98 lines
4.0 KiB
YAML
98 lines
4.0 KiB
YAML
# The acp-agent plugin tree: the ACP server. Also the snapshot RECORD config
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# (the dsh-acp-agent bin selects it for DSH_SNAPSHOT=record): a real llm-deepseek
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# run whose persisted log the snapshot harness harvests. The swappable DeepSeek
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# adapter and local bash executor, the ACP server app (@deepseek-ai/dsh-acp-agent),
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# and the optional model-facing subagent/todo tools loaded below.
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#
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# CRITICAL: this tree loads NO stdout logger and NO hmr — stdout is reserved for
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# the ACP JSON-RPC protocol (see packages/ui/acp). That guarantee is now a
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# property of @deepseek-ai/dsh-acp-agent (it contains no logger entry), not a
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# leaf convention: there is no logger here to get wrong.
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#
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# Requires DEEPSEEK_API_KEY (and optionally DEEPSEEK_BASE_URL) — the
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# dsh-acp-agent bin loads the gitignored repo-root .env first (on STDERR only).
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# The DeepSeek adapter.
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- id: llm-deepseek
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name: '@deepseek-ai/dsh-llm-deepseek'
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config:
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apiKey: !!js process.env.DEEPSEEK_API_KEY
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baseURL: !!js process.env.DEEPSEEK_BASE_URL
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models:
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- deepseek-v4-flash
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- deepseek-v4-pro
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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: the agent-core spine + JSONL persistence + the ACP bridge.
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# Persistence root: $DSH_SNAPSHOT_SESSIONS_ROOT when the snapshot harness sets it
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# (so it can harvest / isolate the log), else ./.sessions for the demo.
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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, as leaf
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# entries after the app (which provides ctx.agents/ctx.tools). spawn (a fresh
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# child) and fork (a child seeded with the parent's completed-turn prefix) are
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# both reachable by the model: dsh-tool-subagent is loaded once per backend with
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# a distinct toolName (subagent → spawn, subagent_fork → fork), so a multi-child
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# scenario can exercise both transports.
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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: whole-list task tracking written to the
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# session log (todo/write), surfaced to the ACP client as a `plan` update.
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- id: tool-todo
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name: '@deepseek-ai/dsh-tool-todo'
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