Agent skill

AI Readme

by swyxio in swyxio/skills

Create or substantially revise repository README files that help a specific reader understand a technical project, reach a verified first result, and progress into realistic use, evaluation…

MITAuto-check passedDevelopment

Install AI Readme

skills CLI
$ npx skills add swyxio/skills --skill ai-readme -a claude-code

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

GitHub CLI
$ gh skill install swyxio/skills ai-readme --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/swyxio/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/ai-readme .claude/skills/ai-readme && 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
ai-readme
GitHub stars
176
Token cost
~2.7k tokens
SKILL.md length
1,443 words
Files
2
Skills in repo
89
Repo updated
First seen
Licence
MIT

At a glance

Create or substantially revise repository README files that help a specific reader understand a technical project, reach a verified first result, and progress into realistic use, evaluation…

  • Works in 4 steps: State each reader belief as a numbered… → Give two to four mutually exclusive… → Recommend one choice for each decision. → …
  • Project landing READMEs
  • SKILL.md covers Choose the README's job, Keep the front door small, Align with the reader and Establish the truth, plus 5 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

AI Readme is an agent skill from swyxio/skills. Create or substantially revise repository README files that help a specific reader understand a technical project, reach a verified first result, and progress into realistic use, evaluation, debugging, or contribution. Use for project landing READMEs, CLI and library quickstarts, experimental or research repositories, executable engineering notebooks, and contributor-facing repository guides when an agent should reconstruct behavior from code, tests, commands, examples, and history rather than write generic…

Its SKILL.md is about 2.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files (for example `agents/openai.yaml`).

It sits in Development, covering Technical documentation. The repository describes itself as: Agent skills for Claude Code and other AI agents. The licence is MIT.

When your agent uses it

  • Project landing READMEs
  • CLI and library quickstarts
  • Research repositories
  • Executable engineering notebooks

Example prompts

  • “/ai-readme”

Workflow steps

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

  1. State each reader belief as a numbered decision.
  2. Give two to four mutually exclusive lettered choices with concrete effects.
  3. Recommend one choice for each decision.
  4. End with `Reply approve all to accept 1A, 2B, 3A, or give changes such as

What it can do on your machine

Read from SKILL.md and the folder at commit 038ef34. 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.

    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

AI Readme loads about 2.7k tokens when it runs. Until then it costs about 134 tokens; SKILL.md has 1,443 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~134
When it runs · the whole SKILL.md, loaded when a task matches
~2.7k

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 swyxio/skills at commit 038ef34, republished under its MIT licence (© swyxio). 1,443 words, ~2,658 tokens.

Download SKILL.mdSave it as .claude/skills/ai-readme/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
ai-readme
description
Create or substantially revise repository README files that help a specific reader understand a technical project, reach a verified first result, and progress into realistic use, evaluation, debugging, or contribution. Use for project landing READMEs, CLI and library quickstarts, experimental or research repositories, executable engineering notebooks, and contributor-facing repository guides when an agent should reconstruct behavior from code, tests, commands, examples, and history rather than write generic documentation.

AI README

Write a repository-native explanation that earns trust by working. Lead with what the project is, who it helps, and the shortest verified path to an observable result. Then reveal the mechanism, tradeoffs, diagnostics, and internals at the reader's pace.

Combine two complementary habits:

  • use an executable, progressive spine: one stable example, working commands, expected results, controlled variants, visible failure modes, and a reference path when correctness or quality can vary;
  • write like a public technical notebook: begin from a real task or curiosity, show the decisive artifact near the claim, explain what happened in ordinary language, preserve surprise and uncertainty, and stop when the reader's job is complete.

Apply swyx-writing as the shared voice and editing layer. For substantial public README work, read its technical-writer influences. Apply research-grounded-writing when external claims or comparisons need support beyond the repository.

Choose the README's job

Identify the primary job before choosing sections:

  • Project landing page: help a newcomer understand, evaluate, and try the project.
  • CLI or library guide: get an adopter from installation to one useful call, then document common recipes, errors, and the deeper reference.
  • Experiment or research repository: state the question, setup, runnable experiment, observed result, uncertainty, and reproduction boundary.
  • Executable engineering notebook: preserve a working reference path, controlled optimizations, benchmarks, diagnostics, and failed trials beside the code they describe.
  • Contributor guide: explain architecture, invariants, ownership, tests, development commands, and safe extension points after the user path is clear.

