Communitygithub.com

opensource-pipeline

Open-source pipeline: fork, sanitize, and package private projects for safe public release. Chains 3 agents (forker, sanitizer, packager). Triggers:

opensource-pipeline란 무엇인가요?

opensource-pipeline is a Claude Code agent skill that open-source pipeline: fork, sanitize, and package private projects for safe public release. Chains 3 agents (forker, sanitizer, packager). Triggers:.

지원 대상Claude Code~Codex CLI~Cursor
npx skills add https://github.com/affaan-m/everything-claude-code/tree/main/skills/opensource-pipeline

즐겨 사용하는 AI에게 물어보기

이 에이전트 스킬이 미리 로드된 새 채팅을 엽니다.

문서

Open-Source Pipeline Skill

Safely open-source any project through a 3-stage pipeline: Fork (strip secrets) → Sanitize (verify clean) → Package (CLAUDE.md + setup.sh + README).

When to Activate

  • User says "open source this project" or "make this public"
  • User wants to prepare a private repo for public release
  • User needs to strip secrets before pushing to GitHub
  • User invokes /opensource fork, /opensource verify, or /opensource package

Commands

CommandAction
/opensource fork PROJECTFull pipeline: fork + sanitize + package
/opensource verify PROJECTRun sanitizer on existing repo
/opensource package PROJECTGenerate CLAUDE.md + setup.sh + README
/opensource listShow all staged projects
/opensource status PROJECTShow reports for a staged project

Protocol

/opensource fork PROJECT

Full pipeline — the main workflow.

Step 1: Gather Parameters

Resolve the project path. If PROJECT contains /, treat as a path (absolute or relative). Otherwise check: current working directory, $HOME/PROJECT, then ask the user.

SOURCE_PATH="<resolved absolute path>"
STAGING_PATH="$HOME/opensource-staging/${PROJECT_NAME}"

Ask the user:

  1. "Which project?" (if not found)
  2. "License? (MIT / Apache-2.0 / GPL-3.0 / BSD-3-Clause)"
  3. "GitHub org or username?" (default: detect via gh api user -q .login)
  4. "GitHub repo name?" (default: project name)
  5. "Description for README?" (analyze project for suggestion)

Step 2: Create Staging Directory

mkdir -p $HOME/opensource-staging/

Step 3: Run Forker Agent

Spawn the opensource-forker agent:

Agent(
  description="Fork {PROJECT} for open-source",
  subagent_type="opensource-forker",
  prompt="""
Fork project for open-source release.

Source: {SOURCE_PATH}
Target: {STAGING_PATH}
License: {chosen_license}

Follow the full forking protocol:
1. Copy files (exclude .git, node_modules, __pycache__, .venv)
2. Strip all secrets and credentials
3. Replace internal references with placeholders
4. Generate .env.example
5. Clean git history
6. Generate FORK_REPORT.md in {STAGING_PATH}/FORK_REPORT.md
"""
)

Wait for completion. Read {STAGING_PATH}/FORK_REPORT.md.

Step 4: Run Sanitizer Agent

Spawn the opensource-sanitizer agent:

Agent(
  description="Verify {PROJECT} sanitization",
  subagent_type="opensource-sanitizer",
  prompt="""
Verify sanitization of open-source fork.

Project: {STAGING_PATH}
Source (for reference): {SOURCE_PATH}

Run ALL scan categories:
1. Secrets scan (CRITICAL)
2. PII scan (CRITICAL)
3. Internal references scan (CRITICAL)
4. Dangerous files check (CRITICAL)
5. Configuration completeness (WARNING)
6. Git history audit

Generate SANITIZATION_REPORT.md inside {STAGING_PATH}/ with PASS/FAIL verdict.
"""
)

Wait for completion. Read {STAGING_PATH}/SANITIZATION_REPORT.md.

If FAIL: Show findings to user. Ask: "Fix these and re-scan, or abort?"

  • If fix: Apply fixes, re-run sanitizer (maximum 3 retry attempts — after 3 FAILs, present all findings and ask user to fix manually)
  • If abort: Clean up staging directory

If PASS or PASS WITH WARNINGS: Continue to Step 5.

Step 5: Run Packager Agent

Spawn the opensource-packager agent:

Agent(
  description="Package {PROJECT} for open-source",
  subagent_type="opensource-packager",
  prompt="""
Generate open-source packaging for project.

Project: {STAGING_PATH}
License: {chosen_license}
Project name: {PROJECT_NAME}
Description: {description}
GitHub repo: {github_repo}

Generate:
1. CLAUDE.md (commands, architecture, key files)
2. setup.sh (one-command bootstrap, make executable)
3. README.md (or enhance existing)
4. LICENSE
5. CONTRIBUTING.md
6. .github/ISSUE_TEMPLATE/ (bug_report.md, feature_request.md)
"""
)

Step 6: Final Review

Present to user:

Open-Source Fork Ready: {PROJECT_NAME}

Location: {STAGING_PATH}
License: {license}
Files generated:
  - CLAUDE.md
  - setup.sh (executable)
  - README.md
  - LICENSE
  - CONTRIBUTING.md
  - .env.example ({N} variables)

Sanitization: {sanitization_verdict}

Next steps:
  1. Review: cd {STAGING_PATH}
  2. Create repo: gh repo create {github_org}/{github_repo} --public
  3. Push: git remote add origin ... && git push -u origin main

Proceed with GitHub creation? (yes/no/review first)

Step 7: GitHub Publish (on user approval)

cd "{STAGING_PATH}"
gh repo create "{github_org}/{github_repo}" --public --source=. --push --description "{description}"

/opensource verify PROJECT

Run sanitizer independently. Resolve path: if PROJECT contains /, treat as a path. Otherwise check $HOME/opensource-staging/PROJECT, then $HOME/PROJECT, then current directory.

