deepseek-harness/apps/cli/config/agent-presets/code/agent.cordis.yml

# The `code` agent preset: the standard coding agent, presented as Code Mode.
#
# Everything in `standard` is here unchanged. What is added is the `tool-presentation`
# row: instead of one tool call per action, the model writes a TypeScript
# program against a generated SDK and `run_code` executes it, so a sequence
# that would be five round trips becomes one.
#
# The registry itself stays on the host plane — the agent loop's scheduler and
# the API proxy's presenters are its consumers — so what this preset owns is
# the PRESENTATION of that registry for this agent alone. Native sessions run
# beside this one in the same process, each seeing its own catalog.
#
# This file is an AGENT-PLANE composition. It is mounted under one agent's
# scope context, so every tool and prompt section it registers belongs to that
# session alone. The host composition (`base.cordis.yml` + `web.cordis.yml`)
# keeps everything a preset must not own: the registries themselves, the
# sandbox and approval stack, persistence, and the model route.
#
# A service row here MUST sit inside a group carrying an `isolate` realm.
# Without one it publishes into the root realm, where it is process-global
# rather than per-session and the second session mounting this preset collides
# with the first; `dsh-agent-presets` rejects that at mount. `true` means an
# entry-local realm — one private instance per mounted session, which is the
# default this deployment wants. A shared label would instead pool one instance
# across every session naming it.

# ── identity ────────────────────────────────────────────────────────────────

# The preset's own persona, shadowing the deployment default for this agent.
# `{{model}}` and `{{cwd}}` resolve from the agent's own route and workspace.
- id: persona
  name: '@deepseek-ai/dsh-persona'
  config:
    text: >-
      You are a coding agent powered by the {{model}} model. Your working directory is {{cwd}}.

- id: agent-instructions
  name: '@deepseek-ai/dsh-agent-instructions'
  config:
    maxBytes: 65536

# ── shell ───────────────────────────────────────────────────────────────────

# `shell-env` stays in the HOST composition: `apps/cli/src/web.ts` injects it to
# publish `DSH_WEB_URL`/`DSH_WEB_MODE`, and a host row that injects a service is
# the criterion for host-plane ownership — injection resolves before any session
# exists, so there is no agent to key by. Behind a preset realm those variables
# never reached the model's shell at all. Both shell tools consume the host
# registry from here; their executors (`bash-sandbox`/`pwsh-sandbox`) are
# host-plane too.
- id: tool-bash
  name: '@deepseek-ai/dsh-tool-bash'
  disabled: !!js process.platform === 'win32'

- id: tool-pwsh
  name: '@deepseek-ai/dsh-tool-pwsh'
  disabled: !!js process.platform !== 'win32'

# ── filesystem ──────────────────────────────────────────────────────────────

# Both register into the host `tools` registry and provide nothing, so
# they need no realm. The `fs` service and its policy stay in the host.
- id: tool-fs
  name: '@deepseek-ai/dsh-tool-fs'

- id: tool-fs-search
  name: '@deepseek-ai/dsh-tool-fs-search'
  config:
    sampleOverCapGlobResults: false

# ── background jobs ────────────────────────────────────────────────────────

# Only the model-facing controls. The task REGISTRY stays on the host plane:
# its producers sit outside any realm this file could put it in — `tool-bash`
# above resolves it with `ctx.get`, and an entry-local realm here is invisible
# to every sibling row, so `run_in_background` would answer "background jobs
# unavailable" while these controls sat in the catalog. The registry is keyed by
# owning agent anyway, so one host instance serves every session. What a preset
# chooses is whether its agent can collect and stop background work at all.
- id: tool-jobs
  name: '@deepseek-ai/dsh-tool-jobs'

# ── skills ──────────────────────────────────────────────────────────────────

# The skill REGISTRY lives in the host composition and is layered per scope:
# these rows register into THIS preset's layer of it, so they need no realm.
# `skill-filesystem` contributes local-root discovery for agents on this preset, and
# `tool-skill` gives them the catalog and loader; the merged catalog also
# carries whatever the deployment registered globally (repository plugins).
- id: skill-filesystem
  name: '@deepseek-ai/dsh-skill-filesystem'

- id: tool-skill
  name: '@deepseek-ai/dsh-tool-skill'

# ── goals ───────────────────────────────────────────────────────────────────

# Only the model-facing tool. The goal SERVICE, its session driver, and the
# `/goal` command stay on the host plane: the Gateway serves the goal domain as
# Remote endpoints whose receiver comes from a generated descriptor, so it
# resolves `goals` on the host and an entry-local realm here would hide it. The
# registry is keyed by session anyway, so one host instance serves every
# session. What a preset chooses is whether its agent can call the goal tool.
- id: tool-goal
  name: '@deepseek-ai/dsh-tool-goal'

# ── plan mode ───────────────────────────────────────────────────────────────

# Plan state is per-agent by nature, so an entry-local realm is not a
# workaround here — it is the correct lifetime.
- id: planning
  name: cordis:group
  group: true
  isolate:
    planMode: true
  config:
    - id: plan-mode
      name: '@deepseek-ai/dsh-plan-mode'
      config:
        section: |
              You are in plan mode. Stay in plan mode until exit_plan_mode succeeds or the user switches the session mode. Imperative language to implement changes means plan the implementation, not execute it. A user's conversational agreement — including an answer confirming something you asked — approves nothing and does not end plan mode; fold the confirmed decision into the plan and submit it through exit_plan_mode.

