# TODO

## Done

- [x] Firecracker CLI runner with snapshots (~1.1s)
- [x] Multi-agent system — overseer + agent VMs + IRC + Ollama
- [x] 5 templates, 5+ models, hot-reload, non-root agents
- [x] Tools: run_command, web_search, fetch_url, save_memory
- [x] Discoverable skill system — SKILL.md + run.py, auto-loaded
- [x] Persistent workspace + memory (MEMORY.md pattern)
- [x] Overseer: !invoke, !destroy, !list, !model, !models, !templates, !persona, !status, !help
- [x] Health checks, crash recovery, graceful shutdown, systemd
- [x] Network policies, thread safety, trigger fix, race condition fix
- [x] Install/uninstall scripts, deployed on 2 machines
- [x] Refactor: firecracker-vm.ts shared helpers, skill extraction

## Next up (Phase 5 — by priority)

### Quick wins
- [ ] Large output handling — save >2K results to file, preview + read_file
- [ ] Iteration budget — configurable max rounds per template, prevent runaway loops

### Medium effort
- [ ] Skill registry git repo — shared skills on Gitea, `fireclaw skills pull`
- [ ] Session persistence — SQLite + FTS5 in workspace
- [ ] Context compression — summarize old turns when context gets long
- [ ] !logs — tail agent history from workspace

### Bigger items
- [ ] Skill learning — agents create new skills from experience
- [ ] Cron agents — scheduled agent spawns
- [ ] Dangerous command approval — pattern detection + allowlist
- [ ] Parallel tool execution — concurrent independent tool calls

## Polish

- [ ] Agent-to-agent response quality — 7B models parrot, needs better prompting or larger models
- [ ] Cost tracking per interaction
- [ ] Execution recording / audit trail
- [ ] Update regression tests for skill system + channel layout