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 personamanagmentlayer/pcl --skill agent-engineering-expertgit clone --depth 1 https://github.com/personamanagmentlayer/pclWrote 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/personamanagmentlayer/pcl/agent-engineering-expert)<a href="https://agentmods.dev/skills/personamanagmentlayer/pcl/agent-engineering-expert"><img src="https://agentmods.dev/badge/skills/personamanagmentlayer/pcl/agent-engineering-expert.svg" alt="Measured on agentmods" 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 | $0.00094 | $0.02545 |
| Opus 5 | $0.00047 | $0.01273 |
| Sonnet 5 | $0.00019 | $0.00509 |
| Haiku 4.5 | $0.00009 | $0.00254 |
Grade A, and why
agent-engineering-expert scanned grade A with 1 finding 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 2d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
return orders.fetch(order_id) # authorisation enforced in the repository How it starts
The opening of the file, as written. The whole thing — 292 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Engineering Expert
An agent is a loop in which a model chooses tools until a goal is met. The difficulty is not the loop — it is that the model's choices are driven by text that may be attacker-controlled, and the tools have real effects.
Core Concepts
The Loop
observe → decide → act → observe …
Concretely: assemble context, call the model with tool definitions, execute the requested tool, append the result, repeat until the model stops or a limit is hit. Every element of that loop needs a bound — iterations, wall time, tokens and cost.
Capability Is the Design
What an agent can do is defined entirely by the tools it holds. Reliability and safety come from designing that surface, not from instructing the model to be careful. A tool the agent does not have is a failure mode that cannot occur.
The Model Is Untrusted Input
Anything the agent reads — a web page, a document, a tool result, an email — may contain instructions aimed at the model. Treat every tool call as a request from an untrusted party and authorise it independently. This is the defining security property of agents, and no amount of prompting removes it.
Autonomy Is a Dial
More steps means more compounding error. A workflow with fixed stages and a model at each decision point is usually more reliable, cheaper and easier to debug than an open-ended agent. Choose the least autonomy that solves the problem.
Designing Tools
The tool description is a prompt. It is the only thing telling the model when to use it.
{
"name": "search_orders",
"description": (
"Search a customer's orders by status and date range. "
"Use when the user asks about their order history or the state of an order. "
"Returns at most 50 orders, newest first. "
"Does not return payment card details. "
"Use `get_order` instead when you already have an order id."
),
"input_schema": {
"type": "object",
"properties": {
"status": {"type": "string", "enum": ["pending", "shipped", "cancelled"]},
"since": {"type": "string", "format": "date"},
"limit": {"type": "integer", "minimum": 1, "maximum": 50, "default": 20},
},
"required": ["status"],
},
}
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.
- 2d ago First seen · 292 lines · 94 tokens per session scan A 496dec0cc175
agent-engineering-expert is a skill published in the GitHub repository personamanagmentlayer/pcl (40 stars, last pushed today), licensed Apache-2.0. It adds 94 tokens to every session and 2,545 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-05.
Other skills, from other repositories
crewai-multi-agent
Multi-agent orchestration framework for autonomous AI collaboration. Use when building teams of specialized agents working together on complex tasks, when you need role-based agent collaboration with memory, or for production workflows requiring sequential/hierarchical execution. Built without LangChain dependencies…
swarmclaw
AI agent runtime and multi-agent orchestration platform. Teaches agents how to use SwarmClaw's 6 primitive tools, persistent memory, dreaming, delegation, connectors, credentials, and the skill system. Use when an agent is running on SwarmClaw and needs to understand the platform's capabilities.
crewai-multi-agent
Multi-agent orchestration framework for autonomous AI collaboration. Use when building teams of specialized agents working together on complex tasks, when you need role-based agent collaboration with memory, or for production workflows requiring sequential/hierarchical execution. Built without LangChain dependencies…
orchestration
Multi-agent orchestration authority — agent memory protocols, task delegation, SOUL/AGENTS patterns, session startup, inter-agent communication, OpenClaw workspace coordination, and agent lifecycle management.
agent-orchestrator
Meta-skill que orquestra todos os agentes do ecossistema. Scan automatico de skills, match por capacidades, coordenacao de workflows multi-skill e registry management.
agent-framework-py-release
Use when cutting a Python release for the microsoft/agent-framework monorepo. Triggers on "bump py versions", "cut a python release", "prepare release PR for python", "release py packages", "bump python to X.Y.Z", or similar requests to bump Python package versions and prepare a release PR. Handles all four lifecycle…