homerail-dag-ops

name

homerail-dag-ops

description

Run, monitor, inspect, debug, and author HomeRail DAG workflows through the CLI or Manager Tools, including supervised multi-Actor runs with stable live Surfaces and later per-Actor follow-ups. Use when: (1) listing templates, (2) starting a DAG run, (3) supervising or watching a run, (4) inspecting chats, handoffs, scorecard, eval-run, or replay output, (5) creating multiple parallel UI panels that update while Actors work, (6) sending commands to waiting Actors, (7) injecting instructions into running nodes, or (8) creating custom DAG templates. For installing or starting Manager/Node/Worker services, use homerail-install-ops first.

HomeRail DAG Operations (TypeScript Backend)

Before acting, apply homerail-shared rules. This skill assumes the local runtime is already ready. If not, run hr start or use homerail-install-ops.

When this Skill is loaded inside the HomeRail Manager Agent, use the harness's native shell and the HomeRail CLI whenever they provide the clearest or most complete path. Prefer --json so command results remain structured. Dedicated Manager Tools such as list_orchestrations, create_and_run, invoke_run, and get_run_status are equivalent shorter paths for operations they already cover; they are not the only valid execution route. For abstract pattern selection, load homerail-dag-patterns and use either its Manager Tools or the corresponding hr patterns commands before running the instantiated workflow.

Supervised multi-Actor live panels

Use the concrete assets/orchestrations/multi-actor-live-report.yaml.template Workflow only when the user wants three stable, independently grounded panels for evidence research, skeptical analysis, and a screenshot-ready publication draft. It explicitly declares digest-pinned Surface views, report_surface_state, a three-Actor join, and await_command, so all three Actors remain addressable after their first round.

Choose exactly three Actors when each panel remains useful by itself, each has a non-overlapping responsibility, and the user benefits from later per-panel follow-ups. Share one source identity and evidence boundary. Parallelize only independent work. When analysis or publication must consume research output, author explicit data edges or structured handoffs instead of using the bundled parallel template unchanged. Use one Manager-owned Block for a simple answer or single report, and never add Actors merely to make the task look sophisticated.

Inside the Manager Agent, start it with start_supervised_dag and the exact yamlPath; pass the user's full objective as prompt and an available runtime profile only when one is required. Do not substitute the abstract orchestrator-workers pattern: that pattern performs dynamic fan-out and verification but intentionally has no live Surface or follow-up contract.

Read references/multi-actor-surfaces.md before authoring another presentation-aware Workflow or handling a follow-up that changes one or more Actor panels.

Quick Path (runtime already ready)

When the install is already done and you only need to run a DAG, follow this shortest path. Do not rebuild or re-install packages for a plain DAG run.

hr doctor                                              # sole readiness check
hr templates list                                      # pick a template
hr run <template> --prompt "<prompt per contract below>"
hr dag watch <run_id> --interval 10 --timeout 600      # wait for terminal state
hr dag handoffs <run_id> --content-limit 0             # authoritative output

Use hr run <template> --profile <profile> only for profiles embedded in that template, such as offline-deterministic. For DB-backed runtime profiles, sync the DAG/profile first and run by --workflow; otherwise an agent can pick a profile id that the Manager cannot resolve.

Judge success only from terminal status + non-empty handoffs (see "Judging Whether Output Is Useful" below). Re-build/install only after changing source, not before every run.

DAG Best Practices

One principle: establish shared facts first, split focused checks next, then judge only from evidence.

