| name | coact |
| description | Start here for coact — the package that turns your .claude/skills/ into .claude/agents/ definitions (COMPLETE) and those into running agents (REALIZE), plus tooling to see and move between the layers (diff/estimate/inventory/back) and scaffold a starter fleet. Use when someone asks "how do I use coact", "what can coact do", "turn my skills into agents", "reuse my AI stuff across the agent stack", or isn't sure which coact capability they need. Routes to the focused coact-complete / coact-realize / coact-analyze skills. |
coact — reuse your AI stuff across the agent stack
coact ("co-act") owns the two transitions the rest of the agent-stack ecosystem
doesn't, and adds thin tooling around them:
python functions/scripts → .claude/skills/ → .claude/agents/ → running agents
(py2mcp, aw) (skill pkg) COMPLETE (coact) REALIZE (coact)
- COMPLETE — lift a
.claude/skills/ skill into an AgentDefinition by adding
the agent-only "extras envelope": persona, return contract, tools, model, memory.
Mechanical and no-LLM by default. The skill on disk stays the single source of
truth; the agent references it (never copies it).
- REALIZE — turn a definition into something that runs, choosing a backend:
host (cheapest — the host agent runs it), sdk / litellm (in-process
aw.AgenticStep runnables), or mcp (expose a skill's Python tools as a server).
The one-liner:
from coact import complete, emit_agent, realize
agent = complete(".claude/skills/ux-analyst")
print(emit_agent(agent, "claude-agents-md"))
realize(agent, backend="host")
Everything that writes or spawns has a dry-run first (progressive disclosure):
plan_completion(skill) previews COMPLETE with per-field provenance, and
realize(..., backend="host", dry_run=True) previews what REALIZE would write.
Which skill do I want?
| You want to… | Use the skill | Key calls |
|---|
| Turn a skill into an agent definition (COMPLETE) | coact-complete | plan_completion, complete, the coact: frontmatter block |
| Make a definition actually run (REALIZE) | coact-realize | realize(backend=host|sdk|mcp|litellm), dry_run, scaffold_fleet |
| See/move between the layers | coact-analyze | diff, estimate, inventory, back |
| Develop coact itself | coact-dev | architecture, invariants, extension points |
Two boundaries worth knowing up front
- Topology is out of scope. coact emits definitions + tool/MCP wiring and
stops — it doesn't serialize graphs/edges/cycles. For a multi-agent run it can
scaffold a starter shim you own (
scaffold_fleet), but it is not LangGraph.
- A running fleet is an optimization, not a default. Multi-agent fan-out costs
~an order of magnitude more tokens (worst on interdependent tasks), so
backend="host" is the default and estimate shows the trade-off before you
spend. See coact-analyze.
More
- A complete, runnable, no-API-key walkthrough:
python examples/walkthrough.py.
- CLI mirror of every verb:
coact plan|complete|emit|realize|diff|estimate|inventory|back|scaffold.
- Design rationale:
misc/docs/DECISIONS.md; reuse boundaries: misc/docs/REUSE.md.
