Agent skill

Tsh Creating Skills

by TheSoftwareHouse in TheSoftwareHouse/copilot-collections

Create new skills (SKILL.md) for GitHub Copilot. An agent skill from TheSoftwareHouse/copilot-collections.

MITAuto-check passedAgent Workflows

Install Tsh Creating Skills

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

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

GitHub CLI
$ gh skill install TheSoftwareHouse/copilot-collections tsh-creating-skills --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-skills .claude/skills/tsh-creating-skills && 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-skills
GitHub stars
284
Token cost
~4.1k tokens
SKILL.md length
1,660 words
Files
4 (incl. references)
Skills in repo
21
Repo updated
First seen
Licence
MIT

At a glance

Create new skills (SKILL.md) for GitHub Copilot. An agent skill from TheSoftwareHouse/copilot-collections.

  • Works in 3 steps: Discovery (~100 tokens): Only name and… → Activation (< 5000 tokens recommended):… → Resources (as needed): Files in…
  • Updating SKILL.md files
  • SKILL.md covers Core Design Principles, Skill Directory Structure, Skill Creation Process and Common Patterns, plus 2 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Tsh Creating Skills is an agent skill from TheSoftwareHouse/copilot-collections. Create new skills (SKILL.md) for GitHub Copilot. Provides naming conventions (gerund form), description guidelines, body structure, progressive disclosure patterns, templates, and validation checklists. Use when creating, reviewing, or updating SKILL.md files, or when discussing skill design and organization.

Its SKILL.md is about 4.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including reference files (for example `examples/reviewing-code.skill.md`, `references/common-patterns.md` and `skill.template.md`).

It sits in Agent Workflows, covering Skill authoring. The repository describes itself as: Opinionated AI-enabled workflows for product engineering. The licence is MIT.

When your agent uses it

  • Updating SKILL.md files
  • Discussing skill design and organization

Example prompts

  • “/tsh-creating-skills”

Workflow steps

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

  1. Discovery (~100 tokens): Only name and description are loaded at startup for all skills — this is how the agent decides which skills to…
  2. Activation (< 5000 tokens recommended): The full SKILL.md body is loaded when the skill is triggered.
  3. Resources (as needed): Files in scripts/, references/, and assets/ are loaded only when required during execution.

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 yaml).

    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 Skills loads about 4.1k tokens when it runs, and up to ~4.8k if it reads all its reference files. Until then it costs about 83 tokens; SKILL.md has 1,660 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~83
When it runs · the whole SKILL.md, loaded when a task matches
~4.1k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~4.8k

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). 1,660 words, ~4,052 tokens.

Download SKILL.mdSave it as .claude/skills/tsh-creating-skills/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
tsh-creating-skills
description
Create new skills (SKILL.md) for GitHub Copilot. Provides naming conventions (gerund form), description guidelines, body structure, progressive disclosure patterns, templates, and validation checklists. Use when creating, reviewing, or updating SKILL.md files, or when discussing skill design and organization.
user-invocable
false

Creating Skills

Creates well-structured, reusable skills for GitHub Copilot. Enforces naming conventions, content structure, and progressive disclosure patterns based on the Agent Skills specification and best practices.

Core Design Principles

<principles>
<what-is-a-skill>
A skill is a folder containing a `SKILL.md` file with YAML frontmatter and Markdown instructions. Skills provide **procedural knowledge** — step-by-step workflows, domain-specific context, templates, and reference materials that an agent loads on demand to perform specialized tasks.
  • Skills = reusable workflows, domain knowledge, step-by-step processes, templates (SKILL.md files)
  • Agents = behavior, personality, responsibilities, problem-solving approach (.agent.md files)
  • Prompts = workflow triggers, task starters, reusable prompt templates (.prompt.md files)

A skill must NOT define who the agent is — that belongs in the agent file. A skill defines HOW to perform a specific task or workflow. </what-is-a-skill>

<conciseness>
The context window is a shared resource. Every token in your skill competes with conversation history, other skills, and the user's actual request.

Default assumption: The LLM is already very smart. Only add context it doesn't already have.

Before adding any content, ask:

  • "Does the LLM really need this explanation?"
  • "Can I assume it already knows this?"
  • "Does this paragraph justify its token cost?"

Write the minimum necessary to guide the agent effectively. Trim explanations of concepts the LLM already understands. </conciseness>

<xml-syntax>
Use XML-like tags for structured content that requires explicit boundaries — principles, rules, specifications, or multi-part sections where nesting adds clarity. This ensures reliable parsing across all LLM model tiers.

Use plain Markdown for sequential content like step-by-step processes, guidelines, and reference tables where structure is already clear from headings and formatting.

