Bring the interception-seams branch onto current master (via A→B). The substantive reconciliation is master's compaction `agent/pre-step` serial seam meeting C's interception seams: - types.ts: keep BOTH master's `agent/pre-step` AND C's new interception events (`agent/prompt-submit`, `agent/session-start`, `agent/turn-continuation`→ `ContinuationDecision`); drop the turn-mirror declarations (removed on A). - loop.ts: the merged per-turn order is `turn/start` → per queued msg `agent/prompt-submit` (rewrite/inject/block) → (fully-blocked ⇒ zero-step `rejected`) → per step: drain steering → assemble system prompt → `agent/pre-step` (compaction, OUTSIDE the step) → `step/start` → single `deriveMessages()` → model → tools/pre-execute·dispatch·post-execute. No turn-mirror emits; `closeTurn()` is the A-simplified single-call form. - Docs (architecture, core.md, agent/agent-loop READMEs, catalog) reconciled to show C's interception seams alongside `agent/pre-step`, no turn/step mirrors. - rfc/README: dropped the stale `proposed/` compaction row (master moved that RFC to implemented/); kept C's new `pre-tool-input-rewrite` proposed row. - interception.spec.ts: migrated its two `agent/turn-end` reason collectors to the `turn/end` session event, and ADDED a cross-test proving a `prompt-submit` rewrite + additionalContext is VISIBLE to an `agent/pre-step` listener on the same turn — pinning the merged seam ordering (compaction sees the post-prompt-submit surface, not stale history).
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Cookbook: extension plugin shapes
The three plugin shapes you write against the harness extension surface, as illustrative snippets (elided imports and helper stubs — not copy-paste-complete). For the full step-by-step guides see adding a package, adding a tool, and adding an LLM adapter; for the seams these hook into see docs/architecture.md.
A tool plugin
A tool registers on ctx.tools. The annotated defineTool example (typed execute args, result shaping, the run_in_background pattern) lives in adding-a-tool.md — that guide is the source of truth for the tool shape. Raw JSON-Schema ToolDefinitions are also accepted by ctx.tools.register() directly (that is how MCP-sourced tools arrive); defineTool is the typed sugar for first-party tools.
A hook plugin (permission gate)
A hook returns a typed decision from the tools/pre-execute gate to allow or deny a call — the seam where sandbox, permission, and plan-mode plugins live. (A "native hook" is just this: an ordinary cordis plugin on the interception seams, returning typed decisions — no external protocol needed.)
import type { Context } from 'cordis'
import type { PreToolDecision, ToolExecution } from '@deepseek-ai/dsh-tools'
declare function isAllowed(exec: ToolExecution): Promise<boolean>
export const name = 'permission-gate'
export function apply(ctx: Context) {
ctx.on('tools/pre-execute', async (exec, next): Promise<PreToolDecision> => {
if (!(await isAllowed(exec))) {
return { kind: 'deny', reason: 'Denied by policy.' }
}
return next()
})
}
A UI plugin
A UI plugin consumes agent/stream-chunk and session events for rendering, and drives input back in via agent.send() / agent.steer().
import type { Context } from 'cordis'
import { AgentId } from '@deepseek-ai/dsh-agent'
declare function render(text: string): void
declare function onUserInput(handler: (text: string) => void): void
export const name = 'my-ui'
export const inject = ['agents']
export function apply(ctx: Context) {
ctx.on('agent/stream-chunk', (agent, turn, step, chunk) => {
if (chunk.type === 'text-delta') render(chunk.text)
})
onUserInput(text => ctx.agents.get(AgentId('main'))?.send([{ type: 'text', text }]))
}
A client-driver plugin (external protocol bridge)
A client driver is a UI plugin whose "user" is another program speaking a wire protocol rather than a human at a terminal. It owns the process's stdio (so it must run with no stdout logger — every non-protocol byte corrupts the stream), creates/resumes agents on demand through the dsh-agent factory seam, translates harness events (session/event, agent/*) into outbound protocol messages, and translates inbound requests back into agent.send() / agent.cancel(). Two harness-specific contracts make it correct: resolve each request exactly once off a settle signal (settle from the durable turn/end session event — the boundary is a session event, not an agent/* mirror — with agent/status as the fallback if a peer listener starved yours), and tear each agent down through its AgentHandle.dispose() (which stops the loop, awaits its exit, and unregisters), not just cancel() — disposal must reach quiescence, not merely request it.
packages/ui/acp is the worked example: it bridges the agent to the Agent Client Protocol (JSON-RPC over stdio) so Zed and other ACP editors can drive it. See its README for the full method surface and the deferred-permission-gate note.
import type { Context } from 'cordis'
export const name = 'my-protocol-bridge'
export const inject = ['agents', 'sessions', 'sessionPersistence']
export function apply(ctx: Context) {
// Stream every logged assistant text/reasoning delta out to the client.
ctx.on('session/event', (_session, event) => {
if (event.type === 'assistant/chunk') {
const chunk = event.data.chunk
if (chunk.type === 'text-delta') {
// sendToClient({ kind: 'message_chunk', text: chunk.text })
}
}
})
// Inbound "prompt": create/resume an agent and feed it; settle on turn end.
// Teardown reaches quiescence via AgentHandle.dispose() (stop + await exit).
}
Runnable wirings
Three complete examples load their plugin trees from cordis.yml: examples/echo-agent (mock model + echo tool — the all-mock skeleton check, pnpm run demo:echo), examples/coding-agent (DeepSeek V4 + the bash tool suite — the real thing, pnpm run demo:coding), and examples/acp-agent (the same coding agent exposed as an ACP server over JSON-RPC stdio — the client-driver shape, pnpm run demo:acp). Each leaf is now just its swappable backends plus an app-package entry: the stdio demos load @deepseek-ai/dsh-stdio-agent, the ACP demo loads @deepseek-ai/dsh-acp-agent, and both app packages share the spine via the @deepseek-ai/dsh-agent-core bundle.