Agent skill

Tsh Creating Agents

by TheSoftwareHouse in TheSoftwareHouse/copilot-collections

Create custom agents (.agent.md) for GitHub Copilot in VS Code.

MITAuto-check passedAI & LLM Engineering

Install Tsh Creating Agents

skills CLI
$ npx skills add TheSoftwareHouse/copilot-collections --skill tsh-creating-agents -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install TheSoftwareHouse/copilot-collections tsh-creating-agents --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/TheSoftwareHouse/copilot-collections.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.github/skills/tsh-creating-agents .claude/skills/tsh-creating-agents && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
tsh-creating-agents
GitHub stars
284
Token cost
~2.4k tokens
SKILL.md length
1,102 words
Files
2
Skills in repo
21
Repo updated
First seen
Licence
MIT

At a glance

Create custom agents (.agent.md) for GitHub Copilot in VS Code.

  • Works in 6 steps: Every section uses a matching opening… → Tags use lowercase-kebab-case naming → Nesting is allowed for sub-sections:… → …
  • Updating .agent.md files
  • SKILL.md covers Core Design Principles, Creation Process, Agent File Structure Reference and XML Syntax Guidelines, plus 1 more section
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Tsh Creating Agents is an agent skill from TheSoftwareHouse/copilot-collections. Create custom agents (.agent.md) for GitHub Copilot in VS Code. Provides templates, guidelines, and a structured process for building agent definitions that describe behavior, personality, responsibilities, and problem-solving approaches. Use when creating, reviewing, or updating .agent.md files.

Its SKILL.md is about 2.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 1 other file (for example `agent.template.md`).

It sits in AI & LLM Engineering, covering Building AI agents. It works with Visual Studio Code. The repository describes itself as: Opinionated AI-enabled workflows for product engineering. The licence is MIT.

When your agent uses it

  • Updating .agent.md files
  • Tasks that involve Building AI agents

Example prompts

  • “/tsh-creating-agents”

Workflow steps

6 steps, taken from the first numbered list in SKILL.md.

  1. Every section uses a matching opening and closing tag: ...
  2. Tags use lowercase-kebab-case naming
  3. Nesting is allowed for sub-sections: inside
  4. Markdown formatting (bold, lists, tables, code blocks) is used inside XML tags for content
  5. Justified, agent-specific domain section tags are permitted only when they carry clear meaning not covered by an existing canonical…
  6. Avoid XML attributes for structural content — use nested tags or Markdown content instead. Exception: identifier attributes (e.g., ) are…

What it can do on your machine

Read from SKILL.md and the folder at commit 2fbe51e. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are xml).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Tsh Creating Agents loads about 2.4k tokens when it runs. Until then it costs about 79 tokens; SKILL.md has 1,102 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~79
When it runs · the whole SKILL.md, loaded when a task matches
~2.4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from TheSoftwareHouse/copilot-collections at commit 2fbe51e, republished under its MIT licence (© TheSoftwareHouse). 1,102 words, ~2,380 tokens.

Download SKILL.mdSave it as .claude/skills/tsh-creating-agents/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
tsh-creating-agents
description
Create custom agents (.agent.md) for GitHub Copilot in VS Code. Provides templates, guidelines, and a structured process for building agent definitions that describe behavior, personality, responsibilities, and problem-solving approaches. Use when creating, reviewing, or updating .agent.md files.
user-invocable
false

Creating Agents

Creates well-structured custom agents for GitHub Copilot in VS Code. Enforces a consistent pattern across all agents and ensures clear separation between agent definitions, skills, and prompts.

Core Design Principles

<principles>
<separation-of-concerns>
An agent file (.agent.md) defines WHO the agent is. It must NOT define HOW specific workflows are executed.
  • Agent = behavior, personality, responsibilities, and problem-solving approach
  • Skills = reusable workflows, domain knowledge, step-by-step processes (SKILL.md files)
  • Prompts = task triggers, workflow starters, reusable prompt templates (.prompt.md files)

Every agent is designed to be extendable with skills and prompts. The agent itself provides the foundation; skills and prompts layer on top for specific workflows. </separation-of-concerns>

<xml-syntax>
All structured content inside the agent body MUST use XML-like tags for explicit structure. This ensures reliable parsing across all LLM model tiers.

Use Markdown only for inline formatting (bold, code blocks, tables, lists) within XML sections. </xml-syntax>

<minimal-scope>
An agent should only describe what is necessary for its specific role. Avoid duplicating instructions that belong in skills or project-level instruction files (.instructions.md).
</minimal-scope>
</principles>

Creation Process

Use the checklist below and track your progress:

Creation progress:
- [ ] Step 1: Define the agent's purpose
- [ ] Step 2: Write the agent role and responsibilities
- [ ] Step 3: Determine tools and write tool usage guidelines
- [ ] Step 4: Determine skills and write skills usage guidelines
- [ ] Step 5: Configure handoffs (if applicable)
- [ ] Step 6: Add domain standards (if applicable)
- [ ] Step 7: Add constraints (if applicable)
- [ ] Step 8: Assemble the agent file using the template
- [ ] Step 9: Validate the agent file

Step 1: Define the agent's purpose

Answer these questions before writing anything:

  • What specific role does this agent fulfill? (e.g., architect, reviewer, engineer)
  • What problems does it solve?
  • What is the agent's primary focus area?
  • Which other agents does it collaborate with?
  • What makes this agent distinct from existing agents?