When to use XML tags: Principles, rules, specifications, structured templates, sections with explicit open/close boundaries. When to use Markdown: Steps, checklists, tables, guidelines, reference lists, code examples. </xml-syntax>

<progressive-disclosure>
Skills use a three-tier loading model:
  1. Discovery (~100 tokens): Only name and description are loaded at startup for all skills — this is how the agent decides which skills to activate.
  2. Activation (< 5000 tokens recommended): The full SKILL.md body is loaded when the skill is triggered.
  3. Resources (as needed): Files in scripts/, references/, and assets/ are loaded only when required during execution.

Keep SKILL.md body under 500 lines. Move detailed reference material, examples, and templates to separate files. Reference those files from the SKILL.md body. </progressive-disclosure>

</principles>

Skill Directory Structure

A skill is a directory containing at minimum a SKILL.md file:

skill-name/
├── SKILL.md              # Required: instructions + metadata
├── scripts/              # Optional: executable code
├── references/           # Optional: additional documentation
└── assets/               # Optional: templates, static resources
  • The directory name MUST match the name field in the SKILL.md frontmatter.
  • Keep file references one level deep from SKILL.md — avoid deeply nested reference chains.
  • Name files descriptively: form-validation-rules.md, not doc2.md.

Skill Creation Process

Use the checklist below and track your progress:

Creation progress:
- [ ] Step 1: Define the skill's purpose
- [ ] Step 2: Create the skill name
- [ ] Step 3: Write the skill description
- [ ] Step 4: Write the skill body
- [ ] Step 5: Create supporting files (if needed)
- [ ] Step 6: Assemble and validate the skill

Step 1: Define the skill's purpose

Before writing anything, clarify the skill's purpose with the user. Use the vscode/askQuestions tool to gather answers to these questions in a single batch:

  1. What task does this skill perform? — The core activity (e.g., "analyze test coverage", "generate API documentation").
  2. What triggers activation? — When should the agent load this skill? What user requests or contexts should match?
  3. What does it produce? — Expected output: a document, code changes, a report, analysis, etc.
  4. What makes it distinct? — How is this different from existing skills? (List current skills for the user to compare against.)

If the user provided enough context in the conversation to answer these questions confidently, skip the clarification and proceed. Only ask about genuinely ambiguous or missing information.

Step 2: Create the skill name

<naming-conventions>
Naming Formula

Use gerund form (verb + -ing) followed by the object:

{gerund-verb}-{object}

This format clearly describes the activity or capability the skill provides. It reads naturally as "this skill is about [doing something]."

Rules
RuleRequirement
FormatGerund form: {verb-ing}-{object}
CharactersLowercase letters, numbers, and hyphens only (a-z, 0-9, -)
Length1–64 characters. Aim for under 20 characters (used as /slash-commands)
Start/endMust NOT start or end with a hyphen
Consecutive hyphensMust NOT contain --
Directory matchMust match the parent directory name exactly
Naming Examples

Good names (gerund form — preferred):

NameCharsSlash command
creating-agents15/creating-agents
reviewing-code14/reviewing-code
testing-e2e11/testing-e2e
analyzing-tasks15/analyzing-tasks
designing-architecture24/designing-architecture
gathering-context17/gathering-context
finding-gaps12/finding-gaps

Avoid:

PatternExampleWhy
Noun phrasestask-analysisPassive — doesn't convey action
Vague nameshelper, utils, toolsIndiscoverable — agent can't match them to tasks
Overly genericdocuments, data, filesToo broad — will trigger on irrelevant tasks
Inconsistent formMix of code-review and creating-agentsBreaks convention — confuses pattern recognition
Shortening Long Names

When the gerund form gets too long (over ~20 chars), simplify the object — let the description field carry the specificity.

VerboseShortenedStrategy
analyzing-implementation-gaps (30)finding-gaps (12)Simpler verb + shorter object
discovering-technical-context (30)gathering-context (17)Broader verb + drop qualifier
Confirming the Name

When multiple valid names exist, use vscode/askQuestions to let the user choose. Present 2-3 candidates with character counts and /slash-command previews. Mark the shortest gerund-form option as recommended.

</naming-conventions>

Step 3: Write the skill description

<description-guidelines>
Purpose

The description field is the primary discovery mechanism. The agent reads all skill descriptions at startup to decide which skill to activate for a given task. Your description must provide enough detail for the agent to match it accurately from potentially 100+ available skills.

