Guides · By AnyCap Team
Agent skills for developer tools
Agent skills are instruction files that teach an AI agent how to discover, install, authenticate, and invoke a capability.
They matter because developer agents do not just need tools. They need a reliable description of how to use those tools inside the execution environment.
A good skill closes the gap between capability availability and capability usage. Instead of forcing the agent to infer commands from scattered documentation, the skill tells it what the capability does, when to call it, what arguments to pass, and what setup has to happen first.
That is why AnyCap uses skills as a distribution layer across Claude Code, Cursor, Codex, and similar products. One capability runtime can be installed once, then exposed consistently to multiple developer agents through product-specific skill files.
A complete SKILL.md example
This small skill helps an agent choose a compatible video model before generation. It has a specific trigger, a repeatable workflow, and a clear stopping point. Save it as anycap-video-model-check/SKILL.md in a skill directory supported by your agent.
SKILL.md
---
name: anycap-video-model-check
description: Find an AnyCap video model for a requested input mode and inspect its request schema. Use when the user asks which model accepts text, images, or video references, or asks to check a model before generation.
---
# Check a video model
This skill only reads the model catalog and request schema.
1. Run `anycap --version` to check that the CLI is installed.
If it is missing, use https://docs.anycap.ai/getting-started/install/.
2. Run `anycap video models` and read the returned model IDs,
status, operations, and supported modes.
If authentication is required, follow
https://docs.anycap.ai/getting-started/authentication/.
3. Choose an active model that exposes the requested mode.
Use the user's model choice when it is available and compatible.
If none matches, report that limitation instead of inventing a model.
4. Replace <model-id> and <mode> with values from the catalog, then run:
`anycap video models <model-id> schema --operation generate --mode <mode>`
5. Report the model ID, mode, input fields, limits, and allowed values
returned by that schema. Explain which reference files are needed.
Do not run `anycap video generate` as part of this check.
Do not treat a successful schema lookup as a generated or tested video.
The folder and name match. The description tells the agent when to load the instructions. The body explains what to run, what to check in the result, and when the task is complete.
Check discovery, triggering, and execution
- Discovery: install the folder using your client's skill-loading instructions, then confirm anycap-video-model-check appears in its skill list. Reload the client if its instructions require it.
- Trigger: ask the agent to use anycap-video-model-check to find an active image-to-video model and inspect its schema. Confirm it reads this SKILL.md before choosing commands.
- Execution: compare the reported model and fields with the actual catalog and schema output. The task should stop after reporting the inputs; it should not submit a generation job.
- Troubleshooting: if the skill is missing, check the directory and frontmatter; if the command is missing, install the CLI; if the API rejects authentication, complete login in the same environment as the task.
The catalog and schema commands were checked with AnyCap CLI 0.6.1 on September 9, 2026. They verify available inputs and modes; they do not test generation quality or guarantee that every client automatically activates a skill.
Install and configure the Agent SkillRead the Agent Skills format specification
What a useful skill file contains
- Capability summary
- A short explanation of what the capability does, the outcomes it enables, and the request patterns that should trigger it.
- Install and auth steps
- Commands for installation, one-time authentication, environment requirements, and any account prerequisites.
- Invocation examples
- Concrete commands the agent can copy or adapt, including model names, flags, URL handling, and common prompt structures.
- Decision rules
- Guidance for when to use the capability instead of staying in text, when to ask for clarification, and when not to call it.
Skills vs native tools vs MCP
| Topic | Skills | Native tools | MCP |
|---|---|---|---|
| Primary job | Teach an agent how to use a capability end to end. | Expose built-in product actions such as search, edit, or terminal. | Expose live APIs or data sources over a standard protocol. |
| Best for | Installation flows, auth, usage patterns, and tool-selection guidance. | Core operations the product already ships with and manages directly. | Dynamic integrations that need structured requests and responses at runtime. |
| Main limitation | They describe usage, but they do not replace the underlying runtime. | They are product-specific and usually cannot be redistributed across agents. | It solves transport, not onboarding. Agents still need guidance on when to use it. |
Practical examples
- Image generation
- A coding agent creating launch assets can read the skill, install the AnyCap runtime, authenticate once, and then call image generation when a user asks for hero art or product illustrations.
- Video generation
- An agent preparing a release can switch from writing release notes to generating a short product demo video because the skill explains which command to call and how async polling works.
- Vision analysis
- A UI-review agent can upload a screenshot, run image analysis, and use the structured output to identify accessibility issues or summarize layout regressions.
How AnyCap uses skills as a distribution layer
AnyCap separates capability execution from capability discovery. The CLI and runtime handle the actual multimodal work, while the skill tells the agent how to reach that runtime inside its own environment.
That design keeps distribution lightweight. Instead of building a separate integration surface for every agent product, AnyCap ships a consistent runtime plus a thin layer of agent-specific instructions. The result is faster rollout, less duplicated integration code, and more predictable behavior inside real agent workflows.
It also makes maintenance simpler. When a new capability is added, the runtime can stay stable while the skill file is updated with new examples, trigger rules, and model choices.
FAQ
What is an agent skill?
An agent skill is an instruction file that tells an AI agent what a capability does, how to install it, how to authenticate, when to call it, and which commands or examples to reuse during execution.
How are skills different from MCP?
Skills explain workflow and tool selection. MCP exposes live tools and data over a protocol. In practice, MCP can provide the capability surface, while the skill teaches the agent when and how to use that surface correctly.
Why do developer agents need skills if they can already use shell commands?
Shell access gives an agent raw execution power, but not reliable operating instructions. Skills reduce mistakes by packaging install steps, authentication, trigger rules, and command examples in a form the agent can follow repeatedly.
Why does AnyCap distribute capabilities through skills?
Because skills let AnyCap expose one multimodal runtime to several agent products without rebuilding the integration for each one. The runtime stays consistent while the instructions are adapted to the target agent.
Apply this to one media task
Choose the workflow that matches your agent or input. Check setup, model access and cost before generation.