A README may serve several jobs, but choose one primary reader path. Split a large implementation ledger, benchmark history, API reference, or contributor manual into linked documents when keeping it inline would bury first use.

Keep the front door small

Default to a focused project README of roughly 600–1,500 words. Treat that range as an editing signal, not a quota. A new evaluator usually needs one plain definition, honest status, one verified first result, the minimum model, the limitations that affect adoption, and a short route into deeper material.

Do not draft complete evaluator, operator, researcher, and contributor paths in the same file. When the chosen reader is an evaluator or adopter, route adapter catalogs, backup and recovery recipes, full protocol status, security details, crate maps, and contributor commands to linked documentation unless one of them changes the adoption decision.

Allow a longer README when its primary job is genuinely an executable engineering notebook and the reader must compare modes, reproduce experiments, or diagnose observable failures beside the code. Length is earned by a progressive recurring example, not by the number of facts available.

Align with the reader

Before a substantial creation or rewrite, determine what the skill user expects the reader to be, know, and want. If those expectations are not already explicit, ask one compact batch using the align-me shape:

  1. State each reader belief as a numbered decision.
  2. Give two to four mutually exclusive lettered choices with concrete effects.
  3. Recommend one choice for each decision.
  4. End with Reply approve all to accept 1A, 2B, 3A, or give changes such as 2C. Then wait.

Tailor the choices to the repository. A useful default is:

  1. Who is the primary reader?
    • A. New evaluator or adopter — optimize for understanding and first success.
    • B. Operator or integrator — optimize for setup, behavior, and failure recovery.
    • C. Contributor or researcher — optimize for internals and extension.
  2. What may they already know?
    • A. General software concepts only — define the domain and every project noun.
    • B. The domain, but not this project — explain the project's distinctive model.
    • C. This ecosystem — move faster, but still define repository-specific terms.
  3. What should they accomplish?
    • A. Decide whether the project fits and obtain one visible result.
    • B. Reproduce or integrate a real workflow.
    • C. Understand, debug, benchmark, or extend the implementation.

Recommend the new evaluator, domain-but-not-project, and first-visible-result path unless the repository clearly serves specialists. Do not ask questions the user has already answered. For a small correction, state the inferred reader briefly and proceed.

Establish the truth

Inspect before writing:

  1. Read the current README and linked documentation without assuming either is current.
  2. Inspect build and dependency manifests, public interfaces, CLI help, examples, tests, configuration, release metadata, and recent relevant history.
  3. Identify the project's real status: proposal, prototype, experimental, supported, production-used, deprecated, or unknown.
  4. Find the shortest safe first-success path. Run it when reasonably cheap.
  5. Capture the actual prerequisite, command, output, duration, environment, and cleanup needed to reproduce it.
  6. Identify the reference or oracle path when faster, approximate, cached, or aggressive modes can change correctness, quality, or behavior.

Do not invent commands, output, support promises, performance, compatibility, or project intent. Mark a path as unverified when it cannot be run. Preserve useful failed attempts and reversals when they prevent readers from repeating a mistake.

Address the elephant in the first screen

The opening viewport must answer, in ordinary language:

  1. What is this project?
  2. Who is it for, and what can they do with it?
  3. What is its current status or most important limitation?
  4. What is the shortest path to seeing it work?

Name the central thing directly. If the repository is a Program Database, say Program Database; do not hide it behind a slogan such as Record every thread. A memorable line may sharpen the explanation, but it cannot replace the subject, status, or reader promise.

Do not put badge walls, architecture inventories, project history, generated hero art, or a table of contents ahead of the definition and first useful path.

Show full SKILL.md (526 more words)Show less

Build an executable progression

Prefer this sequence, adapting it to the reader's job:

  1. Inspect or install. State prerequisites and provide the smallest safe setup.
  2. Produce one visible result. Use one canonical fixture, request, file, prompt, or input that can recur through the README.
  3. Interpret it. Tell the reader what happened and what to notice.
  4. Explain the minimum model. Reduce the mechanism to the fewest concrete parts that predict the observed behavior.
  5. Change one control at a time. Add realistic recipes or variants and state their consequence.
  6. Show the boundary. Describe limitations and failures through observable symptoms rather than quality regressed or may not work.
  7. Go deeper only on demand. Move into architecture, API details, performance, diagnostics, and contribution after successful use is clear.

Stop when the primary reader can make the next decision. Link the secondary path instead of completing it inline.