Do

• Start with one source of truth. Use a seed node or explicit prompt contract to provide code version, input parameters, external URLs, and evidence paths to every downstream node.
• Give each node one clear question: source cleanliness, build result, runtime health, data preservation, regression result, or advisory review.
• Make nodes inspect reality. If a task requires a command, file, API, or clone, the node should execute or read it rather than restating the prompt.
• Require structured handoff. Each useful node output should include conclusion, evidence, gaps, blockers, and a concrete next step when blocked.
• Keep fan-in nodes as judges. They summarize and decide from upstream handoffs and evidence; they should not silently redo or invent missing checks.
• Separate hard gates from advisory review. A hard gate failure blocks the run; advisory output can highlight risk but cannot replace evidence.
• Treat environment differences as normal. Host, container, local, remote, and CI may have different paths, network reachability, credentials, and tools.
• Keep disruptive host actions outside the DAG unless explicitly isolated. Deploying, migrating, and restarting services can invalidate the runtime executing the DAG.
• Record baselines before upgrade or migration checks. User data, settings, projects, sessions, secrets, and model providers need before/after evidence.
• Preserve evidence so the result is auditable: what was checked, what command ran, what returned, and why the run passed or blocked.
• Use smoke runs to test wiring and real model-backed tasks to test capability.
• Use URLs, paths, and credentials that the executing node can actually access. Validate from the container's point of view when the worker runs in Docker.

Do Not

• Do not let nodes use different versions or inputs. If facts diverge, fan-in conclusions are not meaningful.
• Do not use vague natural language as the node-to-node protocol. Pass structured fields instead of relying on chat interpretation.
• Do not overload one node with unrelated responsibilities; mixed failures are hard to diagnose.
• Do not count a node with no handoff as success. Missing output is missing evidence.
• Do not auto-fill handoffs and then call the run clean. Auto-fill only proves the node failed its output contract.
• Do not let fan-in guess through missing evidence. Block explicitly when a hard gate lacks proof.
• Do not treat a quick smoke as complete validation. Smoke proves the path can run, not that the requested work is good.
• Do not assume host access equals container access. Local paths, SSH keys, known_hosts, Docker sockets, and private remotes often differ.
• Do not save only the final verdict. Without process evidence, the verdict is not reviewable.
• Do not hide uncertainty. Unchecked, unreachable, permission denied, and insufficient-evidence states should appear in the result.
• Do not hardcode tokens, private hosts, personal paths, or temporary staging directories in reusable templates. Pass them through runtime parameters or environment configuration.

Quick Checklist

• Is there one shared source of truth?
• Does each node answer one focused question?
• Does every required node produce structured handoff?
• Does fan-in decide only from handoffs and evidence?
• Are hard gates and advisory checks separated?
• Are smoke and real validation separated?
• Were network, path, and permission assumptions checked from the executing node's environment?
• If the run fails, is the next step obvious?

Running a Simple DAG

Check runtime readiness:

hr doctor

List templates and start a run:

hr templates list
hr run assets/orchestrations/public-two-node.yaml.template \
  --prompt "Draft a short release checklist"

For reusable DAGs, sync the asset into the Manager database first. Treat workflow_id as the stable identity: AI edits may change prompts, nodes, and edges, but must not change workflow_id unless intentionally creating a new workflow/version.

hr dag sync assets/orchestrations/public-dev-5node.yaml.template
hr profile sync assets/profiles/example-runtime.profile.yaml.template \
  --workflow public-dev-5node-template
hr run \
  --workflow public-dev-5node-template \
  --profile example-runtime \
  --prompt "Draft a short release checklist"

Profile YAML references DB model_alias or llm_setting_id; it must not write provider/model/key/base_url as executable runtime configuration.

For read-only deployment diagnosis with a configured DB LLM setting, use the single-node template:

hr run assets/orchestrations/local-harness-cli-deploy-diagnosis.yaml.template \
  --prompt "source_repo_url=<https git url> branch=main"

This template must run through the normal Node-provisioned Worker path. Do not use a fixed host Worker, Docker socket mounts, or Docker-in-Docker. The template's deployment proof uses the run's isolated workspace, clones the source, builds it, configures the cloned CLI to talk to the outer Manager, and then verifies runtime status and doctor. This template uses advisory scorecard enforcement: scorecard findings are useful review evidence, but deployment success is determined by terminal DAG state plus the structured handoff and CLI evidence.

