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Agent skill

ShellLM Skill Author

by laude-institute in laude-institute/headlong

Scaffolds new ShellLM skills with the right frontmatter, directory layout and agent-facing writing style, so an agent can extend its own capabilities.

Apache-2.0Auto-check passedAgent Workflows

Install ShellLM Skill Author

skills CLI
$ npx skills add laude-institute/headlong --skill skill-author -a claude-code

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

GitHub CLI
$ gh skill install laude-institute/headlong skill-author --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/laude-institute/headlong.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/skill-author .claude/skills/skill-author && 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
skill-author
GitHub stars
1.2k
Token cost
~1.3k tokens
SKILL.md length
522 words
Files
1
Skills in repo
10
Repo updated
First seen
Licence
Apache-2.0

At a glance

Scaffolds new ShellLM skills with the right frontmatter, directory layout and agent-facing writing style, so an agent can extend its own capabilities.

  • Works in 4 steps: skills check -- requirements parse… → skills show -- content renders correctly → Try a real prompt: say "use the X skill… → …
  • Writing a brand-new SKILL.md for a capability the agent needs
  • SKILL.md covers When to use, What a skill is, Directory structure and Frontmatter spec, plus 7 more sections
  • Needs API_KEY

What it does

For writing a new SKILL.md, the skill lays out the required directory shape (a `SKILL.md` plus optional `README.md`), the frontmatter fields `name` and `description`, and the rule that the description must state specifically when to use the skill rather than describing it vaguely. Requirements such as environment variables or a particular OS go under an optional `metadata.shelllm` block, since skills with unmet requirements are filtered out of the list an agent sees.

The body is written for an agent reader, not a person: concrete commands before prose, tables for command references, copy-pasteable shell examples, and a target of under 1,000 tokens. It prefers calling a REST API with `curl` over wrapping a CLI, reserving a CLI dependency for cases such as OAuth token exchange or client-side signing. API keys are declared under `requires.env` rather than being requested from the user or stored in a file.

When your agent uses it

  • Writing a brand-new SKILL.md for a capability the agent needs
  • Reviewing an existing skill's frontmatter and structure
  • Deciding whether a skill should wrap a CLI or call a REST API directly
  • Trimming an overly long skill body down toward the token budget

Example prompts

  • “Scaffold a new skill that lets me search and append notes in an Obsidian vault.”
  • “Review my new skill's description — is it specific enough about when to trigger?”
  • “This skill needs an API key. Show me how to declare that without storing the secret.”

Workflow steps

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

  1. skills check -- requirements parse correctly
  2. skills show -- content renders correctly
  3. Try a real prompt: say "use the X skill to do Y" (your identity's persona command) and watch what the agent does
  4. Iterate based on whether the agent uses the skill correctly

What it can do on your machine

Read from SKILL.md and the folder at commit c43e597. 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 and markdown).

    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 these keys or tokens, usually read from environment variables:

    • API_KEY

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

Context cost

ShellLM Skill Author loads about 1.3k tokens when it runs. Until then it costs about 50 tokens; SKILL.md has 522 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~50
When it runs · the whole SKILL.md, loaded when a task matches
~1.3k

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 laude-institute/headlong at commit c43e597, republished under its Apache-2.0 licence (© laude-institute). 522 words, ~1,291 tokens.

Download SKILL.mdSave it as .claude/skills/skill-author/SKILL.md (or your agent's skills folder).
name
skill-author
description
Create new ShellLM skills. Use when the user asks to write, author, build, or scaffold a new skill, or when an agent needs to extend its own capabilities by creating a new SKILL.md file.

skill-author

When to use

The user asks to create, write, author, build, or scaffold a new skill. Or you (the agent) need to extend your own capabilities by writing a new SKILL.md.

What a skill is

A skill is a directory containing a SKILL.md file. It tells an agent when to use a capability and how to invoke it. Skills are procedural documentation, not reference docs. The agent reads a skill on demand via skills show <name>.

Directory structure

skills/<skill-name>/
├── SKILL.md           # required: the skill definition
├── README.md          # optional: human-facing notes
└── <other files>      # optional: scripts, templates, data

Frontmatter spec

yaml
---
name: my-skill
description: One or two sentences. Be specific about WHEN to use the skill.
metadata:
  shelllm:
    requires:
      env: ["API_KEY"]           # all must be set
      bins: ["curl", "jq"]      # all must exist on PATH
      anyBins: ["gh", "hub"]    # at least one must exist
      os: ["darwin", "linux"]   # current OS must be in list
---

Required: name (kebab-case, matches directory name), description.

Description matters. It's matched against user intent. Bad: "manages files." Good: "Read, write, search, and append notes in an Obsidian vault. Use when the user mentions Obsidian or asks to interact with markdown notes."

Requirements are optional. Only declare metadata.shelllm if the skill needs env vars, binaries, or a specific OS. Skills with unmet requirements are filtered from skills list.

Writing the body

Write for an agent reader, not a human:

  • Skip marketing language and "why use this"
  • Lead with concrete commands, not prose
  • Use tables for command references when they fit
  • Keep it under 1,000 tokens -- agent context is precious
  • Examples should be copy-pasteable shell commands, not pseudocode
  • Don't enumerate all flags -- teach the patterns specific to this tool
markdown
## When to use
[Concrete intent triggers]

## Setup
[One-time prerequisites: env vars, services to enable]

## Common operations

### Operation 1
\`\`\`bash
exact command here
\`\`\`
One sentence on when/why.

### Operation 2
\`\`\`bash
exact command here
\`\`\`

## Tips
[Edge cases, gotchas, composition with other skills]

Prefer curl + REST APIs over CLIs

Where possible, write skills that use curl against a REST API rather than wrapping a CLI tool. ShellLM minimizes installed dependencies. The SKILL.md is the abstraction layer.