Agent(
  subagent_type="opensource-sanitizer",
  prompt="Verify sanitization of: {resolved_path}. Run all 6 scan categories and generate SANITIZATION_REPORT.md."
)

/opensource package PROJECT

Run packager independently. Ask for "License?" and "Description?", then:

Agent(
  subagent_type="opensource-packager",
  prompt="Package: {resolved_path} ..."
)

/opensource list

ls -d $HOME/opensource-staging/*/

Show each project with pipeline progress (FORK_REPORT.md, SANITIZATION_REPORT.md, CLAUDE.md presence).


/opensource status PROJECT

cat $HOME/opensource-staging/${PROJECT}/SANITIZATION_REPORT.md
cat $HOME/opensource-staging/${PROJECT}/FORK_REPORT.md

Staging Layout

$HOME/opensource-staging/
  my-project/
    FORK_REPORT.md           # From forker agent
    SANITIZATION_REPORT.md   # From sanitizer agent
    CLAUDE.md                # From packager agent
    setup.sh                 # From packager agent
    README.md                # From packager agent
    .env.example             # From forker agent
    ...                      # Sanitized project files

Anti-Patterns

  • Never push to GitHub without user approval
  • Never skip the sanitizer — it is the safety gate
  • Never proceed after a sanitizer FAIL without fixing all critical findings
  • Never leave .env, *.pem, or credentials.json in the staging directory

Best Practices

  • Always run the full pipeline (fork → sanitize → package) for new releases
  • The staging directory persists until explicitly cleaned up — use it for review
  • Re-run the sanitizer after any manual fixes before publishing
  • Parameterize secrets rather than deleting them — preserve project functionality

Related Skills

See security-review for secret detection patterns used by the sanitizer.

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

accessibility

Design, implement, and audit inclusive digital products using WCAG 2.2 Level AA. Use when building or auditing UI that must meet WCAG 2.2 Level AA, or when reviewing a change for keyboard, contrast, or screen-reader support.

affaan-m/claude-api

Anthropic Claude API patterns for Python and TypeScript. Covers Messages API, streaming, tool use, vision, extended thinking, batches, prompt caching, and Claude Agent SDK. Use when building applications with the Claude API or Anthropic SDKs.

affaan-m/everything-claude-code

Development conventions and patterns for everything-claude-code. JavaScript project with conventional commits.

affaan-m/everything-claude-code

End-to-end marketing campaign planning and execution. Covers audience research, positioning, campaign angle definition, landing page copy, email sequences, social posts, ad copy, short-form video scripts, and content calendars. Use as the orchestration layer for multi-channel product launches. Use when planning or executing a multi-channel product launch, or producing landing page, email, social, or ad copy.

affaan-m/everything-claude-code-conventions

Development conventions and patterns for everything-claude-code. JavaScript project with conventional commits.

affaan-m/frontend-design

Create distinctive, production-grade frontend interfaces with high design quality. Use when the user asks to build web components, pages, or applications and the visual direction matters as much as the code quality.

affaan-m/gget

gget CLI and Python workflow for quick genomic database queries, sequence lookup, BLAST-style searches, enrichment checks, and reproducible bioinformatics evidence logs.

affaan-m/literature-review

Systematic literature-review workflow for academic, biomedical, technical, and scientific topics, including search planning, source screening, synthesis, citation checks, and evidence logging.

affaan-m/motion-ui

Production-ready UI motion system for React/Next.js. Use when implementing animations, transitions, or motion patterns.

affaan-m/project-guidelines-example

Example project-specific skill template based on a real production application.

affaan-m/pubmed-database

Direct PubMed and NCBI E-utilities search workflows for biomedical literature, MeSH queries, PMID lookup, citation retrieval, and API-backed literature monitoring.

affaan-m/scholar-evaluation

Structured scholarly-work evaluation for papers, proposals, literature reviews, methods sections, evidence quality, citation support, and research-writing feedback.

affaan-m/uspto-database

USPTO patent and trademark data workflow for official record lookup, PatentSearch queries, TSDR checks, assignment data, and reproducible IP research logs.

agent-architecture-audit

Full-stack diagnostic for agent and LLM applications. Audits the 12-layer agent stack for wrapper regression, memory pollution, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings with code-first fixes. Essential for developers building agent applications, autonomous loops, or any LLM-powered feature. Use when an agent or LLM feature misbehaves and the failing layer is unknown, or before shipping an agent stack.

agent-eval

Head-to-head comparison of coding agents (Claude Code, Aider, Codex, etc.) on custom tasks with pass rate, cost, time, and consistency metrics. Use when choosing between coding agents, or when a change to an agent setup needs measured pass rate, cost, and time rather than an impression.

agent-harness-construction

Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates. Use when defining or revising an agent

agentic-engineering

Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing. Use when planning or executing engineering work that agents will carry out end to end.

agentic-os

Build persistent multi-agent operating systems on Claude Code. Covers kernel architecture, specialist agents, slash commands, file-based memory, scheduled automation, and state management without external databases. Use when building a persistent multi-agent system on Claude Code with its own memory, commands, and scheduling.

agent-introspection-debugging

Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports. Use when an agent run fails and you need a reproducible diagnosis instead of a retry.

agent-payment-x402

Add x402 payment execution to AI agents with per-task budgets, spending controls, and non-custodial wallets. Supports Base through agentwallet-sdk and X Layer through OKX Payments / OKX Agent Payments Protocol. Use when an agent must pay for something itself and needs per-task budgets, spending controls, and a non-custodial wallet.

관련 스킬