Template Prompt Contracts (read before hr run)

A DAG Worker runs inside an isolated Docker workspace. It cannot see your host filesystem — no /Users/..., no /Volumes/..., no host repo checkout. So the --prompt must carry everything a node needs to reach its inputs:

• Repo-access templates (any template whose agent system prompt says "git clone/fetch the repo", "inspect the PR", "review the diff", etc., including local-harness-cli-deploy-diagnosis and any PR-review family templates) require a cloneable git URL in the prompt: --prompt "source_repo_url=<https or ssh git url> branch=<branch>". A host-local path like /Volumes/.../HomeRail will fail inside the container — every reviewer node will hand off WARN: repository not accessible and the run produces no review. Use the Gitea/GitHub remote URL, not a local path.
• Self-contained templates (e.g. public-two-node, public-dev-5node) only need the task text in --prompt; they do not clone anything.

If unsure whether a template needs a repo URL, grep the template YAML for clone|repo|PR|git in agent system: blocks. If present, supply source_repo_url=. A correct prompt avoids a 2-minute run that ends in all-WARN handoffs.

Copy the returned run_id, then supervise:

hr dag supervise <run_id>
hr dag watch <run_id> --interval 5 --timeout 600

Agent Monitoring Ladder

Use this order before claiming a run result:

hr dag quick <run_id> --events 30
hr dag chats <run_id> --tools 10
hr dag chats <run_id> --tools 10 --raw-tools
hr dag handoffs <run_id>
hr scorecard <run_id>
hr eval-run <run_id>

If quick looks stale or ambiguous, use chats before concluding that no progress happened. If a command shows idle but a run_id exists, do not equate idle with completion; verify terminal state and handoffs. Use dag chats --raw-tools when auditing whether a DAG agent actually used the HomeRail CLI, avoided Write/Edit/MultiEdit, or bypassed the CLI with direct API calls. The raw tool view is redacted and should be used together with handoffs, scorecard, and eval-run before claiming a CLI-first run is clean. Only inspect Worker raw audit files locally for deep debugging. New Worker tool audit files are run-scoped at ${HOMERAIL_HOME}/audit/tool-events/<run_id>.jsonl; legacy installs may still contain a global ${HOMERAIL_HOME}/audit/tool-events.jsonl. If scorecard reports advisory findings, separate policy/prompt issues from actual DAG failure before deciding whether to rerun or patch.

Judging Whether Output Is Useful

A completed run with a passing scorecard only proves the pipeline worked, not that the work product is useful. Before reporting results, read the handoff content (hr dag handoffs <run_id> --content-limit 0) and check for these hollow-output signals — if present, the run succeeded mechanically but produced no real result, and the fix is the prompt/inputs, not the code:

• Any handoff verdict of WARN/FAIL citing not accessible, inspection incomplete, repository ... does not exist, or cannot clone.
• Handoff content shorter than the task expects, or a review/summary that describes what it couldn't do rather than what it found.
• A reviewer falling back to inspecting /app (the Worker container itself) instead of the intended target — this means it never reached its inputs.

A run in this state means the template prompt contract was not met (most often a missing source_repo_url=). Re-run with the correct prompt; do not treat the hollow output as a code defect.

To correct a running node, prefer inbox injection:

hr inject <run_id> <node_id> "Correction or additional instruction" --mode inbox

Use replay after terminal failure:

hr replay <run_id>

Public Assets Boundary

The following assets are part of the public interface:

skills/
  homerail-shared/       # Common service, security, provider, and local-source rules
  homerail-install-ops/  # Local-source install, service startup, maintenance, smoke
  homerail-cli/          # CLI command reference
  homerail-dag-ops/      # DAG run, monitor, inspect, and author operations
  homerail-dag-patterns/ # DAG pattern selection, composition, and instantiation

assets/
  orchestrations/    # Public example DAG template YAML files
    public-*.yaml.template
    local-harness-cli-deploy-diagnosis.yaml.template
  profiles/          # Public example runtime profile YAML templates