Worth a CLI dependency when:

  • Auth flows require complex local state (OAuth, multi-step token exchange)
  • Significant client-side computation (signing, encryption)
  • The API has bizarre quirks the CLI smooths over

Declare requirements, don't manage secrets

If a skill needs an API key, declare it in requires.env. Do NOT prompt the user for the key or store it in a file. The user manages secrets via their shell environment or a secret manager.

Show full SKILL.md (208 more words)Show less

Self-contained and composable

A good skill teaches the agent enough to use a tool without external docs. It should also compose -- if your skill produces JSON, the agent can pipe it to jq, etc.

Example: minimal skill

yaml
---
name: cowsay
description: Generate ASCII cow art with messages. Use when the user asks for cowsay or fun ASCII art.
metadata:
  shelllm:
    requires:
      bins: ["cowsay"]
---
markdown
# cowsay

## When to use
User asks for cowsay, ASCII art with a message, or fun text formatting.

## Common operations

### Say something
\`\`\`bash
cowsay "Hello, world!"
\`\`\`

### Use a different character
\`\`\`bash
cowsay -f tux "Linux rules"
\`\`\`

### List available figures
\`\`\`bash
cowsay -l
\`\`\`

### Pipe input
\`\`\`bash
echo "piped message" | cowsay
\`\`\`

Testing a skill

After writing, validate:

  1. skills check <name> -- requirements parse correctly
  2. skills show <name> -- content renders correctly
  3. Try a real prompt: <name> say "use the X skill to do Y" (your identity's persona command) and watch what the agent does
  4. Iterate based on whether the agent uses the skill correctly

Common mistakes

  • Description too vague -- skill never gets matched to user intent
  • Body too long -- wastes context window; keep under 1,000 tokens
  • Missing requires.env -- skill silently fails when API key isn't set
  • Writing for humans -- marketing language, badges, "why use this" sections
  • Hardcoding paths -- don't assume specific shell setup or directory structure
  • Not testing -- always verify what the agent actually does with the skill

After writing a new skill, place it in the skills directory and run skills check <name> to verify it parses and that requirements resolve correctly. If the skill needs to be available to other agents and users, propose adding it to the project's bundled skills or publishing it to a skills registry.

© laude-institute, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/skill-author of laude-institute/headlong.

Open the folder on GitHubat commit c43e597

Compare with similar skills

ShellLM Skill Author 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.

ShellLM Skill Author compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
ShellLM Skill Author this skilllaude-institute/headlong1.2k—~1.3kAutomated safety check: PassApache-2.0
Reviewing Documentationmicrosoft-foundry/foundry-agent-webapp127—~1.1kAutomated safety check: PassMIT
Harness Agent Team Designerrevfactory/harness9.1k—~4.5kAutomated safety check: PassApache-2.0
Harness Evolution Feedback Looprevfactory/harness9.1k—~855Automated safety check: PassApache-2.0
Skill Creatorccusage/ccusage19k—~1.1kAutomated safety check: PassCustom licence
Authoring Skillsvercel/next.js143k—~1kAutomated safety check: PassMIT

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Categories

Questions about ShellLM Skill Author

What does ShellLM Skill Author do?

Scaffolds new ShellLM skills with the right frontmatter, directory layout and agent-facing writing style, so an agent can extend its own capabilities. md`), the frontmatter fields `name` and `description`, and the rule that the description must state specifically when to use the skill rather than describing it vaguely.shelllm` block, since skills with unmet requirements are filtered out of the list an agent sees.

When should I use ShellLM Skill Author?

ShellLM Skill Author fits situations like: writing a brand-new SKILL.md for a capability the agent needs; reviewing an existing skill's frontmatter and structure; deciding whether a skill should wrap a CLI or call a REST API directly; trimming an overly long skill body down toward the token budget.

How do I install ShellLM Skill Author in Claude Code?

Run `npx skills add laude-institute/headlong --skill skill-author -a claude-code`. Or copy the skill folder (skills/skill-author in laude-institute/headlong) into .claude/skills/skill-author in your project. Claude Code loads it when a task matches its description.

How do I install ShellLM Skill Author in Codex?

Run `npx skills add laude-institute/headlong --skill skill-author -a codex`. Or copy the skill folder (skills/skill-author in laude-institute/headlong) into .agents/skills/skill-author in your project. Codex loads it when a task matches its description.

Can I use ShellLM Skill Author 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 laude-institute/headlong --skill skill-author -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/skill-author, .gemini/skills/skill-author, .github/skills/skill-author and .opencode/skills/skill-author in your project.

What does ShellLM Skill Author need to run?

Going by SKILL.md and its folder, ShellLM Skill Author needs credentials named API_KEY.

Does ShellLM Skill Author 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 ShellLM Skill Author 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 ShellLM Skill Author use?

ShellLM Skill Author is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does ShellLM Skill Author use?

About 1.3k tokens (SKILL.md is roughly 5.2k 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 ShellLM Skill Author?

Skills that share tags, products or a category with ShellLM Skill Author: Reviewing Documentation (microsoft-foundry/foundry-agent-webapp, 127 stars), Harness Agent Team Designer (revfactory/harness, 9.1k stars), Harness Evolution Feedback Loop (revfactory/harness, 9.1k stars) and Skill Creator (ccusage/ccusage, 19k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains ShellLM Skill Author?

laude-institute (a GitHub organization) maintains it in laude-institute/headlong, which has 1,216 GitHub stars. The repository holds 10 skills in this directory. The repository was last updated on October 9, 2026.

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