Before every command or code excerpt, state the question it answers. After it, show or summarize the expected result and explain why it matters. Do not make a reader reverse-engineer a tutorial from a command inventory.

When tradeoffs matter, use consistent language such as reference, validated default, and aggressive/experimental. State what may change, the measured benefit, the test boundary, and how to restore the reference path. Introduce a second fixture only to test whether the lesson generalizes.

Use visuals only when they teach

Use an authentic screenshot, compact diagram, measured comparison, or short annotated output when it lets the reader understand or verify something faster. Keep the first success available as text and commands. Never use generated pixels as evidence or let a decorative image displace the definition and quickstart.

Review in three passes

Run the three passes in swyx-writing. During the developmental pass, also confirm one primary reader path, an explicit project definition, progressive order, and a clear cut line between README material and linked detail. During the explanatory pass, check prerequisites, expected results, commands, and whether one example carries the mechanism. Justify a front-door README that grows beyond roughly 1,500 words.

Then run a context-isolated cold-reader review for every substantial README. Give a fresh subagent only the approved reader beliefs and the rendered or source README—not the repository, task thread, intended answers, suspected problems, or this diagnosis. Ask it to:

  • identify the project, intended reader, status, and first useful outcome;
  • predict what the first command will do;
  • explain the central mechanism in plain language;
  • list undefined terms, missing prerequisites, causal gaps, and promises it could not verify;
  • name the point where it would stop reading or become lost.

Revise until the cold reader's account matches the intended reader contract. Do not coach the reviewer toward the desired answer.

Verify the repository handoff

  • Run the documented first-success path and representative tests when safe and reasonably cheap.
  • Check commands, expected output, links, anchors, code wrapping, narrow-screen rendering, and copied snippets.
  • Keep installation, usage, reference behavior, and contribution commands consistent with the code at the inspected revision.
  • Report which paths were executed, which were read from existing evidence, and which remain unverified.
  • Edit only repository documentation and supporting assets unless the user also asked to change product behavior.

© swyxio, 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 ai-readme of swyxio/skills.

  • SKILL.md
  • agents/openai.yaml

Open the folder on GitHubat commit 038ef34

Compare with similar skills

AI Readme 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.

AI Readme compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
AI Readme this skillswyxio/skills176—~2.7kAutomated safety check: PassMIT
Diagram Designcathrynlavery/diagram-design49k1 repos~7.6kAutomated safety check: PassMIT
Simple Englishmoeru-ai/airi50k2 repos~4.6kAutomated safety check: PassMIT
Doc SyncJetBrains/ideavim10k2 repos~2.6kAutomated safety check: PassMIT
Mailspring App ScreenshotsFoundry376/Mailspring18k—~1.5kAutomated safety check: PassGPL-3.0
Draw.io Diagram StudioAgents365-ai/drawio-skill10k—~2.4kAutomated safety check: NotesMIT

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Categories

Questions about AI Readme

What does AI Readme do?

Create or substantially revise repository README files that help a specific reader understand a technical project, reach a verified first result, and progress into realistic use, evaluation…. AI Readme is an agent skill from swyxio/skills. Create or substantially revise repository README files that help a specific reader understand a technical project, reach a verified first result, and progress into realistic use, evaluation, debugging, or contribution.

When should I use AI Readme?

AI Readme fits situations like: project landing READMEs; CLI and library quickstarts; research repositories; executable engineering notebooks.

How do I install AI Readme in Claude Code?

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

How do I install AI Readme in Codex?

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

Can I use AI Readme 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 swyxio/skills --skill ai-readme -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/ai-readme, .gemini/skills/ai-readme, .github/skills/ai-readme and .opencode/skills/ai-readme in your project.

What does AI Readme need to run?

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

Does AI Readme 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 AI Readme 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 AI Readme use?

AI Readme 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 AI Readme use?

About 2.7k tokens (SKILL.md is roughly 11k 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 AI Readme?

Skills that share tags, products or a category with AI Readme: Diagram Design (cathrynlavery/diagram-design, 49k stars), Simple English (moeru-ai/airi, 50k stars), Doc Sync (JetBrains/ideavim, 10k stars) and Mailspring App Screenshots (Foundry376/Mailspring, 18k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains AI Readme?

swyxio (a GitHub user) maintains it in swyxio/skills, which has 176 GitHub stars. The repository holds 89 skills in this directory. The repository was last updated on October 5, 2026.

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