• Registry diff reports for upstream captures when the adapter wave lands

Quick Start

python -m pip install -e .
gogh demo
gogh lint --vault examples/sample-vault
gogh report --vault examples/sample-vault --html-only

To create a client vault:

gogh new acme --client-name "Acme Co" --owner "Daniel Agrici" --out-dir ~/gogh-vaults
gogh ingest --vault ~/gogh-vaults/acme --file tests/fixtures/sample-source.md
gogh synthesize --vault ~/gogh-vaults/acme
gogh visuals --vault ~/gogh-vaults/acme
gogh report --vault ~/gogh-vaults/acme --html-only
gogh next --vault ~/gogh-vaults/acme

Boundaries

V1 is advisory and read-only. It does not mutate accounts, systems, books, pipelines, publishing tools, customer records, or live production data.

Gogh records conflicts between skills instead of hiding them. Impeccable detector results remain upstream Impeccable results, not Gogh-owned findings. License, version, star, and rule claims are treated as current only when backed by the source ledger.

Maturity Gates

1. Scaffolded: product shell, vault, source pack, scripts, tests, and demo exist.
2. Researched: dated trustworthy sources replace placeholder research.
3. Domain-adapted: real domain importer, synthesis, reports, fixtures, and tests exist.
4. Demo-verified: sample vault regenerates deterministically and reports cite sources.
5. Market-ready: audit score is at least 90 with no critical failures.

Scores are capped by maturity. A scaffold cannot become market-ready by edited markdown alone.

Research Policy

Use official, primary, or vendor documentation first. Use market or practitioner sources only as supporting evidence. Do not treat blog roundups or AI summaries as primary truth. Record evidence in references/source-ledger.json; prose-only research notes do not satisfy the gate.

Release

python scripts/package_release.py --version 1.0.0 --release-type market-ready

Release packaging scans for secrets, local paths, symlinks, untracked drift, and unsafe ZIP entries before writing dist/RELEASE_MANIFEST.json and dist/SHA256SUMS. Market-ready packaging also runs scripts/audit_brain.py.

Community

• 💬 AI Marketing Hub: free community for AI-powered marketing
• ⚡ AI Marketing Hub Pro: early access to in-development features and direct collaboration
• 🎬 YouTube: tutorials and demos