88 lines
3.6 KiB
YAML
88 lines
3.6 KiB
YAML
# Snapshot-test REPLAY config: the acp-agent plugin tree with the model backend
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# swapped to llm-replay (serves a recorded session JSONL — no API key, no
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# network). The dsh-acp-agent bin selects this file for DSH_SNAPSHOT=replay.
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#
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# Same app as cordis.yml (@deepseek-ai/dsh-acp-agent: the agent-core spine +
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# JSONL persistence + the ACP bridge) — only the LLM backend differs: llm-replay
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# here, llm-deepseek there. It can't reuse the real adapter because llm-deepseek's
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# apply() throws without DEEPSEEK_API_KEY, killing a keyless replay run at boot.
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#
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# stdout is reserved for the ACP JSON-RPC protocol — no stdout logger (the app
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# package omits it). The replay fixture path comes from $DSH_SNAPSHOT_FILE (and
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# an optional $DSH_SNAPSHOT_OVERRIDE sidecar), set by the snapshot harness.
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# The replay adapter: short-circuits llm/stream with the recorded log's chunks,
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# in place of llm-deepseek.
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- id: llm-replay
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name: '@deepseek-ai/dsh-llm-replay'
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# Local bash executor for agent-core's tool-bash schema.
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# FIXME(config-comments): keep this executor note from implying bash is the
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# whole tool set; subagent and todo_write are loaded below.
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- id: bash
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name: '@deepseek-ai/dsh-bash-local'
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config:
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timeoutMs: 60000
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# The ACP server app — identical to cordis.yml's entry.
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- id: acp-agent
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name: '@deepseek-ai/dsh-acp-agent'
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config:
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model: deepseek-v4-flash
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persistenceRoot: !!js process.env.DSH_SNAPSHOT_SESSIONS_ROOT ?? './.sessions'
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systemPrompt: |
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You are a coding assistant driven over the Agent Client Protocol.
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Your tools are bash (plus bash_output/bash_kill for background tasks)
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and subagent. Do ALL file operations through bash: read with
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cat/sed/head, search with grep, write with heredocs (cat <<'EOF' >
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file), edit with sed or a rewrite. Each bash call runs in a fresh
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shell — pass workdir instead of cd. Check the [exit code: N] marker;
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verify your work. Keep answers brief and factual.
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Use the subagent tool to delegate a focused, self-contained subtask to
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a fresh child agent (it works in its own context and returns only its
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final result) — give it a complete, standalone instruction. Use
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subagent_fork instead when the subtask needs THIS conversation's
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context: the child inherits the log so far.
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For multi-step work, use the todo_write tool to track a task list:
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send the WHOLE list each call (it replaces the previous one), keep at
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most one task in_progress (exactly one while work remains), and mark a
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task completed as soon as it is done. Skip it for trivial single-step
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tasks.
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# The subagent seam + both in-process backends + two model-facing tools —
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# identical to cordis.yml's wiring (only the LLM backend differs above): spawn
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# and fork are each reachable via a dsh-tool-subagent bound to it with a distinct
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# toolName (subagent → spawn, subagent_fork → fork).
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- id: subagent
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name: '@deepseek-ai/dsh-subagent'
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- id: subagent-spawn
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name: '@deepseek-ai/dsh-subagent-spawn'
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config:
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providerName: spawn
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- id: subagent-fork
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name: '@deepseek-ai/dsh-subagent-fork'
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config:
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providerName: fork
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- id: tool-subagent
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name: '@deepseek-ai/dsh-tool-subagent'
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config:
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provider: spawn
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toolName: subagent
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- id: tool-subagent-fork
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name: '@deepseek-ai/dsh-tool-subagent'
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config:
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provider: fork
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toolName: subagent_fork
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# The model-facing todo_write tool — identical to cordis.yml's wiring, so a
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# replayed todo_write tool call resolves to a real tool during snapshot replay.
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- id: tool-todo
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name: '@deepseek-ai/dsh-tool-todo'
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