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Get Started Free →Delegate analytical, sandboxed, or long-running work to the Google Antigravity (agy) CLI via a JSON-bridge wrapper. Use when you need a second opinion, sandboxed execution, or a detached long-running agent loop. Install location is .agents/skills/collaborating-with-antigravity/.
.claude/skills/bilal140202-collaborating-with-antigravity/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-16 | ✗→✓ | ▲ Improved | 48% | 0% |
| case-01 | ✗→✓ | ▲ Improved | 36% | 0% |
| case-17 | ✗→✓ | ▲ Improved | 28% | 0% |
| case-02 | ✗→✓ | ▲ Improved | 281% | 0% |
| case-03 | ✗→✓ | ▲ Improved | 32% | 0% |
agy-bridge is a thin JSON wrapper around the Google Antigravity (agy) CLI plus a gemini CLI fallback. The bridge returns stable BridgeResponse envelopes designed to drop cleanly into Codex's exec-json protocol via --output-protocol codex.
bug, design call, or code review.
touching the main checkout.
Codex continues other work.
Avoid for trivial single-step questions — the round-trip is overkill.
bashpython scripts/agy_bridge.py \ --cd "/path/to/project" \ --PROMPT "Find every place that calls db.commit() without a try/except." \ --mode review \ --output-protocol codex
The bridge prints one JSON line on stdout: {"success": true, "SESSION_ID": "…", "agent_messages": "…", "adapter": {…}}. With --output-protocol codex the event log conforms to Codex exec-json (thread.started, item.completed, turn.completed).
| Mode | Use it for | Worktree | Writes | |------|-----------|----------|--------| | ask (default) | Q&A, code reading | no | no | | plan | Multi-step planning | no | no | | prototype | Diff-only suggestions | optional | no | | review | Critique a staged change | no | no | | execute | Apply edits in a worktree | yes | requires --allow-write | | browser | Research with browsing | no | no | | long | Detached agent loop | no | varies |
Capture and reuse SESSION_ID:
bash# Turn 1 python scripts/agy_bridge.py --cd /proj --PROMPT "Find race conditions in src/queue/" # → {"SESSION_ID": "abc-123", ...} # Turn 2 python scripts/agy_bridge.py --cd /proj --SESSION_ID abc-123 \ --PROMPT "Propose a minimal fix for the worst one."
Codex projects that run long agent loops should prefer the MCP tool surface (agy_start / agy_status / agy_result / agy_read / agy_cancel / agy_sessions) over polling the CLI in a shell loop. The supervisor handles worker thread lifecycle, log spooling, and cross-platform process-group cleanup.
--output-protocol codex — Codex-shaped exec-json events(thread.started, item.completed, turn.completed).
--output-protocol claude — Claude Code stream-json (default).--output-protocol raw — internal canonical event envelope.The bridge scrubs secrets from every error / log / response, runs under SafetyPolicy, and refuses destructive prompts even with --allow-write. The doctor (agy_doctor MCP tool, or python -m agy_mcp.doctor) reports the environment without leaking secrets.
references/usage.md — full CLI flag reference + MCP tool surface +exit codes.
references/prompt-patterns.md — proven prompt scaffolds per mode.references/security.md — threat model, secret scrub, denylist,worktree, audit log layout.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-06 | pass→pass | 10,890 | 5,490 | -50% | 1 | 1 | 0% | 1,701 | 1,843 | +8% | 0 | 0 | — |
case-16 | fail→pass | 7,658 | 10,841 | +42% | 1 | 1 | 0% | 1,542 | 2,275 | +48% | 0 | 0 | — |
case-01 | fail→pass | 12,867 | 9,342 | -27% | 1 | 1 | 0% | 1,591 | 2,160 | +36% | 0 | 0 | — |
case-17 | fail→pass | 4,695 | 1,300 | -72% | 1 | 1 | 0% | 815 | 1,045 | +28% | 0 | 0 | — |
case-02 | fail→pass | 4,447 | 4,308 | -3% | 1 | 1 | 0% | 337 | 1,285 | +281% | 0 | 0 | — |
case-03 | fail→pass | 6,113 | 3,257 | -47% | 1 | 1 | 0% | 1,216 | 1,605 | +32% | 0 | 0 | — |
case-04 | pass→pass | 4,775 | 2,734 | -43% | 1 | 1 | 0% | 955 | 1,413 | +48% | 0 | 0 | — |
case-05 | pass→pass | 4,878 | 2,701 | -45% | 1 | 1 | 0% | 995 | 1,415 | +42% | 0 | 0 | — |
case-07 | fail→fail | 13,679 | 5,580 | -59% | 1 | 1 | 0% | 2,567 | 1,214 | -53% | 0 | 0 | — |
case-08 | fail→pass | 8,828 | 5,657 | -36% | 1 | 1 | 0% | 2,110 | 1,887 | -11% | 0 | 0 | — |
case-09 | fail→fail | 4,031 | 27,986 | +594% | 1 | 1 | 0% | 254 | 1,161 | +357% | 0 | 0 | — |
case-10 | fail→pass | 6,786 | 5,999 | -12% | 1 | 1 | 0% | 1,286 | 1,954 | +52% | 0 | 0 | — |
case-11 | fail→pass | 11,682 | 3,464 | -70% | 1 | 1 | 0% | 1,902 | 1,527 | -20% | 0 | 0 | — |
case-12 | fail→pass | 11,975 | 9,571 | -20% | 1 | 1 | 0% | 2,621 | 2,121 | -19% | 0 | 0 | — |
case-13 | fail→pass | 4,521 | 5,013 | +11% | 1 | 1 | 0% | 904 | 1,294 | +43% | 0 | 0 | — |
case-14 | fail→pass | 15,019 | 5,157 | -66% | 1 | 1 | 0% | 3,162 | 1,835 | -42% | 0 | 0 | — |
case-15 | fail→pass | 4,176 | 5,762 | +38% | 1 | 1 | 0% | 661 | 1,258 | +90% | 0 | 0 | — |
case-18 | fail→pass | 12,473 | 1,844 | -85% | 1 | 1 | 0% | 2,194 | 1,114 | -49% | 0 | 0 | — |
case-19 | fail→pass | 10,439 | 4,854 | -54% | 1 | 1 | 0% | 1,692 | 1,070 | -37% | 0 | 0 | — |
case-20 | pass→pass | 4,796 | 2,416 | -50% | 1 | 1 | 0% | 887 | 1,342 | +51% | 0 | 0 | — |
case-21 | fail→pass | 10,348 | 3,342 | -68% | 1 | 1 | 0% | 1,872 | 1,359 | -27% | 0 | 0 | — |
case-22 | fail→pass | 12,962 | 2,937 | -77% | 1 | 1 | 0% | 2,352 | 1,391 | -41% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted, and 19 counted toward the lift figure. The other 3 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +73 percentage points is the difference between those two pass rates over the 19 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.