Rules
RuleRequirement
Length1–1024 characters. Non-empty.
Point of viewAlways third person. Never use "I", "you", or "we".
ContentMust describe both WHAT the skill does AND WHEN to use it.
KeywordsInclude specific trigger terms that help the agent identify relevant tasks.
Formula
{What the skill does — core capabilities}. {When to use it — triggers and contexts}.
Good Examples
yaml
description: "Create custom agents (.agent.md) for GitHub Copilot in VS Code. Provides templates, guidelines, and a structured process for building agent definitions. Use when creating, reviewing, or updating .agent.md files."
yaml
description: "Extracts text and tables from PDF files, fills PDF forms, and merges multiple PDFs. Use when working with PDF documents or when the user mentions PDFs, forms, or document extraction."
Bad Examples
yaml
# Too vague — agent can't determine when to activate:
description: "Helps with documents."

# Wrong point of view — causes discovery problems:
description: "I can help you process Excel files."

# Missing trigger context — agent doesn't know WHEN to use it:
description: "Processes data from various sources."
</description-guidelines>

Step 4: Write the skill body

The Markdown body after the frontmatter contains the skill instructions. There are no strict format restrictions — write whatever helps the agent perform the task effectively.

Show full SKILL.md (724 more words)Show less
Content Guidelines
<body-guidelines>

Line limit: Keep the SKILL.md body under 500 lines. If approaching this limit, split content into referenced files using progressive disclosure patterns.

Structure the body with these sections (see template at ./skill.template.md):

SectionRequiredPurpose
IntroductionYes1-2 sentences describing what the skill does.
PrinciplesNoCore design principles using <principles> XML tags — when the skill has foundational rules that constrain all decisions.
Process / WorkflowYesStep-by-step checklist and detailed instructions. The core of the skill.
Reference tablesNoQuick-reference tables for rules, patterns, or conventions.
Connected SkillsYesLinks to related skills with brief rationale for each.

Conciseness rules:

  • Only add context the LLM doesn't already have.
  • Use examples instead of explanations — they convey style and expectations more efficiently.
  • See examples/reviewing-code.skill.md for a complete example demonstrating conciseness.
  • Provide a default approach, not multiple options. Add alternatives only when a specific condition requires them.
  • Don't explain concepts the LLM already knows (e.g., what PDFs are, how REST APIs work).

Consistent terminology: Choose one term for each concept and use it throughout the skill. Don't alternate between "API endpoint", "URL", "route", and "path" if they mean the same thing.

No time-sensitive information: Don't include dates or version-dependent guidance. If you must reference a deprecated approach, use an "Old patterns" section.

Use workflows for complex tasks: Break operations into clear, sequential steps. Provide a checklist the agent can copy and track progress against.

Implement feedback loops: For quality-critical tasks, include validation steps: run validator → fix errors → repeat.

</body-guidelines>
Degrees of Freedom

Match specificity to the task's fragility and variability:

Freedom LevelWhen to useExample
High (text instructions)Multiple valid approaches; decisions depend on contextCode review guidelines
Medium (pseudocode / templates)A preferred pattern exists but variation is acceptableReport generation with customizable sections
Low (exact scripts, no params)Operations are fragile; consistency is criticalDatabase migrations, file format validation

Step 5: Create supporting files (if needed)

When the SKILL.md body approaches 500 lines, or when the skill includes resources that should be loaded on demand:

<supporting-files>
File typeLocationPurposeLoad behavior
TemplatesSkill root or assets/Output format templates the agent fills inLoaded when agent needs to produce output
Reference docsreferences/Detailed specs, API docs, domain knowledgeLoaded when agent needs specific details
Scriptsscripts/Executable utility scriptsExecuted (not read into context) — saves tokens
ExamplesSkill root or references/Input/output examples, sample filesLoaded when agent needs to understand expected format

File reference rules:

  • Use relative paths from the skill root: ./references/REFERENCE.md
  • Keep references one level deep from SKILL.md — avoid nested reference chains
  • Make clear whether the agent should execute a script ("Run analyze.py") or read it as reference ("See analyze.py for the algorithm")
  • Include a table of contents in reference files longer than 100 lines
</supporting-files>

Step 6: Assemble and validate the skill

Use the ./skill.template.md template to build the SKILL.md file. See examples/reviewing-code.skill.md for a complete filled-in example.

After assembling the skill, use vscode/askQuestions to run a final review with the user. Present the proposed name, description, and a summary of the skill's workflow steps. Ask the user to confirm or request changes before finalizing.

