libghostty-recording

# libghostty-vt Recording Skill 📹 **Trit**: 0 (ERGODIC - Coordinator) **GF(3) Triad**: `asciinema (-1) ⊗ libghostty-recording (0) ⊗ vhs (+1) = 0` ## Overview Record, stream, and replay libghostty-vt terminal sessions for documentation, debugging, and LLM training. ## Recording Methods ### 1. Asciinema (Lightweight .cast) ```bash # Record session asciinema rec ~/recordings/session-$(date +%Y%m%d_%H%M%S).cast # Auto-record all sessions (add to .zshrc) asciinema rec --append ~/recordings/daily-$(date +%Y%m%d).cast # Stream to server asciinema rec -t "libghostty demo" https://asciinema.org ``` **Pros**: Compact, text-based, searchable, LLM-friendly **Cons**: No video export ### 2. Charmbracelet VHS (GIF/Video) ```tape # demo.tape Output demo.gif Set FontSize 14 Set Width 1200 Set Height 600 Set Theme "Ghostty" Type "echo 'libghostty-vt recording'" Enter Sleep 500ms Type "skill load omniglot" Enter Sleep 1s ``` ```bash vhs demo.tape ``` **Pros**: Produces shareable GIFs, scriptable **Cons**: Larger files ### 3. libghostty-vt Native Hooks ```zig // Hook into libghostty-vt stream const recorder = ghostty_vt.Recorder.init(.{ .output = "session.cast", .format = .asciinema_v2, }); terminal.setOutputHook(recorder.hook); ``` ## CI Gate Controls (on the way IN) ### Pre-Installation Validation ```yaml # .github/workflows/skill-gate.yml name: Skill Installation Gate on: pull_request: paths: - 'skills/**' - 'SKILL.md' jobs: validate-skills: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 - name: Validate GF(3) conservation run: | # Sum all trits, must equal 0 mod 3 python3 -c " import json skills = json.load(open('skills.json')) total = sum(s.get('trit', 0) for s in skills) assert total % 3 == 0, f'GF(3) violation: sum={total}' print('✓ GF(3) conserved') " - name: Check SKILL.md structure run: | for f in skills/*/SKILL.md; do grep -q "^# " "$f" || (echo "Missing title: $f" && exit 1) grep -q "Trit" "$f" || (echo "Missing trit: $f" && exit 1) done echo "✓ All skills have required structure" - name: Verify no placeholder tokens run: | ! grep -rE "(TODO|FIXME|placeholder|mock-|pseudo-)" skills/ || \ (echo "❌ Placeholder tokens found" && exit 1) ``` ### Local Gate ```bash # Validate before install validate-skills() { local repo=$1 gh api repos/$repo/contents/skills.json -q '.content' | \ base64 -d | python3 -c " import json, sys skills = json.load(sys.stdin) total = sum(s.get('trit', 0) for s in skills) if total % 3 != 0: print(f'❌ GF(3) violation: {total}') sys.exit(1) print(f'✓ {len(skills)} skills, GF(3) conserved') " } # Use before install validate-skills plurigrid/asi && \ npx ai-agent-skills install plurigrid/asi --agent codex ``` ## LLM Training from Recordings From asciinema discourse (2024): > "My real interest is not so much in playing back the recordings but in using the `.cast` files for creating a vector database that I can then query and use an LLM to extract useful workflows." ### Cast File → Vector DB ```python import json import duckdb def parse_cast(cast_file: str) -> list: """Extract commands and outputs from .cast file""" with open(cast_file) as f: lines = f.readlines() header = json.loads(lines[0]) events = [json.loads(line) for line in lines[1:]] return [{ "timestamp": e[0], "type": e[1], # 'o' = output, 'i' = input "data": e[2] } for e in events] # Store in DuckDB for querying con = duckdb.connect("recordings.duckdb") con.execute(""" CREATE TABLE IF NOT EXISTS terminal_events ( session_id VARCHAR, timestamp DOUBLE, event_type VARCHAR, data VARCHAR, embedding FLOAT[1024] ) """) ``` ## Integration with libghostty-ewig From [libghostty-ewig.jl](file:///Users/bob/ies/libghostty-ewig.jl): ```julia # Connect libghostty-vt parsing to ewig modal editor module LibghosttyEwig # VT escape sequence parsing # Gay.jl color integration # Modal editing state machine end ``` ## Best Practices 1. **Daily auto-recording**: Start asciinema on shell init 2. **Session naming**: `session-{date}_{project}_{task}.cast` 3. **Compression**: Cast files are JSON, gzip well 4. **Privacy**: Filter secrets with `asciinema rec --env=TERM` 5. **Playback speed**: `asciinema play -s 2 session.cast` ## Files | Path | Purpose | |------|---------| | `~/recordings/` | Default recording directory | | `~/.config/asciinema/` | Asciinema config | | `~/ies/ghostty-vt-src/` | libghostty-vt source | ## References - [asciinema/asciinema](https://github.com/asciinema/asciinema) - Terminal recorder - [charmbracelet/vhs](https://github.com/charmbracelet/vhs) - CLI GIF recorder - [libghostty-vt](https://mitchellh.com/writing/libghostty-is-coming) - Mitchell's blog post ## 490 Skills Installed ✓ ``` npx ai-agent-skills install plurigrid/asi --agent codex Installed 490 skill(s) from plurigrid/asi ``` --- ## Autopoietic Marginalia > **The interaction IS the skill improving itself.** Every use of this skill is an opportunity for worlding: - **MEMORY** (-1): Record what was learned - **REMEMBERING** (0): Connect patterns to other skills - **WORLDING** (+1): Evolve the skill based on use *Add Interaction Exemplars here as the skill is used.*