keisei/KeiSeiKit-1.0
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KeiSeiKit-1.0/_blocks/deploy-local-only.md
Parfii-bot 0be354a920 KeiSeiKit-public — clean state 
Single-commit clean baseline after security scrub of niche-tells,
project codenames, internal jargon, and contributor-email leaks.

Contents:
- 100 Rust crates (_primitives/_rust/)
- 37 agent manifests (_manifests/) + generated specs (_generated/)
- 67 user-invocable skills (skills/)
- 33 hooks (hooks/)
- Composition blocks (_blocks/)
- Documentation (docs/, README.md)
- TS adapter packages (_ts_packages/)
- Assembler (_assembler/)
- Roles (_roles/)
- Templates (_templates/)
- Forgejo CI (.forgejo/)

Author: Denis Parfionovich <info@greendragon.info>

License: see LICENSE.
2026-05-01 12:09:03 +08:00

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DEPLOY — LOCAL ONLY (sensitive / pre-disclosure project)

Use this block for any project that CANNOT be publicly deployed — typical triggers: proprietary ML weights/architectures you don't want in public training corpora, security tooling that burns its own usefulness on exposure, kernel-level code, client-confidential codebases.

Hard forbidden (no matter how small the change):

  • Public-URL share pages / static HTML dumps to public hosting
  • Vercel / Netlify / GitHub Pages / Cloudflare Pages public deploy
  • gh repo create public, gh repo edit --visibility public
  • git push to a public remote (GitHub, public GitLab)
  • Publishing architecture diagrams with node counts, param totals, or training configs
  • Public benchmark tables naming this project

Allowed:

  • Private remotes (self-hosted Forgejo/Gitea over SSH on a private network)
  • Tailscale-only internal services
  • Local-only 127.0.0.1 / LAN dev servers
  • .app / .dmg distribution via private channels

Double-confirmation override (both phrases required, in order, exact wording):

  1. "yes, deploy"
  2. "I confirm publication"

No approximations. Informal variants do NOT count. If either phrase is absent, refuse.

Example categories that typically require local-only: censorship-circumvention tooling (public push burns exit-node IPs), ML ensembles with trained weights, control / guidance algorithms, offensive security research.

Report field: "Public-deploy surface touched: none | — double-confirm obtained yes/no."