Then validate against this checklist:

Validation Checklist
Validation:
- [ ] Frontmatter: `name` is valid (gerund form, lowercase, hyphens, ≤64 chars)
- [ ] Frontmatter: `name` matches the parent directory name
- [ ] Frontmatter: `description` describes WHAT the skill does and WHEN to use it
- [ ] Frontmatter: `description` is in third person (no "I", "you", "we")
- [ ] Frontmatter: `description` includes specific trigger keywords
- [ ] Body: Under 500 lines total
- [ ] Body: Introduction is 1-2 sentences
- [ ] Body: Process/workflow has a trackable checklist
- [ ] Body: Only adds context the LLM doesn't already have
- [ ] Body: Uses consistent terminology throughout
- [ ] Body: No time-sensitive information
- [ ] Body: XML tags (if used) are properly opened and closed
- [ ] Body: Connected Skills section references existing skills
- [ ] Files: Supporting files are one level deep (no nested reference chains)
- [ ] Files: Template files (if any) use XML tags for structured sections
- [ ] Files: Reference files over 100 lines have a table of contents
- [ ] Testing: Skill tested with real usage scenarios

Common Patterns

For standard workflow patterns (checklists, templates, conditional workflows, feedback loops), see references/common-patterns.md.

Anti-Patterns to Avoid

Anti-patternWhy it's harmfulFix
Over-explaining known conceptsWastes tokens; agent already knows what PDFs areRemove. Only explain project/domain-specific knowledge
Offering too many optionsConfusing; agent may pick wrong oneProvide a default, with escape hatch for edge cases
Deeply nested referencesAgent may partially read files at depth > 1Keep all references one level deep from SKILL.md
Vague file namesdoc2.md, helpers.mdUse descriptive names: form-validation-rules.md
Windows-style pathsBreaks on Unix systemsAlways use forward slashes: scripts/validate.py
Inconsistent naming within collectionConfuses pattern recognition; breaks discoverabilityUse gerund form consistently for all skill names
Magic numbers in scriptsAgent can't determine the right valueDocument why: TIMEOUT = 30 # HTTP requests typically complete within 30s

Connected Skills

  • tsh-creating-agents - to understand how skills relate to agent definitions and avoid overlap
  • tsh-creating-prompts - to understand how prompts reference and trigger skills
  • tsh-technical-context-discovering - to discover existing skill patterns in the project before creating a new one
  • tsh-codebase-analysing - to analyze existing skills and identify conventions to follow
  • tsh-creating-instructions - to understand when project rules belong in instruction files rather than skill content

© 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 3 other files (references) in .github/skills/tsh-creating-skills of TheSoftwareHouse/copilot-collections.

  • SKILL.md
  • examples/reviewing-code.skill.md
  • references/common-patterns.md
  • skill.template.md

Open the folder on GitHubat commit 2fbe51e

Compare with similar skills

Tsh Creating Skills 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 Skills compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Tsh Creating Skills this skillTheSoftwareHouse/copilot-collections284—~4.1kAutomated safety check: PassMIT
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Claude Code Skill Developer Guidediet103/claude-code-infrastructure-showcase10k11 repos~3.5kAutomated safety check: PassMIT
Darwin Skill Optimizeralchaincyf/darwin-skill6.2k1 repos~4.7kAutomated safety check: PassMIT
Claude Code Command Developmentanthropics/claude-plugins-official38k10 repos~4.8kAutomated safety check: PassApache-2.0
Claude Code Plugin Structureanthropics/claude-plugins-official38k10 repos~3.4kAutomated safety check: PassApache-2.0

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Categories

Questions about Tsh Creating Skills

What does Tsh Creating Skills do?

Create new skills (SKILL.md) for GitHub Copilot. An agent skill from TheSoftwareHouse/copilot-collections. Tsh Creating Skills is an agent skill from TheSoftwareHouse/copilot-collections.md) for GitHub Copilot.

When should I use Tsh Creating Skills?

Tsh Creating Skills fits situations like: updating SKILL.md files; discussing skill design and organization.

How do I install Tsh Creating Skills in Claude Code?

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

How do I install Tsh Creating Skills in Codex?

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

Can I use Tsh Creating Skills 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-skills -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-skills, .gemini/skills/tsh-creating-skills, .github/skills/tsh-creating-skills and .opencode/skills/tsh-creating-skills in your project.

What does Tsh Creating Skills need to run?

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

Does Tsh Creating Skills 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 Skills 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 Skills use?

Tsh Creating Skills 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 Skills use?

About 4.1k tokens (SKILL.md is roughly 16k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 722 tokens, read only when the agent opens those files.

What are the alternatives to Tsh Creating Skills?

Skills that share tags, products or a category with Tsh Creating Skills: Skill Creator (Azure/azqr, 796 stars), Claude Code Skill Developer Guide (diet103/claude-code-infrastructure-showcase, 10k stars), Darwin Skill Optimizer (alchaincyf/darwin-skill, 6.2k stars) and Claude Code Command Development (anthropics/claude-plugins-official, 38k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Tsh Creating Skills?

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.