Step 2: Write the agent role and responsibilities

Write the <agent-role> section. This is the core of the agent. It must describe:

  • A clear role statement starting with "Role: You are..."
  • The agent's primary responsibilities and focus areas
  • The agent's behavioral guidelines (how it approaches work)
  • Skip-level instructions for skill and tool usage (always check skills first, always use tools to gather context)

Follow the pattern from existing agents. Keep the role focused. Do not include workflow-specific steps — those belong in skills.

Step 3: Determine tools and write tool usage guidelines

Review available tools and select only those relevant to the agent's role:

  • List each tool in the YAML frontmatter tools array
  • For each tool, write a <tool> entry in the <tool-usage> section describing:
    • MUST use when: Specific conditions requiring tool use
    • IMPORTANT: Configuration notes, prerequisites, or behavioral constraints
    • SHOULD NOT use for: Anti-patterns and out-of-scope usage

Match tool selection to the agent's responsibilities. Read-only agents should not get edit tools. Implementation agents need execution tools.

Step 4: Determine skills and write skills usage guidelines

Review available skills and select those the agent should load:

  • For each skill, write a short entry explaining WHEN to use it
  • Use the format: skill-name - brief description of when to use it

Do not duplicate skill content in the agent file. The agent only references skills.

Step 5: Configure handoffs (if applicable)

If the agent participates in multi-step workflows:

  • Define handoff entries in the YAML frontmatter
  • Each handoff needs: label, agent, prompt, and send (typically false for user approval)
  • Optionally specify model for the target agent

Step 6: Add domain standards (if applicable)

If the agent's role requires enforcing domain-specific standards (e.g., testing conventions, security rules, UI patterns), add a <domain-standards> section. This section is optional and should only appear when the agent genuinely needs domain-specific rules that are NOT covered by skills.

Step 7: Add constraints (if applicable)

If the agent has specific limitations or anti-patterns to avoid, add a <constraints> section. Common constraints include:

  • What the agent must NOT produce (e.g., "don't create implementation plans")
  • Scope boundaries (e.g., "don't provide deployment instructions")
  • Delegation rules (e.g., "escalate architectural decisions to the architect")

Step 8: Assemble the agent file using the template

Use the ./agent.template.md template to assemble the final .agent.md file. Place the file in .github/agents/ with the naming convention <agent-name>.agent.md.

Step 9: Validate the agent file

Verify the agent file against this checklist:

  • YAML frontmatter is valid and parseable
  • description is present and concise
  • tools array includes only relevant tools
  • All tools listed in frontmatter have corresponding <tool> entries in <tool-usage>
  • All skills referenced in <skills-usage> are existing skills in the project
  • XML-like tags are properly opened and closed
  • No workflow-specific instructions are embedded (those belong in skills)
  • No coding standards are embedded (those belong in .instructions.md)
  • Agent role is focused and distinct from existing agents
  • Handoffs (if present) target valid agent names
Show full SKILL.md (390 more words)Show less

Agent File Structure Reference

Frontmatter Fields
FieldRequiredDescription
descriptionYesBrief description of the agent, shown as placeholder text in chat input.
toolsYesArray of tool/tool-set names available to the agent. Use <server>/* for all MCP server tools.
handoffsNoArray of handoff configurations for multi-step workflows.
agentsNoArray of agent names available as subagents. Use * for all, [] for none.
nameNoOverride display name (defaults to filename).
argument-hintNoHint text shown in chat input to guide user interaction.
modelNoPreferred AI model (string or prioritized array).
user-invokableNoBoolean, controls visibility in agents dropdown (default: true).
disable-model-invocationNoBoolean, prevents auto-invocation as subagent (default: false).
Body Sections
SectionRequiredPurpose
<agent-role>YesRole definition, responsibilities, behavioral guidelines.
<approach>NoHigh-level method or philosophy for the agent, typically nested inside <agent-role>.
<skills-usage>YesList of skills the agent uses with guidance for each.
<tool-usage>YesTool access rules and usage guidelines per tool.
<domain-standards>NoDomain-specific standards and rules the agent enforces.
<collaboration>NoInteraction patterns with other agents or team members.
<constraints>NoExplicit limitations and anti-patterns for the agent.
<output-format>NoExpected structure or format of the agent's deliverables.

