diff --git a/docs/environment-and-policy.md b/docs/environment-and-policy.md
index 2b85dee7..9721f964 100644
--- a/docs/environment-and-policy.md
+++ b/docs/environment-and-policy.md
@@ -174,7 +174,7 @@ The split pays off when there is a policy worth learning. Skip it in these cases
 - [Machines as data](machines-as-data.md): the config format and `describeMachine`.
 - [Testing and verification](verify.md): `canReach`, `explorePaths`, and payload variants.
 - [Event log](event-log.md) and [Persistence](persistence.md): stores, threads, and replay.
-- Agent skills: [`graph-engineering`](https://github.com/statelyai/agent/blob/main/skills/graph-engineering/SKILL.md) routes to [`loop-to-machine`](https://github.com/statelyai/agent/blob/main/skills/loop-to-machine/SKILL.md) converts existing agent code and checks parity with `compareRuns`, or to [`generate-machine`](https://github.com/statelyai/agent/blob/main/skills/generate-machine/SKILL.md), which authors a JSON policy from scratch; then [`add-traces`](https://github.com/statelyai/agent/blob/main/skills/add-traces/SKILL.md) stamps and persists runs, [`add-evals`](https://github.com/statelyai/agent/blob/main/skills/add-evals/SKILL.md) writes evals per state, and [`improve-machine`](https://github.com/statelyai/agent/blob/main/skills/improve-machine/SKILL.md) improves a policy from traces and hands the change to a person as a pull request.
+- The [`graph-engineering`](https://github.com/statelyai/agent/blob/main/skills/graph-engineering/SKILL.md) skill routes existing code to [`loop-to-machine`](https://github.com/statelyai/agent/blob/main/skills/loop-to-machine/SKILL.md), which converts the loop and checks parity with `compareRuns`. It routes new policies to [`generate-machine`](https://github.com/statelyai/agent/blob/main/skills/generate-machine/SKILL.md). Then [`add-traces`](https://github.com/statelyai/agent/blob/main/skills/add-traces/SKILL.md) persists runs, [`add-evals`](https://github.com/statelyai/agent/blob/main/skills/add-evals/SKILL.md) writes evaluations for each state, and [`improve-machine`](https://github.com/statelyai/agent/blob/main/skills/improve-machine/SKILL.md) proposes a pull request from traces, or reports that no candidate beat the current policy.
 - [examples/self-improving-refunds](../examples/self-improving-refunds): a refund desk as the environment, JSON playbooks as the policy, an improvement loop, and a benchmark against a raw tool loop.
 - [examples/code-review-loop](../examples/code-review-loop): review gates as the environment, a triage policy improved from traces against hidden tests, run with Claude Code in headless mode.
 - [examples/tic-tac-toe](../examples/tic-tac-toe): a game as the environment, a policy over board features, and a `canReach` proof that it never loses as the bar for promotion.