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Oss Ready

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luongnv89
oss-ready

Transform a project into a professional open-source repository by adding LICENSE, README, CONTRIBUTING, CODE_OF_CONDUCT, SECURITY, and GitHub issue/PR templates. Don't use for documentation overhauls, landing-page generation, or registry publishing.

Overview

Publisherluongnv89
Repositoryskills
Skill nameoss-ready
Stars
124
Forks
18
Bundled files
3
LicenseMIT
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 luongnv89 on GitHub. Read the source before you install it.

Installation

Install the Oss Ready 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.
  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/luongnv89/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/oss-ready .claude/skills/oss-ready
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Oss Ready 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 Oss Ready 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 Oss Ready 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.

OSS Ready

Transform a project into a professional open-source repository with standard community files and GitHub templates.

Repo Sync Before Edits (mandatory)

Before creating/updating/deleting files in an existing repository, sync the current branch with remote:

bash
branch="$(git rev-parse --abbrev-ref HEAD)"
git fetch origin
git pull --rebase origin "$branch"

If the working tree is not clean, stash first, sync, then restore:

bash
git stash push -u -m "pre-sync"
branch="$(git rev-parse --abbrev-ref HEAD)"
git fetch origin && git pull --rebase origin "$branch"
git stash pop

If origin is missing, pull is unavailable, or rebase/stash conflicts occur, stop and ask the user before continuing.

Workflow

Before proceeding, check Edge Cases below for a non-MIT LICENSE, a monorepo, no detectable language, a private/internal repo, or a dirty/detached HEAD — handle the matching case first if it applies.

0. Create Feature Branch

Before making any changes:

  1. Check the current branch - if already on a feature branch for this task, skip
  2. Check the repo for branch naming conventions (e.g., feat/, feature/, etc.)
  3. Create and switch to a new branch following the repo's convention, or fallback to: feat/oss-ready

1. Analyze Project

Identify:

  • Primary language(s) and tech stack
  • Project purpose and functionality
  • Existing documentation to preserve
  • Package manager (npm, pip, cargo, etc.)

2. Create/Update Core Files

README.md - Enhance with:

  • Project overview and motivation
  • Key features list
  • Quick start (< 5 min setup)
  • Prerequisites and installation
  • Usage examples with code
  • Project structure
  • Technology stack
  • Contributing link
  • License badge

CONTRIBUTING.md - Include:

  • How to contribute overview
  • Development setup
  • Branching strategy (feature branches from main)
  • Commit conventions (Conventional Commits)
  • PR process and review expectations
  • Coding standards
  • Testing requirements

LICENSE - Default to MIT unless specified. Copy from assets/LICENSE-MIT.

CODE_OF_CONDUCT.md - Use Contributor Covenant. Copy from assets/CODE_OF_CONDUCT.md.

SECURITY.md - Vulnerability reporting process. Copy from assets/SECURITY.md.

3. Create GitHub Templates

Copy from assets/.github/:

  • ISSUE_TEMPLATE/bug_report.md
  • ISSUE_TEMPLATE/feature_request.md
  • PULL_REQUEST_TEMPLATE.md

4. Create Documentation Structure

docs/
├── ARCHITECTURE.md    # System design, components
├── DEVELOPMENT.md     # Dev setup, debugging
├── DEPLOYMENT.md      # Production deployment
└── CHANGELOG.md       # Version history

5. Update Project Metadata

Update package file based on tech stack:

  • Node.js: package.json - name, description, keywords, repository, license
  • Python: pyproject.toml or setup.py
  • Rust: Cargo.toml
  • Go: go.mod + README badges

6. Ensure .gitignore

Verify comprehensive patterns for the tech stack.

7. Present Checklist

After completion, show:

  • Files created/updated
  • Items needing manual review
  • Recommendations for next steps

Step Completion Reports

After completing each major step, output a status report in this format:

◆ [Step Name] ([step N of M] — [context])
··································································
  [Check 1]:          √ pass
  [Check 2]:          √ pass (note if relevant)
  [Check 3]:          × fail — [reason]
  [Check 4]:          √ pass
  [Criteria]:         √ N/M met
  ____________________________
  Result:             PASS | FAIL | PARTIAL

Adapt the check names to match what the step actually validates. Use for pass, × for fail, and to add brief context. The "Criteria" line summarizes how many acceptance criteria were met. The "Result" line gives the overall verdict.

Analysis (step 1 of 7)

◆ Analysis (step 1 of 7 — project profiling)
··································································
  Language detected:       √ pass — TypeScript (primary)
  Project type identified: √ pass — CLI tool
  Existing docs found:     √ pass — README.md (partial), no LICENSE
  [Criteria]:              √ 3/3 met
  ____________________________
  Result:                  PASS

Repeat this format for each subsequent step — Core Files, GitHub Templates, Documentation, Project Metadata, .gitignore, Checklist — adapting the check names to what that step actually validates. Steps 5 and 6 carry acceptance criteria of their own, so a run that reports complete without them has skipped verifiable work. Step 0 sets up the branch and gets no report.

