What is homerail-dag-patterns?

A library and selection tool for HomeRail DAG patterns. These patterns are reusable workflow designs, such as issue diagnosis, planner-and-worker tasks, or periodic triage, rather than complete workflows themselves.

Use it to compare workflow designs, inspect their requirements, instantiate one with task-specific settings, and start and track the resulting workflow.

Choosing the right structure for a multi-step workflow can be difficult. It lets an agent inspect available patterns, supply parameters, create a workflow, and validate the choice before running it.

homerail-dag-patterns — Skill for Codex by xiaotianfotos

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent

npx skills add xiaotianfotos/homerail --skill homerail-dag-patterns

Clone the repo

git clone --depth 1 https://github.com/xiaotianfotos/homerail

Made for: Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

README.md

[![agentmods](https://agentmods.dev/badge/skills/xiaotianfotos/homerail/homerail-dag-patterns/github.svg)](https://agentmods.dev/skills/xiaotianfotos/homerail/homerail-dag-patterns)

Your own site

<a href="https://agentmods.dev/skills/xiaotianfotos/homerail/homerail-dag-patterns"><img src="https://agentmods.dev/badge/skills/xiaotianfotos/homerail/homerail-dag-patterns/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

Your own site · 80×15

<a href="https://agentmods.dev/skills/xiaotianfotos/homerail/homerail-dag-patterns"><img src="https://agentmods.dev/badge/skills/xiaotianfotos/homerail/homerail-dag-patterns.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 148 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 2,610 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits

NVIDIA SkillSpector warn 7 Sept 2026

SkillSpector: 1 finding, up to medium

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

medium Excessive Agency · line 175
Skill grants unrestricted tool access without appropriate constraints. An agent with unfettered tool access can perform arbitrary actions including file modification, network requests, and code execution.

Fix: Restrict tool access to only the tools required for the skill's stated purpose. Use an explicit allowlist rather than granting blanket access.

How audits are shown

Origin original No closer match found in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5.1	$0.00148	$0.02610
Opus 5	$0.00074	$0.01305
Sonnet 5	$0.00030	$0.00522
Haiku 4.5	$0.00015	$0.00261

Measured 12d ago against content hash af22f65e81ed, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

homerail-dag-patterns scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/homerail-dag-patterns/SKILL.md · 236 lines

How it starts

The opening of the file, as written. The whole thing — 236 lines — stays where its author put it; the contents beside it link to each section on GitHub.

HomeRail DAG Patterns

Treat a pattern as an abstract control-flow invariant, not a finished task template. Read the live Manager catalog before choosing one; it is the source of truth for parameters and generated topology.

Manager Agent Native Path

When running as the HomeRail Manager Agent, dedicated Manager Tools are the shortest path for the operations below. The harness-native shell and hr --json remain valid when they expose a needed command that has no dedicated Tool:

Call list_dag_patterns and compare typical_uses, avoid_when, required primitives, and parameters.
Call get_dag_pattern for the best candidate before changing Manager state.
Call instantiate_dag_pattern with a stable workflow_id, task-specific name, and typed parameters. Keep its default sync=true only after the selection is justified.
Call create_and_run with the returned workflow_id, an available runtime profile when needed, and a prompt containing task inputs and boundaries.
Use get_run_status for progress. Never claim the DAG started without the real run id returned by create_and_run.
Use list_dag_approvals to find durable human-decision nodes. Never call the approval decision API yourself; present the proposal hash to the human.
Use list_dag_triggers and fire_dag_event for Manager-owned schedules and idempotent event delivery. Always supply a stable idempotency key.
Use get_dag_state and set_dag_state for versioned trust, budget, and goal ledgers. Preserve compare-and-set versions instead of overwriting blindly.

When adapting a pattern or authoring a custom workflow instead of using the generated instance unchanged:

Call get_dag_schema; do not rely on a remembered WorkflowSpec shape.
Emit api_version: homerail.ai/v1 with explicit top-level edges and terminal nodes.
Call validate_dag_workflow and repair every structured diagnostic.
Call sync_dag_workflow only after validation returns valid: true.
Use the returned workflow revision and canonical hash as provenance.

Read the full file on GitHub · 236 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

agents/openai.yaml 236 B

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

12d ago First seen · 236 lines · 148 tokens per session scan A af22f65e81ed

Subscribe to this mod's changes

homerail-dag-patterns is a skill published in the GitHub repository xiaotianfotos/homerail (951 stars, last pushed today), licensed MIT. It adds 148 tokens to every session and 2,610 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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