Every AI coding session on your team
learns from the ones before it.
Your team's Claude gets smarter with every session. Parker captures what engineers discover and shares it automatically.
No manual documentation. No CLAUDE.md files going stale. Parker watches how your team works in Claude Code and builds a living knowledge layer that every future session draws from.
— figure out who owns payments module
— discover withRetry() convention the hard way
— repeat what teammate learned yesterday
withRetry()npm install -g @parkercto/cliOne command. No admin approval. Works with Claude Code.
The problem
Knowledge evaporates between sessions.
Your engineers solve hard problems every day inside Claude Code. Tomorrow, those insights are gone.
Every session starts from zero.
An engineer spends 20 minutes tracing a dependency chain, discovers a convention, works around a gotcha. Next day, a teammate hits the same files and starts from scratch. The knowledge was never captured.
- ✕10-15 min reloading context in every session
- ✕CLAUDE.md maintained by hand — stale within days
- ✕Same mistakes repeat because discoveries aren't shared
- ✕Teammates solve the same problem independently, over and over
One command. Then the loop closes.
Run parker enable once. Parker hooks into Claude Code, watches for knowledge-rich patterns — error recovery, dependency tracing, backtracking — and extracts what was learned. Next time anyone touches those files, the knowledge appears automatically.
- ✓Discoveries captured automatically from real sessions
- ✓Context injected at session start — no manual loading
- ✓Knowledge compounds across the team, not just one engineer
- ✓Nothing to maintain — the loop runs itself
The loop
See how knowledge compounds
Parker hooks into Claude Code sessions. No new tools. No workflow changes. Knowledge flows automatically.
constructEventAsync() instead of constructEvent().withRetry(). Jordan hit a rate limit bug last week when this was skipped.Working with a handful of Series A & B teams.
If your agents are capable but your context is slowing them down — that's the problem we exist to solve. We're looking for engineering teams who want to shape the product alongside us.
Early access. Direct line to the team. Pricing that reflects the partnership.
tremayne@ineedacto.comparkerteam.ai