Coact Analyze

Analyze: see and move between the layers

Four small tools wrap the two transforms. All have a .render() for the terminal and a structured object for code.

diff — what extras an agent adds over its skill

Audit drift between the SSOT skill and a derived agent (the §3.2 extras table):

from coact import diff
print(diff(".claude/skills/ux-analyst", ".claude/agents/ux-analyst.md").render())
# rows tagged [from skill] (name/description/skills) vs [extra (agent-only)]
# (tools/model/memory/persona/returns/...)

coact diff .claude/skills/ux-analyst .claude/agents/ux-analyst.md

agent may be an AgentDefinition, an agent *.md path, or a skill source (completed first). Use it to confirm an agent didn't silently drift from its skill, or to see exactly which envelope fields COMPLETE synthesized.

estimate — the fan-out cost gate (use before you spawn)

A running fleet costs ~an order of magnitude more tokens than one agent, and the premium is worst on interdependent tasks. estimate surfaces the tradeoff so you don't pay it blindly:

from coact import estimate
est = estimate([agent_a, agent_b])
est.token_multiplier_vs_chat   # ~4× one agent, ceiling ~15× a fleet
est.interdependent             # True if they share skills or declare a `consumes` contract
est.shared_skills
print(est.recommendation)      # often: "prefer one host agent (backend='host')"

coact estimate .claude/agents/a.md .claude/agents/b.md

Heuristic: shared skills or any declared input contract ⇒ interdependent ⇒ multi-agent is a poor fit (isolation buys you nothing; context is shared anyway), so it steers you to backend="host". An independent, breadth-first set is the case where a fleet can pay off — if the task value justifies the spend.

inventory — a project's reusable AI assets

from coact import inventory
inv = inventory(".")
inv.skills, inv.agents, inv.mcp_tools
print(inv.render())

coact inventory .

Enumerates .claude/skills/ skills, .claude/agents/ derived agents, and the MCP tools declared in skills' coact: mcp blocks (skill: module:function). Reuses skill discovery and adds the agent + MCP dimensions.

back — lossy agent → skill extraction

Harvest an ad-hoc agent back into reusable procedural knowledge. Lossy by design: it strips the persona / return-contract envelope to a skill stub (name + description + a pointer to the skills the agent referenced). The actual procedure usually lives in those referenced skills, so this is a starting point you flesh out — not a faithful inverse of COMPLETE.

from coact import back
skill_stub = back(".claude/agents/ad-hoc.md")
print(skill_stub.to_string())

coact back .claude/agents/ad-hoc.md

Related

• coact-complete — produce the agents diff/estimate/inventory inspect.
• coact-realize — estimate decides the backend; realize materializes it.