fireclaw/TODO.md

TODO

Done

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