anentrypoint/gm-zed

GM — zed Platform

AI-native software engineering orchestrated via skill chain: PLAN → EXECUTE → EMIT → VERIFY → UPDATE-DOCS.

Bootstrap pattern: bun x gm-plugkit@latest --daemon downloads the correct platform binary, verifies SHA256, and starts the spool watcher daemon. Call once at session start; idempotent on subsequent calls. All execution routes through the file-spool: write to .gm/exec-spool/in/<lang>/<N>.<ext> or in/<verb>/<N>.txt, poll out/<N>.json for results.

Session-ID threading (no session-start hook): At skill invoke time, generate or detect SESSION_ID (env var SESSION_ID or uuid()). Pass sessionId: "<id>" in every rs-exec RPC body (spawn, tail, watch, etc.) and every spool-written task body. All task-scoped cleanup (deleteTask, getTask, appendOutput, killSessionTasks) requires matching sessionId. Absence is forbidden — hard reject by rs-exec handler.

Spool dispatch surface: Write to .gm/exec-spool/in/<lang>/<N>.<ext> (languages: nodejs, python, bash, typescript, go, rust, c, cpp, java, deno) or in/<verb>/<N>.txt (verbs: codesearch, recall, memorize, wait, sleep, status, close, browser, runner, etc.). Watcher executes and streams out/<N>.out (stdout) + out/<N>.err (stderr) line-by-line, then out/<N>.json metadata (exitCode, durationMs, timedOut, startedAt, endedAt) at completion.

End-to-end skill chaining (skills-based platforms): When gm SKILL.md includes end-to-end: true, adapter detects signal and parses stdout for trailing JSON: {"nextSkill": "...", "context": {...}, "phase": "..."}. If nextSkill is non-null, invoke Skill(skill="gm:<nextSkill>") with context dict, repeat until null. This auto-chains 5 invocations into 1 user invocation.

Every task returns complete: taskId, exitCode, durationMs, timedOut, stdout, stderr. Background tasks return immediately with task_id; continue with in/status/<N>.txt (tail), in/watch/<N>.txt (watch), or in/close/<N>.txt (close).