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 j4flmao/agent-skills --skill agentic-workflowsgit clone --depth 1 https://github.com/j4flmao/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/j4flmao/agent-skills/agentic-workflows)<a href="https://agentmods.dev/skills/j4flmao/agent-skills/agentic-workflows"><img src="https://agentmods.dev/badge/skills/j4flmao/agent-skills/agentic-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/j4flmao/agent-skills/agentic-workflows"><img src="https://agentmods.dev/badge/skills/j4flmao/agent-skills/agentic-workflows.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00000 | $0.00654 |
| Opus 5.5 | $0.00000 | $0.00262 |
| Sonnet 5.5 | $0.00000 | $0.00131 |
| Haiku 4.5 | $0.00000 | $0.00065 |
Grade A, and why
agentic-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 1mo 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 — 35 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agentic Workflows & Multi-Agent Orchestration
1. Skill Context
Focus: Designing autonomous AI agents capable of reasoning, planning, executing tools, and correcting their own mistakes over long-running tasks. Triggers: ai-agents, agentic-workflows, react, langgraph, autogen, multi-agent, planning.
2. The Evolution of Prompting
Standard LLM interactions rely on Zero-Shot or Few-Shot prompting, where the model generates a final answer immediately. Agentic Workflows wrap the LLM in a control loop (a state machine) that allows it to interact with the external world (via APIs, code execution, or databases) before returning an answer.
3. Core Agent Architectures
A. ReAct (Reason + Act)
The foundational agentic loop. The agent iterates through a strict cycle:
- Thought: The LLM reasons about what to do next based on the user prompt and current state.
- Action: The LLM requests to call a specific Tool (e.g.,
search_web,read_file). - Observation: The system executes the tool and feeds the raw result back to the LLM. (The loop repeats until the LLM's "Thought" decides the final answer is reached).
B. Plan-and-Solve (Planner-Executor)
ReAct struggles with massive, multi-step goals because the LLM loses focus or gets stuck in rabbit holes. Plan-and-Solve splits the brain:
- Planner Agent: Looks at the user request and generates a rigid Markdown checklist of steps. (It does not execute tools).
- Executor Agent(s): Takes one step from the checklist, executes it using ReAct, and returns the result.
- Benefit: The Planner maintains the high-level context, ensuring the system doesn't drift.
C. Multi-Agent Orchestration (LangGraph / AutoGen)
Complex enterprise tasks require multiple specialized agents working together.
- Supervisor Pattern: A routing agent (Supervisor) receives the task, decides which sub-agent is best suited (e.g., the
Database_Agentor theFrontend_Agent), routes the request, evaluates the response, and then routes to the next agent. - Hierarchical Teams: Structuring agents like a human company. A
Tech_Lead_Agentreviews the code produced by theCoder_Agent. If the code fails tests written by theQA_Agent, theTech_Lead_Agentsends it back to theCoder_Agentwith feedback.
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.
- 1mo ago Changed · -4 lines · -17 tokens per session dcb621a88e93
- 1mo ago First seen · 39 lines · 17 tokens per session scan A dfa11abf1177
agentic-workflows is a skill published in the GitHub repository j4flmao/agent-skills (34 stars, last pushed 11d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 654 tokens. 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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