refactor: apply minimal tfcode branding

- Rename packages/opencode → packages/tfcode (directory only)
- Rename bin/opencode → bin/tfcode (CLI binary)
- Rename .opencode → .tfcode (config directory)
- Update package.json name and bin field
- Update config directory path references (.tfcode)
- Keep internal code references as 'opencode' for easy upstream sync
- Keep @opencode-ai/* workspace package names

This minimal branding approach allows clean merges from upstream
opencode repository while providing tfcode branding for users.
This commit is contained in:
Gab
2026-03-24 13:19:59 +11:00
parent 8bcbd40e9b
commit a8b73fd754
608 changed files with 26 additions and 32 deletions

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.tfcode/command/learn.md Normal file
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---
description: Extract non-obvious learnings from session to AGENTS.md files to build codebase understanding
---
Analyze this session and extract non-obvious learnings to add to AGENTS.md files.
AGENTS.md files can exist at any directory level, not just the project root. When an agent reads a file, any AGENTS.md in parent directories are automatically loaded into the context of the tool read. Place learnings as close to the relevant code as possible:
- Project-wide learnings → root AGENTS.md
- Package/module-specific → packages/foo/AGENTS.md
- Feature-specific → src/auth/AGENTS.md
What counts as a learning (non-obvious discoveries only):
- Hidden relationships between files or modules
- Execution paths that differ from how code appears
- Non-obvious configuration, env vars, or flags
- Debugging breakthroughs when error messages were misleading
- API/tool quirks and workarounds
- Build/test commands not in README
- Architectural decisions and constraints
- Files that must change together
What NOT to include:
- Obvious facts from documentation
- Standard language/framework behavior
- Things already in an AGENTS.md
- Verbose explanations
- Session-specific details
Process:
1. Review session for discoveries, errors that took multiple attempts, unexpected connections
2. Determine scope - what directory does each learning apply to?
3. Read existing AGENTS.md files at relevant levels
4. Create or update AGENTS.md at the appropriate level
5. Keep entries to 1-3 lines per insight
After updating, summarize which AGENTS.md files were created/updated and how many learnings per file.
$ARGUMENTS