              Explore first. Use non-mutating reads, searches, static analysis, and checks to ground the plan in the actual repository. Do not edit or write files, change configuration, run formatters or code generation that rewrites tracked files, commit, or otherwise carry out the plan. Prefer existing functions and patterns over new machinery.

              The tool catalog stays the same across modes for request-cache stability. These plan-mode rules override any later tool description or guidance that suggests using mutation tools; those tools remain listed to keep the tool catalog unchanged. Do not use todo_write to track this planning phase: it tracks implementation after an approved plan, while the plan itself belongs in exit_plan_mode.

              Resolve discoverable facts by inspection. Use ask_user_question only for user-owned choices or material ambiguity that inspection cannot answer. Do not ask the user where code lives or how current behavior works when you can find out.

              Make the plan decision-complete: state the goal and success criteria; group implementation changes by subsystem; identify public API, schema, and data-flow changes; cover edge cases, failure modes, tests, acceptance criteria, and explicit assumptions. Keep it concise enough to review but detailed enough that another engineer can implement it without making design decisions.

              When ready, call exit_plan_mode with the complete plan markdown, starting with a # title. Make exit_plan_mode the only and final tool call in that assistant response: it presents the plan for approval, and implementation begins only in a later step after approval. Do not paste the final plan as a plain reply or ask "should I proceed?" through prose or ask_user_question. If review rejects it, incorporate the feedback and present again. If the review channel is unavailable or aborted, stay in plan mode and ask the user to switch modes manually; do not proceed with implementation.

# ── compaction ──────────────────────────────────────────────────────────────

# `compaction-basic` reads `toolResultPrune` through `ctx.get`, so the pruner must
# share this realm rather than sit outside it.
#
# `tokenMeter` is deliberately NOT in this realm: the meter stays on the HOST
# plane, and the rows here resolve that one instance. It takes no configuration,
# keys every fold by Session, and owns the context-meter projection units the
# browser reads for every session — behind a realm those units would come and go
# with whichever presets happen to be mounted. What a preset chooses is whether
# its agent compacts at all, which is `compaction-basic` below.
- id: compaction
  name: cordis:group
  group: true
  isolate:
    compaction: true
    toolResultPruner: true
  config:
    - id: compaction-basic
      name: '@deepseek-ai/dsh-compaction-basic'

    - id: command-compact
      name: '@deepseek-ai/dsh-command-compact'

    - id: tool-result-pruner
      name: '@deepseek-ai/dsh-compaction-tool-result-pruner'
      config:
        thresholdChars: 8192
        headChars: 4096
        tailChars: 1024

# ── delegation and workflows ────────────────────────────────────────────────

# The `subagents` registry and its spawn/fork backends live in the HOST
# composition: the registry is a process singleton whose cross-session queries
# the api-proxy serves to the browser, and a provider name may only be
# registered once. This preset contributes the delegation TOOLS, which resolve
# that host registry.
#
# `workflows` is different — nothing outside an agent reads it — so every row
# that reaches it shares one entry-local realm here, and a consumer left
# outside would resolve a host registry this preset does not populate.
- id: delegation
  name: cordis:group
  group: true
  isolate:
    workflowEngine: true
  config:
    - id: tool-subagent-control
      name: '@deepseek-ai/dsh-tool-subagent-control'

    - id: tool-subagent-list-agents
      name: '@deepseek-ai/dsh-tool-subagent-control/list-agents'

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

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

    # Production dsh does not install these optional providers. An opting-in
    # Profile mounts each provider once on the host plane; copy this preset,
    # then remove `disabled` from the matching tool row.
    - id: tool-subagent-codex
      name: '@deepseek-ai/dsh-tool-subagent'
      disabled: true
      config:
        provider: codex
        toolName: subagent_codex
        backgroundMode: one-shot
        maxDepth: provider-managed

    - id: tool-subagent-claude-code
      name: '@deepseek-ai/dsh-tool-subagent'
      disabled: true
      config:
        provider: claude-code
        toolName: subagent_claude_code
        backgroundMode: one-shot
        maxDepth: provider-managed

    - id: workflow-worker-thread
      name: '@deepseek-ai/dsh-workflow-worker-thread'
      config:
        provider: spawn

    - id: tool-workflow
      name: '@deepseek-ai/dsh-tool-workflow'

    - id: tool-ralph
      name: '@deepseek-ai/dsh-tool-ralph'
      config:
        subagentProvider: spawn
        maxRounds: 64

# ── remaining model-facing rows ─────────────────────────────────────────────

- id: tool-ask-user
  name: '@deepseek-ai/dsh-tool-ask-user'

- id: tool-todo
  name: '@deepseek-ai/dsh-tool-todo'
  config:
    allowParallelInProgress: true

# The `web` service and its search provider stay in the host composition; only
# the model-facing tool is per-session.
- id: tool-web
  name: '@deepseek-ai/dsh-tool-web'
  config:
    fetch: false
    searchTimeoutMs: 60000

# ── presentation ────────────────────────────────────────────────────────────

# Code Mode for this agent alone. The row waits for the host's `codeRuntime`
# rather than assuming it: a deployment that composes no TypeScript runtime
# fails this preset at mount, naming this id, instead of at the first request.
- id: tool-presentation
  name: '@deepseek-ai/dsh-agent-tool-presentation'
  config:
    mode: code