# Snapshot-test REPLAY config: the acp-agent plugin tree with the model backend
# swapped to llm-replay (serves a recorded session JSONL — no API key, no
# network). The dsh-acp-agent bin selects this file for DSH_SNAPSHOT=replay.
#
# Same app as cordis.yml (@deepseek-ai/dsh-acp-agent: the agent-core spine +
# JSONL persistence + the ACP bridge) — only the LLM backend differs: llm-replay
# here, llm-deepseek there. It can't reuse the real adapter because llm-deepseek's
# apply() throws without DEEPSEEK_API_KEY, killing a keyless replay run at boot.
#
# stdout is reserved for the ACP JSON-RPC protocol — no stdout logger (the app
# package omits it). The replay fixture path comes from $DSH_SNAPSHOT_FILE (and
# an optional $DSH_SNAPSHOT_OVERRIDE sidecar), set by the snapshot harness.

# The replay adapter: short-circuits llm/stream with the recorded log's chunks,
# in place of llm-deepseek.
- id: llm-replay
  name: '@deepseek-ai/dsh-llm-replay'

# Local bash executor for agent-core's tool-bash schema.
# FIXME(config-comments): keep this executor note from implying bash is the
# whole tool set; subagent and todo_write are loaded below.
- id: bash
  name: '@deepseek-ai/dsh-bash-local'
  config:
    timeoutMs: 60000

# The ACP server app — identical to cordis.yml's entry.
- id: acp-agent
  name: '@deepseek-ai/dsh-acp-agent'
  config:
    model: deepseek-v4-flash
    persistenceRoot: !!js process.env.DSH_SNAPSHOT_SESSIONS_ROOT ?? './.sessions'
    systemPrompt: |
      You are a coding assistant driven over the Agent Client Protocol.

      Your tools are bash (plus bash_output/bash_kill for background tasks)
      and subagent. Do ALL file operations through bash: read with
      cat/sed/head, search with grep, write with heredocs (cat <<'EOF' >
      file), edit with sed or a rewrite. Each bash call runs in a fresh
      shell — pass workdir instead of cd. Check the [exit code: N] marker;
      verify your work. Keep answers brief and factual.

      Use the subagent tool to delegate a focused, self-contained subtask to
      a fresh child agent (it works in its own context and returns only its
      final result) — give it a complete, standalone instruction.

      For multi-step work, use the todo_write tool to track a task list:
      send the WHOLE list each call (it replaces the previous one), keep at
      most one task in_progress (exactly one while work remains), and mark a
      task completed as soon as it is done. Skip it for trivial single-step
      tasks.

# The subagent seam + both in-process backends + two model-facing tools —
# identical to cordis.yml's wiring (only the LLM backend differs above): spawn
# and fork are each reachable via a dsh-tool-subagent bound to it with a distinct
# toolName (subagent → spawn, subagent_fork → fork).
- id: subagent
  name: '@deepseek-ai/dsh-subagent'

- id: subagent-spawn
  name: '@deepseek-ai/dsh-subagent-spawn'
  config:
    providerName: spawn

- id: subagent-fork
  name: '@deepseek-ai/dsh-subagent-fork'
  config:
    providerName: fork

- id: tool-subagent
  name: '@deepseek-ai/dsh-tool-subagent'
  config:
    provider: spawn
    toolName: subagent

- id: tool-subagent-fork
  name: '@deepseek-ai/dsh-tool-subagent'
  config:
    provider: fork
    toolName: subagent_fork

# The model-facing todo_write tool — identical to cordis.yml's wiring, so a
# replayed todo_write tool call resolves to a real tool during snapshot replay.
- id: tool-todo
  name: '@deepseek-ai/dsh-tool-todo'