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 humaisali/Awesome-AI-Skills --skill agent-workflow-designergit clone --depth 1 https://github.com/humaisali/Awesome-AI-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/humaisali/awesome-ai-skills/agent-workflow-designer)<a href="https://agentmods.dev/skills/humaisali/awesome-ai-skills/agent-workflow-designer"><img src="https://agentmods.dev/badge/skills/humaisali/awesome-ai-skills/agent-workflow-designer/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/humaisali/awesome-ai-skills/agent-workflow-designer"><img src="https://agentmods.dev/badge/skills/humaisali/awesome-ai-skills/agent-workflow-designer.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 | $0.00073 | $0.00544 |
| Opus 5 | $0.00036 | $0.00272 |
| Sonnet 5 | $0.00015 | $0.00109 |
| Haiku 4.5 | $0.00007 | $0.00054 |
Grade A, and why
agent-workflow-designer 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.
This is a copy
100% identical to agent-workflow-designer — 2 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Workflow Designer
Tier: POWERFUL
Category: Engineering
Domain: Multi-Agent Systems / AI Orchestration
Overview
Design production-grade multi-agent workflows with clear pattern choice, handoff contracts, failure handling, and cost/context controls.
Core Capabilities
- Workflow pattern selection for multi-step agent systems
- Skeleton config generation for fast workflow bootstrapping
- Context and cost discipline across long-running flows
- Error recovery and retry strategy scaffolding
- Documentation pointers for operational pattern tradeoffs
When to Use
- A single prompt is insufficient for task complexity
- You need specialist agents with explicit boundaries
- You want deterministic workflow structure before implementation
- You need validation loops for quality or safety gates
Quick Start
# Generate a sequential workflow skeleton
python3 scripts/workflow_scaffolder.py sequential --name content-pipeline
# Generate an orchestrator workflow and save it
python3 scripts/workflow_scaffolder.py orchestrator --name incident-triage --output workflows/incident-triage.json
Pattern Map
sequential: strict step-by-step dependency chainparallel: fan-out/fan-in for independent subtasksrouter: dispatch by intent/type with fallbackorchestrator: planner coordinates specialists with dependenciesevaluator: generator + quality gate loop
Detailed templates: references/workflow-patterns.md
Recommended Workflow
- Select pattern based on dependency shape and risk profile.
- Scaffold config via
scripts/workflow_scaffolder.py. - Define handoff contract fields for every edge.
- Add retry/timeouts and output validation gates.
- Dry-run with small context budgets before scaling.
Common Pitfalls
- Over-orchestrating tasks solvable by one well-structured prompt
- Missing timeout/retry policies for external-model calls
- Passing full upstream context instead of targeted artifacts
- Ignoring per-step cost accumulation
What ships with it
2 files 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.
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 · 84 lines · 73 tokens per session scan A f1133218f4bc
agent-workflow-designer is a skill published in the GitHub repository humaisali/Awesome-AI-Skills (5 stars, last pushed 1mo ago), licensed MIT. It adds 73 tokens to every session and 544 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to agent-workflow-designer, differing in 2 lines, and is treated as a copy.
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