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.
npx skills add itallstartedwithaidea/agent-skills --skill long-horizon-workflowsgit clone --depth 1 https://github.com/itallstartedwithaidea/agent-skillsWrote 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.
[](https://agentmods.dev/skills/itallstartedwithaidea/agent-skills/long-horizon-workflows)<a href="https://agentmods.dev/skills/itallstartedwithaidea/agent-skills/long-horizon-workflows"><img src="https://agentmods.dev/badge/skills/itallstartedwithaidea/agent-skills/long-horizon-workflows/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.
<a href="https://agentmods.dev/skills/itallstartedwithaidea/agent-skills/long-horizon-workflows"><img src="https://agentmods.dev/badge/skills/itallstartedwithaidea/agent-skills/long-horizon-workflows.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00034 | $0.02583 |
| Opus 5 | $0.00017 | $0.01291 |
| Sonnet 5 | $0.00007 | $0.00517 |
| Haiku 4.5 | $0.00003 | $0.00258 |
Grade A, and why
long-horizon-workflows 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 9d 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.
How it starts
The opening of the file, as written. The whole thing — 251 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Long-Horizon Workflows
Part of Agent Skills™ by googleadsagent.ai™
Description
Long-Horizon Workflows enable AI agents to execute multi-hour, multi-phase autonomous pipelines that far exceed the scope of a single conversation turn. Inspired by the DeerFlow architecture and production patterns from googleadsagent.ai™, these workflows decompose complex objectives into staged execution plans with checkpoints, progress tracking, error recovery, and human-in-the-loop gates at critical decision points. A long-horizon workflow might analyze an entire Google Ads account (dozens of campaigns, thousands of keywords), generate a comprehensive optimization report, and prepare implementation-ready change sets — all autonomously over several hours.
The core challenge of long-horizon execution is reliability. A workflow that takes 3 hours but fails at hour 2.5 with no recovery is worse than useless — it wastes time and compute. Checkpoint management ensures that work completed before a failure is preserved and can be resumed. Progress tracking provides visibility into what the agent is doing and how far along it is. Human-in-the-loop gates allow a human to validate critical decisions (like budget changes) before the agent proceeds, preventing catastrophic errors in unattended operation.
DeerFlow's contribution to this pattern is the concept of hierarchical task decomposition — a planner agent breaks the objective into phases, each phase into tasks, and each task into atomic operations. Each level of the hierarchy has its own checkpoint, timeout, and error handling policy. This creates a robust execution model that can survive individual task failures without losing the broader workflow state.
Use When
- The objective requires more computation than fits in a single agent conversation
- Multi-campaign or multi-account analysis needs to run unattended
- The workflow has natural phases with different tool and data requirements
- Critical decisions require human approval before proceeding
- Long-running workflows must survive interruptions and resume cleanly
- You need audit trails showing what the agent did, decided, and produced at each stage
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.
- 9d ago First seen · 251 lines · 34 tokens per session scan A ddaa5d78a135
long-horizon-workflows is a skill published in the GitHub repository itallstartedwithaidea/agent-skills (37 stars, last pushed 4mo ago), licensed MIT. It adds 34 tokens to every session and 2,583 once invoked, about $0.0002 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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