How do I install the Coact skill?

Run npx skillmds@latest add thorwhalen/coact in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Coact safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Coact?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Coact free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

thorwhalen (@thorwhalen) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Coact by thorwhalen — Agent Skill

Name: Coact
Author: thorwhalen

Coact

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.

thorwhalen 0435d0c 3.4 KB Updated Sep 17, 2026

File contents

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")   # Skill -> AgentDefinition (no LLM)
print(emit_agent(agent, "claude-agents-md"))     # the .claude/agents/*.md
realize(agent, backend="host")                   # materialize so Claude Code runs it

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.

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.

---name: coactdescription: 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 ecosystemdoesn'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:```pythonfrom coact import complete, emit_agent, realizeagent = complete(".claude/skills/ux-analyst") # Skill -> AgentDefinition (no LLM)print(emit_agent(agent, "claude-agents-md")) # the .claude/agents/*.mdrealize(agent, backend="host") # materialize so Claude Code runs it```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`.

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