XML Syntax Guidelines

All body content in the agent file must use XML-like tags for structure. Rules:

  1. Every section uses a matching opening and closing tag: <section-name> ... </section-name>
  2. Tags use lowercase-kebab-case naming
  3. Nesting is allowed for sub-sections: <tool> inside <tool-usage>
  4. Markdown formatting (bold, lists, tables, code blocks) is used inside XML tags for content
  5. Justified, agent-specific domain section tags are permitted only when they carry clear meaning not covered by an existing canonical section; they are not arbitrary, and they must still use lowercase-kebab-case with matching opening and closing tags.
  6. Avoid XML attributes for structural content — use nested tags or Markdown content instead. Exception: identifier attributes (e.g., <tool name="...">) are acceptable when they improve readability.

Example structure:

xml
<agent-role>
Role: You are a...
</agent-role>

<tool-usage>
<tool name="context7">
- **MUST use when**: ...
- **SHOULD NOT use for**: ...
</tool>
</tool-usage>

Connected Skills

  • tsh-creating-prompts - to understand how prompts reference agents and ensure agents don't overlap with prompt responsibilities
  • tsh-creating-skills - to ensure this skill's own structure follows the canonical skill creation requirements
  • tsh-technical-context-discovering - to understand existing agent patterns in the project before creating a new one
  • tsh-codebase-analysing - to analyze existing agents and identify patterns to follow
  • tsh-creating-instructions - to understand when coding standards and project conventions belong in instruction files rather than agent definitions

© TheSoftwareHouse, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file in .github/skills/tsh-creating-agents of TheSoftwareHouse/copilot-collections.

  • SKILL.md
  • agent.template.md

Open the folder on GitHubat commit 2fbe51e

Compare with similar skills

Tsh Creating Agents next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Tsh Creating Agents compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Tsh Creating Agents this skillTheSoftwareHouse/copilot-collections284—~2.4kAutomated safety check: PassMIT
Microsoft Docsmicrosoft/ai-agents-for-beginners77k3 repos~1.2kAutomated safety check: PassMIT
Microsoft Docsmicrosoft/ai-agents-for-beginners77k—~1.6kAutomated safety check: PassMIT
Microsoft Docsmicrosoft/ai-agents-for-beginners77k—~1.3kAutomated safety check: PassMIT
Microsoft Docsmicrosoft/ai-agents-for-beginners77k—~1.4kAutomated safety check: PassMIT
Microsoft Docsmicrosoft/ai-agents-for-beginners77k—~1.4kAutomated safety check: PassMIT

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Questions about Tsh Creating Agents

What does Tsh Creating Agents do?

Create custom agents (.agent.md) for GitHub Copilot in VS Code. Tsh Creating Agents is an agent skill from TheSoftwareHouse/copilot-collections.md) for GitHub Copilot in VS Code.

When should I use Tsh Creating Agents?

Tsh Creating Agents fits situations like: updating .agent.md files; tasks that involve Building AI agents.

How do I install Tsh Creating Agents in Claude Code?

Run `npx skills add TheSoftwareHouse/copilot-collections --skill tsh-creating-agents -a claude-code`. Or copy the skill folder (.github/skills/tsh-creating-agents in TheSoftwareHouse/copilot-collections) into .claude/skills/tsh-creating-agents in your project. Claude Code loads it when a task matches its description.

How do I install Tsh Creating Agents in Codex?

Run `npx skills add TheSoftwareHouse/copilot-collections --skill tsh-creating-agents -a codex`. Or copy the skill folder (.github/skills/tsh-creating-agents in TheSoftwareHouse/copilot-collections) into .agents/skills/tsh-creating-agents in your project. Codex loads it when a task matches its description.

Can I use Tsh Creating Agents in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add TheSoftwareHouse/copilot-collections --skill tsh-creating-agents -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/tsh-creating-agents, .gemini/skills/tsh-creating-agents, .github/skills/tsh-creating-agents and .opencode/skills/tsh-creating-agents in your project.

What does Tsh Creating Agents need to run?

SKILL.md names no scripts, command-line tools or credentials: Tsh Creating Agents is instructions for the agent only.

Does Tsh Creating Agents access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Tsh Creating Agents safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Tsh Creating Agents use?

Tsh Creating Agents is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Tsh Creating Agents use?

About 2.4k tokens (SKILL.md is roughly 9.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Tsh Creating Agents?

Skills that share tags, products or a category with Tsh Creating Agents: Microsoft Docs (microsoft/ai-agents-for-beginners, 77k stars), Microsoft Docs (microsoft/ai-agents-for-beginners, 77k stars), Microsoft Docs (microsoft/ai-agents-for-beginners, 77k stars) and Microsoft Docs (microsoft/ai-agents-for-beginners, 77k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Tsh Creating Agents?

TheSoftwareHouse (a GitHub organization) maintains it in TheSoftwareHouse/copilot-collections, which has 284 GitHub stars. The repository holds 21 skills in this directory. The repository was last updated on October 5, 2026.

Source: TheSoftwareHouse/copilot-collections on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.