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 Tyler-R-Kendrick/agent-skills --skill adlgit clone --depth 1 https://github.com/Tyler-R-Kendrick/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/tyler-r-kendrick/agent-skills/adl)<a href="https://agentmods.dev/skills/tyler-r-kendrick/agent-skills/adl"><img src="https://agentmods.dev/badge/skills/tyler-r-kendrick/agent-skills/adl/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/tyler-r-kendrick/agent-skills/adl"><img src="https://agentmods.dev/badge/skills/tyler-r-kendrick/agent-skills/adl.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 | $0.00087 | $0.01115 |
| Opus 5 | $0.00044 | $0.00558 |
| Sonnet 5 | $0.00017 | $0.00223 |
| Haiku 4.5 | $0.00009 | $0.00112 |
Grade A, and why
adl 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 11d 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 — 140 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ADL — Agent Definition Language
Overview
ADL is a vendor-neutral, declarative specification for defining AI agents — their identity, capabilities, tools, permissions, and governance metadata. It acts as a portable blueprint (like OpenAPI for APIs) that is independent of any runtime, framework, or vendor. Open-sourced by Next Moca under Apache 2.0, also adopted by Eclipse LMOS.
Purpose
ADL defines what an agent is and what it can do, not how it runs. It complements:
- MCP — how agents call tools at runtime
- A2A — how agents communicate
- Agent Skills — how capabilities are packaged
Example
adl: "1.0"
agent:
name: research-assistant
version: "1.0.0"
description: "Researches topics and produces structured summaries"
author: "team-name"
license: MIT
llm:
provider: anthropic
model: claude-sonnet-4-5-20250929
temperature: 0.3
max_tokens: 4096
system_prompt: |
You are a research assistant. Produce well-structured,
factual summaries with cited sources.
tools:
- name: web-search
type: mcp
server: "search-server"
description: "Search the web for information"
- name: read-document
type: mcp
server: "doc-server"
description: "Read and parse documents"
rag:
- name: knowledge-base
source: "vector-store://company-docs"
description: "Internal documentation and policies"
permissions:
allowed_tools:
- web-search
- read-document
denied_actions:
- file_write
- code_execution
boundaries:
max_tokens_per_request: 8192
max_requests_per_minute: 30
dependencies:
- name: fact-checker
type: agent
description: "Validates factual claims before including them"
governance:
owner: "[email protected]"
review_status: approved
last_reviewed: "2026-01-15"
tags:
- research
- internal
Schema Sections
Agent Identity
| Field | Description |
|---|---|
name |
Unique agent identifier |
version |
Semantic version |
description |
What the agent does |
author |
Creator or team |
license |
SPDX identifier |
What ships with it
11 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.
- AGENTS.md 3.9 KB
- metadata.json 690 B
- README.md 648 B
- rules/_sections.md 1.3 KB
- rules/_template.md 362 B
- rules/adl-declare-dependencies-on-other-agents-explicitly-so-the.md 383 B
- rules/adl-define-permissions-explicitly.md 321 B
- rules/adl-include-governance-metadata-owner-review-status-for.md 371 B
- rules/adl-keep-system-prompts-in-adl-rather-than-hardcoded-in.md 371 B
- rules/adl-use-adl-alongside-mcp-tool-runtime-and-a2a.md 387 B
- rules/adl-use-semantic-versioning-so-dependent-systems-can-track.md 361 B
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.
- 11d ago First seen · 140 lines · 87 tokens per session scan A 49d705160563
adl is a skill published in the GitHub repository Tyler-R-Kendrick/agent-skills (11 stars, last pushed 3mo ago), licensed MIT. It adds 87 tokens to every session and 1,115 once invoked, about $0.0004 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 2026-08-31.
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