Rules for public DAG templates and agents:

• Use only public component names: homerail-manager, homerail-node, homerail_worker.
• Do not assume assets/agents, assets/prompts, or assets/skills exists. Current public templates keep prompts inline or in checked template/profile assets.
• Do not treat every local assets/orchestrations/*.yaml.template as public. Internal release gates, staging-only templates, and private checks must stay out of public export and reusable user instructions.
• Use the CLI config flow for Manager address and credentials. --base-url and HOMERAIL_MANAGER_URL remain per-command overrides; HOMERAIL_MANAGER_PORT controls the local-service default port.
• Do not reference internal-only paths, internal hostnames, or legacy runtimes.
• The CLI default base URL is http://localhost:19191 (TS Manager port).

Self-Dev Architecture Boundary

When a DAG is used to change HomeRail itself, the first implementation handoff must classify the correct product layer before editing code:

Layer Decision:
- Problem layer:
- Preferred fix layer:
- Manager touched: yes/no
- Manager Change Justification: <allowed category or n/a>
- Rejected shortcuts:
- Public entry path:
- Validation path:

Allowed Manager categories are limited to:

• run/node/edge/handoff/event lifecycle management.
• Node/Worker registration, scheduling, provisioning, cleanup, and runtime status.
• Persisted run evidence needed by chats, handoffs, usage, scorecard, or eval.
• Public provider, asset catalog, settings, and validation surfaces.
• Stable inspection/mutation APIs used by CLI or Agent UI.

If no allowed category applies, return DESIGN_BLOCKED and describe the missing CLI, skill, DAG contract, install path, or documentation work. Do not add Manager routes, fake state, deterministic runs, empty successful defaults, compatibility aliases, embedded DAG YAML, hardcoded issue IDs, or private shortcuts only to make a smoke, UI, or scorecard pass.

Before accepting a self-dev DAG result, run:

npm run ci

For intentionally broad Manager changes, set HOMERAIL_BOUNDARY_SCAN_STRICT=1 and provide HOMERAIL_MANAGER_CHANGE_JUSTIFICATION during local review so the justification is explicit.

Creating a Custom DAG Template

Create a new YAML file in assets/orchestrations/:

name: my-linear-pipeline
description: "A simple three-step linear pipeline"

agents:
  researcher:
    system: |
      Research the given topic.
      Call handoff(port="done", content=findings).

  writer:
    system: |
      Write a document based on the research.
      Call handoff(port="done", content=document).

  reviewer:
    system: |
      Review the document for quality.
      Call handoff(port="approved", content=review) if good.
      Call handoff(port="needs-work", content=feedback) otherwise.

nodes:
  research:
    name: "Research"
    agent: researcher
    after: []
    outputs:
      done:
        to: "write.in:data"

  write:
    name: "Write"
    agent: writer
    after: [research]
    outputs:
      done:
        to: "review.in:document"

  review:
    name: "Review"
    agent: reviewer
    after: [write]
    outputs:
      approved:
        to: ""
      needs-work:
        to: "write.in:feedback"

Supported public backend names include claude-sdk, kimi-code, kimi_code, codex_appserver, and deterministic. Do not use direct-llm or Chat Completions for Coding Plan / Agent Plan accounts. Kimi should use the Kimi Code CLI harness (kimi-code); other Coding Plan providers should use the Claude Code compatible harness (claude-sdk) with an Anthropic-compatible endpoint. Prefer hyphenated names in new YAML (kimi-code) unless you are preserving an older template.

Restart the Manager if templates are loaded at startup.

Run the new template:

hr run assets/orchestrations/my-linear-pipeline.yaml --prompt "Research TypeScript best practices"
hr dag supervise <run_id>