09c8e549b0
Address review on the bridges: - Hook cwd (blocking): the bridges never passed a workdir to runHook, so hooks ran in the executor default (the ACP server launch dir), not the session cwd — a hook doing `pwd`/relative reads/marker writes operated in the wrong tree. Both bridges now thread the agent's session `header.cwd` (the session/new.cwd) as the hook workdir for agent-scoped points. Regression per bridge: server cwd ≠ session cwd, a `pwd` hook proves it ran in the session workspace (proven red without the workdir). - Example config honesty (blocking): `configPath: ./hooks.json` is read ONCE at load against the PROCESS cwd, not per-session — the comment/README now say so explicitly (a project-local per-session hooks.json is not discovered; TODO(per-session-hook-config)). The hooks-run-in-session-cwd fix above is the distinct, separately-documented half. - Session-start timing (blocking): agent/session-start is a synchronous emit and the hook runs on a detached .then, so injected context is BEST-EFFORT — not guaranteed before the first request. Downgrade the contract in code comments + README + RFC (TODO(session-start-gating)) rather than implying "first request sees it", and add a no-wait regression that asserts the safe properties without pre-waiting for the inject. - systemMessage (non-blocking): the merge collects merged.systemMessages but no bridge surfaced it. Warn per hook (like updatedInput) and document it as deferred in both READMEs + the RFC; tests assert the warn + non-surfacing.
101 lines
4.3 KiB
YAML
101 lines
4.3 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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# The Claude Code hook bridge. `configPath` is read ONCE at load and resolves
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# `./hooks.json` against the PROCESS cwd (not per-session) — in these snapshot
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# runs the harness launches the subprocess with process cwd = the scenario's temp
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# workspace, so a scenario that ships `workspace/hooks.json` (copied into that cwd
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# before the run) exercises the hooks path end-to-end; every other scenario has no
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# such file, so the parse fails-soft and the bridge registers nothing (a silent
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# no-op — the ACP app loads no logger exporter, so the warning never reaches
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# stdout). Hooks themselves run in the session cwd (the bridge passes it as workdir).
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- id: hooks-claude
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name: '@deepseek-ai/dsh-hooks-claude'
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config:
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configPath: ./hooks.json
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