Guidelines

  • Adapt to project's actual tech stack
  • Include working examples from the actual codebase

Acceptance Criteria

The skill is complete when every item below can be verified with test -f, grep, or a quick visual check. Treat this as a checklist the agent must assert before reporting success.

  • LICENSE exists at repo root and contains a valid SPDX identifier (e.g., MIT, Apache-2.0). Verify: grep -E "MIT License|Apache License" LICENSE.
  • README.md exists, is at least 40 lines, and includes sections for Installation, Usage, and License. Verify: grep -iE "^#+ (install|usage|license)" README.md | wc -l returns >= 3.
  • CONTRIBUTING.md exists and references the issue tracker plus a branching/PR workflow. Verify: grep -iE "issue|pull request|branch" CONTRIBUTING.md.
  • CODE_OF_CONDUCT.md exists and mentions the Contributor Covenant. Verify: grep -i "contributor covenant" CODE_OF_CONDUCT.md.
  • SECURITY.md exists and lists at least one vulnerability-reporting contact (email or form URL). Verify: grep -E "@|https?://" SECURITY.md.
  • .github/ISSUE_TEMPLATE/bug_report.md and .github/ISSUE_TEMPLATE/feature_request.md both exist with YAML frontmatter (name:, about:).
  • .github/PULL_REQUEST_TEMPLATE.md exists and contains a checklist (- [ ]).
  • .gitignore exists and excludes the language-appropriate build/temp artefacts (e.g., node_modules/, dist/, __pycache__/, target/).
  • Project metadata file (package.json, pyproject.toml, Cargo.toml, or go.mod) declares license, description, and repository fields where the format supports them.
  • No previously committed files were deleted; only additions and non-destructive enhancements were made.

Expected Output

After a successful run on a TypeScript CLI project that started with only a partial README.md, the agent emits a final report shaped like this:

◆ OSS Ready summary (7 of 7 steps complete)
··································································
  Files created:
    √ LICENSE                                  (MIT)
    √ CONTRIBUTING.md                          (33 lines)
    √ CODE_OF_CONDUCT.md                       (Contributor Covenant 2.1)
    √ SECURITY.md                              (reporting via security@example.com)
    √ .github/ISSUE_TEMPLATE/bug_report.md
    √ .github/ISSUE_TEMPLATE/feature_request.md
    √ .github/PULL_REQUEST_TEMPLATE.md
    √ docs/ARCHITECTURE.md, DEVELOPMENT.md, DEPLOYMENT.md, CHANGELOG.md
  Files updated:
    √ README.md                                (+ Quick Start, Usage, License badge)
    √ package.json                             (license, repository, keywords)
    √ .gitignore                               (added dist/, .env)
  Acceptance criteria:    √ 10/10 met
  Manual review needed:
    - Confirm SECURITY.md contact email is monitored
    - Add real maintainer names to CODE_OF_CONDUCT enforcement section
  ____________________________
  Result:                 PASS

The agent must list the manual-review items explicitly so the user can finish what cannot be automated. Assert that the file tree printed by the agent matches what is actually on disk before declaring PASS.

Edge Cases

The skill should detect and handle these inputs explicitly rather than fail silently:

  • Existing LICENSE with a non-MIT identifier — never overwrite. Read it, log the detected license, and skip the LICENSE step.
  • Monorepo with multiple package.json files — update only the root metadata file unless the user names a sub-package.
  • Repo with no detectable language (empty repo or only docs) — pause and ask the user which template stack to assume.
  • Existing CODE_OF_CONDUCT.md or SECURITY.md — diff against the template; only append a missing section, never replace user content.
  • Private/internal projects — confirm with the user before adding public-facing files like SECURITY.md or community templates.
  • Pre-existing .github/ workflows or templates — preserve them; merge only the missing files.
  • Non-English README — keep the existing language; do not translate, only add structurally missing sections in the same language.
  • Detached HEAD or dirty working tree — abort with the sync warning above; never force changes onto an unstable branch.

Assets

Templates in assets/:

  • LICENSE-MIT - MIT license template
  • CODE_OF_CONDUCT.md - Contributor Covenant
  • SECURITY.md - Security policy template
  • .github/ISSUE_TEMPLATE/bug_report.md
  • .github/ISSUE_TEMPLATE/feature_request.md
  • .github/PULL_REQUEST_TEMPLATE.md

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 Oss Ready AI skill do?

Transform a project into a professional open-source repository by adding LICENSE, README, CONTRIBUTING, CODE_OF_CONDUCT, SECURITY, and GitHub issue/PR templates. Don't use for documentation overhauls, landing-page generation, or registry publishing.

Why use Oss Ready on TypingMind?

Because you install it once and use it with any model. Oss Ready 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 Oss Ready in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/luongnv89/skills/tree/main/skills/oss-ready. 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 Oss Ready?

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 Oss Ready?

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

Is the Oss Ready AI skill free?

Yes. It is published on GitHub by luongnv89 under the MIT 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.

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