Agent Capability logo

Agent Capability

Organization
vstorm-co
agent-capability

Add or change what an agent can do — a new tool the model can call, a new capability (knowledge, web search, charts, a guardrail, a compaction strategy), a tool rename, or a per-tool approval gate. Use whenever the ask is "give the agent a new tool/function/action", "add a capability", "let the agent do X". There is no @agent.tool and no assistant module in this project; every tool arrives through the capability registry.

Overview

Publishervstorm-co
Repositoryagenticos
Skill nameagent-capability
Stars
137
Forks
27
Bundled files
3
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 3 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by vstorm-co on GitHub. Read the source before you install it.

Installation

Install the Agent Capability AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/vstorm-co/agenticos/tree/main/.claude/skills/agent-capability
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/vstorm-co/agenticos.git /tmp/agenticos
mkdir -p .claude/skills
cp -r /tmp/agenticos/.claude/skills/agent-capability .claude/skills/agent-capability
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Agent Capability in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Agent Capability on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Agent Capability is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Capabilities — the only way a tool reaches a model

Read docs/howto/add-capability.md first. It is the walkthrough and it is current. This file is the decision layer around it: what shape the work should take, and the traps that are silent.

An agent here is data. There is no module with an Agent object to decorate — @agent.tool, RunContext[Deps] and app/agents/assistant.py do not exist. An agent is assembled per run from the capabilities its spec names, so a new tool is always part of a capability.

Pick the shape first

The askDo this
A tool that belongs to a decision somebody already makesAdd it to that capability's _toolset.py and its tools= tuple
A genuinely new switch an agent author would wantNew capability package
Behaviour change, not a tool (reasoning, a guard, injected context)New capability with tools=() — see clock/, thinking/
A third-party SaaS that already publishes an MCP serverNo code. See the mcp-connections skill and docs/mcp.md
Same tool, different wording for one agentNot code either — tool_overrides in that agent's spec

That last row catches a lot of requests. A tool's name and description are prompt, and an agent needing different behaviour from the same tool usually needs them reworded per agent, not a second tool written.

Layout, which a test enforces

backend/app/agents/capabilities/<name>/
  __init__.py       @register(...) — and nowhere else
  _capability.py    the AbstractCapability subclass
  _toolset.py       the tools, and the text the model reads
  README.md         why this exists and what it deliberately does not do

tests/test_capability_layout.py enforces all four. @register in a submodule only fires if something imports that module, which is how a capability vanishes from the Builder with every test still green.

A capability whose tools come from a library has no _toolset.py — skills and sandbox are both in the test's EXTERNAL_TOOLSET set. The tool text is still this repository's: declare it once in _capability.py and hand the same descriptions to the library, so the catalog and the model read the same sentence rather than two copies drifting in two repositories.

Read clock/ for the smallest complete example, knowledge/ for one with a config schema, resources and a scope, web_research/ for a conditional secret requirement, sandbox/ for per-tool approval and a resource the runner resolves.

When one flag cannot describe the whole capability

side_effecting on @register is the capability's answer, and CapabilityToolInfo.side_effecting overrides it per tool. Use the per-tool form when a capability genuinely both reads and writes: marking the whole of sandbox side-effecting makes an agent ask permission to list a directory, and not marking it lets a write run unattended. An author would then hand-write a tool_approval override per tool in every spec, and the one they forget is the dangerous one.

None — the default — defers to the capability, so every capability that declares nothing behaves exactly as it did. A binding's tool_approval still beats both: that is the operator's decision, and it wins over the code's.

The three things that fail silently

1. A tool missing from tools=. It still runs. It just cannot be approved, cannot be renamed per agent, and does not appear in the Builder. The dangerous half is that an author adds a side-effecting tool, forgets to declare it, and it runs unattended forever. tests/test_capability_registry.py is the drift test that catches this — it builds every registered capability and compares the declared list against the tools the model is actually offered.

2. A module missing from load_builtins(). The capability does not exist as far as the Builder is concerned. Registration is an import, not a scan.

3. A changed id. Ids are in every published spec and in clients' git repositories. Rename the class freely; never re-id.

What the tool says, and what it says when the call was wrong

Both are prompt, and both have a house shape - references/tool-text-and-failures.md has it, with the worked examples. The two things that keep being missed:

  • A docstring with no Returns:. The model cannot infer the shape of the answer, that a list stopped at 100 entries, or that a failure still carries output. Every tool here now says so; a new one that does not is the odd one out.
  • A bare raise ModelRetry. Use steer(ctx, ...) from capabilities/_failures.py. A retry past a tool's budget - one attempt, by default - does not fail the call, it ends the run with UnexpectedModelBehavior, so the same malformed chart sent twice takes the conversation with it. steer returns the message on the last attempt instead. A tool that needs it takes ctx; RunContext never reaches the schema.

And what stays a returned string: a result that is bad news, and a refusal. A retry prompt on a refusal invites the model to look for a way around it.

Then

  • Add the module to load_builtins() in _registry.py.
  • Write the README.md. Reasoning goes there, not in the commit message.
  • Test it — app/agents/** is at 100% and CI fails below it. See the backend-tests skill.
  • Give every config field Field(title=..., description=...) written for the person in the Builder, and add both under capabilityConfig.<id>.<field> in frontend/messages/{en,pl,de}.json — the English entry equal to the schema. TestCapabilitySettingsCopy fails on any of the three missing (#2098).
  • If the capability needs a credential, declare it as a SecretRequirement kind, never an instance. See the vault-secrets skill.
  • Update docs/reference/capabilities.md — it is the human-readable catalog and it is a snapshot of the registry.

Depth

  • references/registry-contract.md — every @register argument, ctx.resources, returning None, scopes, conditional secrets, returning a capability we did not write.
  • references/tool-text-and-failures.md — the four parts of a tool's docstring, and which failures steer the model rather than answering it.
  • references/approval-and-overrides.md — how side_effecting, approval, tool_approval and tool_overrides resolve, and why everything keys on the stable tool id.
  • docs/reference/capabilities.md — what ships today.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Agent Capability AI skill do?

Add or change what an agent can do — a new tool the model can call, a new capability (knowledge, web search, charts, a guardrail, a compaction strategy), a tool rename, or a per-tool approval gate. Use whenever the ask is "give the agent a new tool/function/action", "add a capability", "let the agent do X". There is no @agent.tool and no assistant module in this project; every tool arrives through the capability registry.

Why use Agent Capability on TypingMind?

Because you install it once and use it with any model. Agent Capability is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Agent Capability in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/vstorm-co/agenticos/tree/main/.claude/skills/agent-capability. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Agent Capability?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Agent Capability?

As many as you like. As long as a model supports skills, you can use Agent Capability with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Agent Capability AI skill free?

Yes. It is published on GitHub by vstorm-co under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